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<title>Data Validation</title>
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<title>Microsoft Purview and Datagaps: Why Data Trust Needs Both Governance and Execution</title>
<link>https://www.datagaps.com/blog/microsoft-purview-and-datagaps-why-data-trust-needs-both-governance-and-execution/</link>
<dc:creator><![CDATA[Raj Mohan Achanta]]></dc:creator>
<pubDate>Mon, 26 Oct 2026 08:53:19 +0000</pubDate>
<category><![CDATA[Data Quality]]></category>
<category><![CDATA[Data Validation]]></category>
<category><![CDATA[DataOps]]></category>
<category><![CDATA[AI Rule Generation]]></category>
<category><![CDATA[dataops]]></category>
<category><![CDATA[Microsoft Fabric]]></category>
<guid isPermaLink="false">https://www.datagaps.com/?p=63760</guid>
<description><![CDATA[<p>Generate data quality rules for Microsoft Fabric using AI. Learn how AI simplifies rule creation, improves data accuracy, and strengthens data governance.</p>
<p>The post <a href="https://www.datagaps.com/blog/microsoft-purview-and-datagaps-why-data-trust-needs-both-governance-and-execution/">Microsoft Purview and Datagaps: Why Data Trust Needs Both Governance and Execution</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></description>
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<p><span class="TextRun SCXW24282872 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW24282872 BCX8">Enterprise data trust is a measurable state in which organizations can verify that their data is </span><span class="NormalTextRun SCXW24282872 BCX8">accurate</span><span class="NormalTextRun SCXW24282872 BCX8">, governed, and continuously </span><span class="NormalTextRun SCXW24282872 BCX8">validated</span><span class="NormalTextRun SCXW24282872 BCX8"> across every stage of the pipeline. Achieving it requires two complementary layers: a metadata governance layer (such as Microsoft Purview) for visibility and policy, and an operational execution layer (such as </span><span class="NormalTextRun SpellingErrorV2Themed SCXW24282872 BCX8">Datagaps</span> <span class="NormalTextRun SpellingErrorV2Themed SCXW24282872 BCX8">DataOps</span><span class="NormalTextRun SCXW24282872 BCX8"> Suite) for automated testing, quality scoring, and validation. Neither layer alone is sufficient.</span></span><span class="EOP Selected SCXW24282872 BCX8" data-ccp-props="{"201341983":0,"335557856":16512491,"335559685":360,"335559737":360,"335559738":120,"335559739":120,"335559740":312,"335572083":12,"335572084":0,"335572085":12682798,"469789810":"single"}"> </span></p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li><span data-contrast="none">Metadata governance shows what your data is and where it lives. Operational execution proves whether that data is actually correct today.</span><span data-ccp-props="{"201341983":0,"335559685":720,"335559738":60,"335559739":60,"335559740":312}"> </span></li><li><span data-contrast="none">Microsoft Purview handles cataloging, classification, lineage, and compliance policies across your data estate.</span></li><li><span data-contrast="none">Datagaps DataOps Suite handles automated ETL validation, transformation testing, data quality scoring, schema drift detection, and continuous monitoring.</span></li><li><span data-contrast="none">Combining both layers creates a complete trust architecture: Purview governs, Datagaps validates.</span></li><li><span data-contrast="none">According to Gartner (2025), 60% of data management tasks will be automated by 2027, making this dual-layer approach essential for enterprises scaling AI and analytics.</span></li></ul> </div>
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<h2 class="elementor-heading-title elementor-size-default">Why Does Data Governance Alone Not Build Trust? </h2> </div>
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<p><span class="NormalTextRun SCXW218133633 BCX8">Enterprises today manage hundreds of data and AI products for both internal operations and external clients. From industry observations over the past three years, projects in their </span><span class="NormalTextRun SCXW218133633 BCX8">initial</span><span class="NormalTextRun SCXW218133633 BCX8"> stages rarely have a governance layer. In many cases, even mature projects lack one entirely.</span></p> </div>
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<p><span data-contrast="none">Even when governance programs exist, they focus almost entirely on metadata. Assets get cataloged. Sensitive columns are classified. Business glossaries are created. Lineage diagrams begin to show how data moves across pipelines. These are all critical investments.</span><span data-ccp-props="{"201341983":0,"335559738":80,"335559739":120,"335559740":312}"> </span></p> </div>
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<p>But here is the core problem: governance gives you visibility, not proof.</p> </div>
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<img fetchpriority="high" decoding="async" width="1120" height="429" src="https://www.datagaps.com/wp-content/uploads/Purview-Data-Health-Controls-Table-.png" class="attachment-full size-full wp-image-64013" alt="Alt: Microsoft Purview data health controls showing governance metrics for access, discoverability, estate curation, value creation, and trusted data" srcset="https://www.datagaps.com/wp-content/uploads/Purview-Data-Health-Controls-Table-.png 1120w, https://www.datagaps.com/wp-content/uploads/Purview-Data-Health-Controls-Table--300x115.png 300w, https://www.datagaps.com/wp-content/uploads/Purview-Data-Health-Controls-Table--1024x392.png 1024w, https://www.datagaps.com/wp-content/uploads/Purview-Data-Health-Controls-Table--768x294.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> <figcaption class="widget-image-caption wp-caption-text">Microsoft Purview's Data Health Controls provide visibility into governance maturity across six control groupings. </figcaption>
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<p><span class="TextRun SCXW50379313 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW50379313 BCX8">You can </span><span class="NormalTextRun SpellingErrorV2Themed SCXW50379313 BCX8">catalog</span><span class="NormalTextRun SCXW50379313 BCX8"> every table, classify every column, and trace every lineage path. Yet a random unexpected value can still break your pipeline on any given day. Enterprises simply cannot afford that kind of risk.</span></span><span class="EOP Selected SCXW50379313 BCX8" data-ccp-props="{"201341983":0,"335559738":80,"335559739":120,"335559740":312}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">What Is the Difference Between Metadata and Operational Governance?</h2> </div>
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<p><span class="TextRun SCXW222770681 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW222770681 BCX8">There is a well-known principle in the data world: metadata tells the story, but data tells the truth.</span></span><span class="EOP Selected SCXW222770681 BCX8" data-ccp-props="{"201341983":0,"335559738":80,"335559739":120,"335559740":312}"> </span></p> </div>
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<p><span class="TextRun SCXW51321890 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW51321890 BCX8"><b>Metadata governance</b> </span></span><span class="TextRun SCXW51321890 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW51321890 BCX8">answers</span><span class="NormalTextRun SCXW51321890 BCX8"> identity and context questions. Where did this dataset originate? Who owns it? Does this table </span><span class="NormalTextRun SCXW51321890 BCX8">contain</span><span class="NormalTextRun SCXW51321890 BCX8"> PII? What is the lineage of this report? Which glossary term maps to this column? Which compliance policy applies?</span></span><span class="EOP Selected SCXW51321890 BCX8" data-ccp-props="{"201341983":0,"335559738":80,"335559739":120,"335559740":312}"> </span></p> </div>
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<p><strong><span class="TextRun SCXW31187652 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW31187652 BCX8">Operational governance </span></span></strong><span class="TextRun SCXW31187652 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW31187652 BCX8">answers health and execution questions. Did today’s pipeline load successfully? Did all expected records arrive? Are there unexpected nulls or duplicates? Did any transformation corrupt the data? Are business rules still passing? Can Finance trust today’s dashboard?</span></span><span class="EOP Selected SCXW31187652 BCX8" data-ccp-props="{"201341983":0,"335559738":80,"335559739":120,"335559740":312}"> </span></p> </div>
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<p><span class="TextRun SCXW109362073 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW109362073 BCX8">The distinction matters because you can have perfect metadata governance and still have broken data reaching your dashboards. Governance without execution is visibility without verification.</span></span></p> </div>
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<img decoding="async" width="1168" height="1390" src="https://www.datagaps.com/wp-content/uploads/Metadata-vs-Operational-Governance-Comparison-Diagram-.png" class="attachment-full size-full wp-image-64014" alt="Comparison diagram showing metadata governance questions on the left mapped to operational governance questions on the right" srcset="https://www.datagaps.com/wp-content/uploads/Metadata-vs-Operational-Governance-Comparison-Diagram-.png 1168w, https://www.datagaps.com/wp-content/uploads/Metadata-vs-Operational-Governance-Comparison-Diagram--252x300.png 252w, https://www.datagaps.com/wp-content/uploads/Metadata-vs-Operational-Governance-Comparison-Diagram--860x1024.png 860w, https://www.datagaps.com/wp-content/uploads/Metadata-vs-Operational-Governance-Comparison-Diagram--768x914.png 768w" sizes="(max-width: 1168px) 100vw, 1168px" /> <figcaption class="widget-image-caption wp-caption-text">Metadata governance provides the identity layer. Operational governance provides the health and execution layer. Both are required for data trust.</figcaption>
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<h2 class="elementor-heading-title elementor-size-default">How Does Microsoft Purview Handle the Governance Layer? </h2> </div>
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<p><span class="TextRun SCXW88928104 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW88928104 BCX8">Microsoft Purview provides unified data governance across the entire data estate. It acts as the metadata and policy backbone for enterprises running Microsoft Fabric, Azure Synapse, SQL Server, Power BI, and hybrid cloud environments.</span></span><span class="EOP Selected SCXW88928104 BCX8" data-ccp-props="{"201341983":0,"335559738":80,"335559739":120,"335559740":312}"> </span></p> </div>
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<p><span class="NormalTextRun SCXW172484454 BCX8">Purview’s governance capabilities include a Data </span><span class="NormalTextRun SpellingErrorV2Themed SCXW172484454 BCX8">Catalog</span><span class="NormalTextRun SCXW172484454 BCX8"> with business glossary and asset discovery, a Data Map for lineage and relationship tracking, Data Classification with sensitivity labels and PII detection, Access Governance for permissions and policy enforcement, Compliance and Audit for regulatory reporting, and Data Estate Insights for governance maturity analytics.</span></p> </div>
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<p><span class="TextRun SCXW165174799 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW165174799 BCX8">Recently, Purview introduced its new portal with advanced capabilities around governance, compliance, and data security. These updates make it a stronger governance backbone than ever, particularly for organizations invested in the Microsoft ecosystem.</span></span></p> </div>
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<p><span data-contrast="none">However, Purview’s scope is metadata and policy. It tells you what data exists, where it came from, and who should have access. What it does not do is validate whether that data is correct at the record level, test transformations, or catch quality regressions before they reach production dashboards.</span><span data-ccp-props="{"201341983":0,"335559738":80,"335559739":120,"335559740":312}"> </span></p><p><span data-contrast="none">That execution gap is where Datagaps steps in.</span><span data-ccp-props="{"201341983":0,"335559738":80,"335559739":120,"335559740":312}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">How Does Datagaps DataOps Suite Build the Execution Layer? </h2> </div>
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<p><span class="TextRun SCXW30407300 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SpellingErrorV2Themed SCXW30407300 BCX8">Datagaps</span> <span class="NormalTextRun SpellingErrorV2Themed SCXW30407300 BCX8">DataOps</span><span class="NormalTextRun SCXW30407300 BCX8"> Suite is an enterprise data testing and validation platform that automates quality assurance across the full data lifecycle. Listed in both Gartner’s Market Guide for </span><span class="NormalTextRun SpellingErrorV2Themed SCXW30407300 BCX8">DataOps</span><span class="NormalTextRun SCXW30407300 BCX8"> Tools (October 2025) and Market Guide for Data Observability Tools (February 2026), it provides the operational execution layer that governance platforms like Purview cannot deliver alone.</span></span><span class="EOP Selected SCXW30407300 BCX8" data-ccp-props="{"201341983":0,"335559738":80,"335559739":120,"335559740":312}"> </span></p> </div>
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<p><span class="TextRun SCXW41872744 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW41872744 BCX8">Where Purview governs metadata, </span><span class="NormalTextRun SpellingErrorV2Themed SCXW41872744 BCX8">Datagaps</span><span class="NormalTextRun SCXW41872744 BCX8"> validates data. Here is what the </span><span class="NormalTextRun SpellingErrorV2Themed SCXW41872744 BCX8">DataOps</span><span class="NormalTextRun SCXW41872744 BCX8"> Suite delivers:</span></span><span class="EOP Selected SCXW41872744 BCX8" data-ccp-props="{"201341983":0,"335559738":80,"335559739":120,"335559740":312}"> </span></p><p><strong><span class="TextRun SCXW79181437 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW79181437 BCX8">ETL Validation: </span></span></strong><span class="TextRun SCXW79181437 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW79181437 BCX8">Automated source-to-target data validation powered by AI. Validates 100% of records across 200+ data sources, not just a statistical sample. Catches row-level discrepancies, schema mismatches, and transformation errors before they propagate downstream.</span></span></p> </div>
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<p><span class="TextRun SCXW60542423 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW60542423 BCX8"><strong>Data Quality Scoring:</strong> </span></span><span class="TextRun SCXW60542423 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW60542423 BCX8">A continuously updated Data Quality Score that quantifies the health of your data estate at any point in time. Tracks completeness, consistency, validity, and timeliness across datasets.</span></span><span class="EOP Selected SCXW60542423 BCX8" data-ccp-props="{"201341983":0,"335559738":80,"335559739":120,"335559740":312}"> </span></p> </div>
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<p><strong><span class="TextRun SCXW234338522 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW234338522 BCX8">Transformation Testing: </span></span></strong><span class="TextRun SCXW234338522 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW234338522 BCX8">Validates</span><span class="NormalTextRun SCXW234338522 BCX8"> complex transformations across multiple columns and tables while moving data to dashboards or gold layers. This stage </span><span class="NormalTextRun SCXW234338522 BCX8">determines</span><span class="NormalTextRun SCXW234338522 BCX8"> what eventually becomes visible on reports or gets exposed through APIs. If anything goes wrong here, you end up with incorrect numbers.</span></span></p> </div>
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<p><strong><span class="TextRun SCXW245147437 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW245147437 BCX8">Schema Drift Detection: </span></span></strong><span class="TextRun SCXW245147437 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW245147437 BCX8">Tracks structural changes across your data sources, whether columns are added, removed, or renamed. Performs impact analysis showing affected tables and columns across data flows.</span></span></p> </div>
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<img decoding="async" width="1120" height="520" src="https://www.datagaps.com/wp-content/uploads/Datagaps-Schema-Drift-Detection-Dashboard-.png" class="attachment-full size-full wp-image-64015" alt="Datagaps DataOps Suite schema drift detection dashboard showing tables, columns tracked with detailed change history" srcset="https://www.datagaps.com/wp-content/uploads/Datagaps-Schema-Drift-Detection-Dashboard-.png 1120w, https://www.datagaps.com/wp-content/uploads/Datagaps-Schema-Drift-Detection-Dashboard--300x139.png 300w, https://www.datagaps.com/wp-content/uploads/Datagaps-Schema-Drift-Detection-Dashboard--1024x475.png 1024w, https://www.datagaps.com/wp-content/uploads/Datagaps-Schema-Drift-Detection-Dashboard--768x357.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> <figcaption class="widget-image-caption wp-caption-text">Datagaps DataOps Suite tracks schema drift across all connected data sources with full impact analysis for every structural change. </figcaption>
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<p><strong>Data Contracts:</strong> Reusable validation rules that standardize quality checks across similar datasets. Instead of recreating checks for every new pipeline, Data Contracts enforce consistency at scale.</p> </div>
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<p><strong><span class="TextRun SCXW213271826 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW213271826 BCX8">BI Validation: </span></span></strong><span class="TextRun SCXW213271826 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW213271826 BCX8">Automated testing of Power BI, Tableau, Oracle Analytics, and other BI platforms. Catches report errors and performance issues before business users see incorrect dashboards. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW213271826 BCX8">Datagaps</span><span class="NormalTextRun SCXW213271826 BCX8"> reduces BI validation time by 70% and accelerates turnaround by 80%.</span></span><span class="EOP Selected SCXW213271826 BCX8" data-ccp-props="{"201341983":0,"335559738":80,"335559739":120,"335559740":312}"> </span></p> </div>
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<p><strong><span class="TextRun SCXW186361231 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW186361231 BCX8">Synthetic Test Data: </span></span></strong><span class="TextRun SCXW186361231 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW186361231 BCX8">Using the </span><span class="NormalTextRun SpellingErrorV2Themed SCXW186361231 BCX8">Datagaps</span><span class="NormalTextRun SCXW186361231 BCX8"> Test Data Manager (TDM) module, teams can generate compliant synthetic test data to </span><span class="NormalTextRun SCXW186361231 BCX8">validate</span><span class="NormalTextRun SCXW186361231 BCX8"> transformations before moving anything into production.</span></span></p> </div>
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<img loading="lazy" decoding="async" width="997" height="853" src="https://www.datagaps.com/wp-content/uploads/Datagaps-Data-Quality-Dashboard.png" class="attachment-full size-full wp-image-64016" alt="Datagaps DataOps Suite data quality dashboard showing Data Quality Score, Data Models, Data Lineage, and Data Quality Issues" srcset="https://www.datagaps.com/wp-content/uploads/Datagaps-Data-Quality-Dashboard.png 997w, https://www.datagaps.com/wp-content/uploads/Datagaps-Data-Quality-Dashboard-300x257.png 300w, https://www.datagaps.com/wp-content/uploads/Datagaps-Data-Quality-Dashboard-768x657.png 768w" sizes="(max-width: 997px) 100vw, 997px" /> <figcaption class="widget-image-caption wp-caption-text">The Datagaps DataOps Suite Data Quality dashboard provides a single view of quality scores, data models, lineage, and active quality issues. </figcaption>
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<p><span class="TextRun SCXW188684399 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW188684399 BCX8">Instead of discovering data issues after a stakeholder reports incorrect values, </span><span class="NormalTextRun SpellingErrorV2Themed SCXW188684399 BCX8">Datagaps</span> <span class="NormalTextRun SpellingErrorV2Themed SCXW188684399 BCX8">DataOps</span><span class="NormalTextRun SCXW188684399 BCX8"> Suite continuously </span><span class="NormalTextRun SCXW188684399 BCX8">validates</span><span class="NormalTextRun SCXW188684399 BCX8"> data throughout its lifecycle and sends alerts before incorrect data reaches business users. That is the real </span><span class="NormalTextRun SCXW188684399 BCX8">objective</span><span class="NormalTextRun SCXW188684399 BCX8"> of operational governance.</span></span><span class="EOP Selected SCXW188684399 BCX8" data-ccp-props="{"201341983":0,"335559738":80,"335559739":120,"335559740":312}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">How Do Microsoft Purview and Datagaps Work Together? </h2> </div>
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<p><span class="TextRun SCXW237141887 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW237141887 BCX8">The strongest data architectures use both platforms for their native strengths. Purview handles the governance and metadata layer. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW237141887 BCX8">Datagaps</span><span class="NormalTextRun SCXW237141887 BCX8"> handles the testing and validation layer. Together, they cover the entire data governance and quality lifecycle.</span></span><span class="EOP Selected SCXW237141887 BCX8" data-ccp-props="{"201341983":0,"335559738":80,"335559739":120,"335559740":312}"> </span></p> </div>
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<img loading="lazy" decoding="async" width="986" height="842" src="https://www.datagaps.com/wp-content/uploads/Combined-Architecture-Microsoft-Purview-Datagaps-DataOps-Suite.png" class="attachment-full size-full wp-image-64018" alt="Architecture diagram combining Microsoft Purview for governance with Datagaps DataOps Suite for validation across the full data lifecycle" srcset="https://www.datagaps.com/wp-content/uploads/Combined-Architecture-Microsoft-Purview-Datagaps-DataOps-Suite.png 986w, https://www.datagaps.com/wp-content/uploads/Combined-Architecture-Microsoft-Purview-Datagaps-DataOps-Suite-300x256.png 300w, https://www.datagaps.com/wp-content/uploads/Combined-Architecture-Microsoft-Purview-Datagaps-DataOps-Suite-768x656.png 768w" sizes="(max-width: 986px) 100vw, 986px" /> <figcaption class="widget-image-caption wp-caption-text">Reference architecture combining Microsoft Purview for governance and Datagaps DataOps Suite for execution across the entire data lifecycle.</figcaption>
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<h3 class="elementor-heading-title elementor-size-default">At the Data Source Layer </h3> </div>
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<p><span class="TextRun SCXW224086464 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW224086464 BCX8">Purview tracks end-to-end lineage from source to report. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW224086464 BCX8">Datagaps</span> <span class="NormalTextRun SpellingErrorV2Themed SCXW224086464 BCX8">DataOps</span><span class="NormalTextRun SCXW224086464 BCX8"> Suite runs ETL Validation across every record, </span><span class="NormalTextRun SCXW224086464 BCX8">validating</span><span class="NormalTextRun SCXW224086464 BCX8"> transformations using AI and LLM integration capabilities.</span></span><span class="EOP Selected SCXW224086464 BCX8" data-ccp-props="{"201341983":0,"335559738":80,"335559739":120,"335559740":312}"> </span></p> </div>
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<p><span class="TextRun SCXW196805744 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW196805744 BCX8" data-ccp-parastyle="heading 3">At the Warehouse and Data Mart Layers</span></span><span class="EOP Selected SCXW196805744 BCX8" data-ccp-props="{"335559738":240,"335559739":120}"> </span></p> </div>
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<p><span class="TextRun SCXW84723454 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW84723454 BCX8">Purview applies business glossary terms, classifies sensitive data, and enforces access policies. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW84723454 BCX8">Datagaps</span> <span class="NormalTextRun SpellingErrorV2Themed SCXW84723454 BCX8">DataOps</span><span class="NormalTextRun SCXW84723454 BCX8"> Suite continues validation through Data Quality Monitoring and automated testing to ensure the data reaching production data marts is verified.</span></span><span class="EOP Selected SCXW84723454 BCX8" data-ccp-props="{"201341983":0,"335559738":80,"335559739":120,"335559740":312}"> </span></p> </div>
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<p><span class="TextRun SCXW66948887 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW66948887 BCX8" data-ccp-parastyle="heading 3">At the Reporting and Dashboards Layer</span></span><span class="EOP Selected SCXW66948887 BCX8" data-ccp-props="{"335559738":240,"335559739":120}"> </span></p> </div>
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<p><span class="TextRun SCXW133534118 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW133534118 BCX8">Purview provides compliance audit trails and data estate analytics. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW133534118 BCX8">Datagaps</span> <span class="NormalTextRun SpellingErrorV2Themed SCXW133534118 BCX8">DataOps</span><span class="NormalTextRun SCXW133534118 BCX8"> Suite runs BI Validation to verify that the final reports and dashboards reflect </span><span class="NormalTextRun SCXW133534118 BCX8">accurate</span><span class="NormalTextRun SCXW133534118 BCX8">, trustworthy data.</span></span><span class="EOP Selected SCXW133534118 BCX8" data-ccp-props="{"201341983":0,"335559738":80,"335559739":120,"335559740":312}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">What Additional Capabilities Does the DataOps Suite Offer? </h2> </div>
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<p><span class="TextRun SCXW112288288 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW112288288 BCX8">Beyond core data testing, </span><span class="NormalTextRun SpellingErrorV2Themed SCXW112288288 BCX8">Datagaps</span> <span class="NormalTextRun SpellingErrorV2Themed SCXW112288288 BCX8">DataOps</span><span class="NormalTextRun SCXW112288288 BCX8"> Suite includes several components that extend the execution layer of governance.</span></span><span class="EOP Selected SCXW112288288 BCX8" data-ccp-props="{"201341983":0,"335559738":80,"335559739":120,"335559740":312}"> </span></p> </div>
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<img loading="lazy" decoding="async" width="1120" height="753" src="https://www.datagaps.com/wp-content/uploads/Datagaps-DataOps-Suite-Full-Navigation-and-Modules-.png" class="attachment-full size-full wp-image-64019" alt="Datagaps DataOps Suite navigation showing Data Quality, Data Models, Dataflows, Pipelines, BI DevOps, TDM, and additional modules" srcset="https://www.datagaps.com/wp-content/uploads/Datagaps-DataOps-Suite-Full-Navigation-and-Modules-.png 1120w, https://www.datagaps.com/wp-content/uploads/Datagaps-DataOps-Suite-Full-Navigation-and-Modules--300x202.png 300w, https://www.datagaps.com/wp-content/uploads/Datagaps-DataOps-Suite-Full-Navigation-and-Modules--1024x688.png 1024w, https://www.datagaps.com/wp-content/uploads/Datagaps-DataOps-Suite-Full-Navigation-and-Modules--768x516.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> <figcaption class="widget-image-caption wp-caption-text">Datagaps DataOps Suite includes over 15 integrated modules covering data quality, DevOps, test data, scheduling, and monitoring. </figcaption>
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<p><span class="NormalTextRun SCXW159739676 BCX8">Data Models provide an abstract model that organizes data elements and standardizes how they relate to one another and to the properties of real-world entities. Data Lineage tracks data as it moves through systems and processes, helping users understand where data originated and how it has changed. Data Quality Issues actively </span><span class="NormalTextRun SCXW159739676 BCX8">monitors</span><span class="NormalTextRun SCXW159739676 BCX8"> and </span><span class="NormalTextRun SCXW159739676 BCX8">identifies</span><span class="NormalTextRun SCXW159739676 BCX8"> data points that are inaccurate, incomplete, or failing predefined quality standards.</span></p> </div>
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<p><span class="TextRun SCXW120187522 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW120187522 BCX8">Data Contracts define reusable validation rules for data files, ensuring all incoming data conforms to expected structure and quality standards. Common Data Models provide high-level organization of data </span><span class="NormalTextRun SCXW120187522 BCX8">across functional areas. And the Analysis module delivers structured reporting on data quality patterns, enabling teams to </span><span class="NormalTextRun SCXW120187522 BCX8">identify</span><span class="NormalTextRun SCXW120187522 BCX8"> systemic issues rather than reacting to individual incidents.</span></span><span class="EOP SCXW120187522 BCX8" data-ccp-props="{"201341983":0,"335559738":80,"335559739":120,"335559740":312}"> </span></p> </div>
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<img loading="lazy" decoding="async" width="592" height="712" src="https://www.datagaps.com/wp-content/uploads/Datagaps-Data-Quality-Detail-View.png" class="attachment-full size-full wp-image-64020" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Datagaps-Data-Quality-Detail-View.png 592w, https://www.datagaps.com/wp-content/uploads/Datagaps-Data-Quality-Detail-View-249x300.png 249w" sizes="(max-width: 592px) 100vw, 592px" /> <figcaption class="widget-image-caption wp-caption-text">Datagaps DataOps Suite provides Data Contracts, Common Data Models, and structured Analysis for repeatable governance execution.</figcaption>
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<h2 class="elementor-heading-title elementor-size-default">Where Is Enterprise Data Trust Headed? </h2> </div>
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<p><span class="TextRun SCXW181035878 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW181035878 BCX8">Microsoft Fabric enables fast analytics and AI experiences, but reliable outcomes depend on trustworthy data. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW181035878 BCX8">Datagaps</span><span class="NormalTextRun SCXW181035878 BCX8"> supports Fabric data quality workflows by helping teams generate rules faster, execute validations consistently, and interpret results with actionable detail. This reduces risk, improves reliability, and helps scale data quality practices across teams and environments.</span></span><span class="EOP Selected SCXW181035878 BCX8" data-ccp-props="{}"> </span></p> </div>
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<p><span class="TextRun SCXW220791632 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW220791632 BCX8">The future of enterprise data trust will be built on two complementary layers. The first layer focuses on visibility through tools like Microsoft Purview. The second focuses on continuous validation through platforms like </span><span class="NormalTextRun SpellingErrorV2Themed SCXW220791632 BCX8">Datagaps</span> <span class="NormalTextRun SpellingErrorV2Themed SCXW220791632 BCX8">DataOps</span><span class="NormalTextRun SCXW220791632 BCX8"> Suite.</span></span><span class="EOP Selected SCXW220791632 BCX8" data-ccp-props="{"201341983":0,"335559738":80,"335559739":120,"335559740":312}"> </span></p> </div>
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<p><span class="TextRun SCXW198799002 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW198799002 BCX8">According to Gartner (2025), the data observability market is growing at 20.8% annually, reaching $346.4 million. And 53% of data and AI leaders have already implemented data observability tools, with 43% more planning to do so within 18 months (Gartner, 2025). Organizations that invest in both governance and execution will build the data trust foundation required for reliable AI, accurate analytics, and confident business decisions.</span></span><span class="EOP Selected SCXW198799002 BCX8" data-ccp-props="{"201341983":0,"335559738":80,"335559739":120,"335559740":312}"> </span></p> </div>
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<p><span style="color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/request-a-demo/" target="_blank" rel="noopener"><span style="text-decoration: underline;"><span class="TextRun SCXW128260341 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW128260341 BCX8"><span class="TextRun SCXW265958618 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW265958618 BCX8"><span class="TextRun SCXW28705792 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW28705792 BCX8">See how </span><span class="NormalTextRun SpellingErrorV2Themed SCXW28705792 BCX8">Datagaps</span><span class="NormalTextRun SCXW28705792 BCX8"> validates data across your Purview-governed pipeline</span></span><span class="EOP Selected SCXW28705792 BCX8" data-ccp-props="{"335551550":2,"335551620":2,"335559738":360,"335559739":120}"> </span>→</span></span></span></span></span></a></span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Frequently Asked Questions </h2> </div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="1" aria-controls="elementor-tab-content-1531"><h3 class="eael-accordion-tab-title">How does Microsoft Purview differ from Datagaps DataOps Suite? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1531" class="eael-accordion-content clearfix" data-tab="1" aria-labelledby="faq-1"><p><span class="TextRun SCXW207559465 BCX8"><span class="NormalTextRun SCXW207559465 BCX8">Microsoft Purview is a unified data governance platform that handles metadata </span><span class="NormalTextRun SpellingErrorV2Themed SCXW207559465 BCX8">cataloging</span><span class="NormalTextRun SCXW207559465 BCX8">, classification, lineage, access policies, and compliance auditing. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW207559465 BCX8">Datagaps</span> <span class="NormalTextRun SpellingErrorV2Themed SCXW207559465 BCX8">DataOps</span><span class="NormalTextRun SCXW207559465 BCX8"> Suite is a data testing and validation platform that automates ETL validation, transformation testing, data quality scoring, schema drift detection, and BI report verification. Purview governs what data exists. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW207559465 BCX8">Datagaps</span><span class="NormalTextRun SCXW207559465 BCX8"> validates whether that data is correct.</span></span><span class="EOP Selected SCXW207559465 BCX8"> </span></p></div>
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<div id="faq-2" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="2" aria-controls="elementor-tab-content-1532"><h3 class="eael-accordion-tab-title">Can Datagaps DataOps Suite work alongside Microsoft Purview? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1532" class="eael-accordion-content clearfix" data-tab="2" aria-labelledby="faq-2"><p><span class="TextRun SCXW199315262 BCX8"><span class="NormalTextRun SCXW199315262 BCX8">Yes. The two platforms are complementary by design. Purview </span><span class="NormalTextRun SCXW199315262 BCX8">operates</span><span class="NormalTextRun SCXW199315262 BCX8"> as the metadata and policy layer across your data estate. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW199315262 BCX8">Datagaps</span> <span class="NormalTextRun SpellingErrorV2Themed SCXW199315262 BCX8">DataOps</span><span class="NormalTextRun SCXW199315262 BCX8"> Suite </span><span class="NormalTextRun SCXW199315262 BCX8">operates</span><span class="NormalTextRun SCXW199315262 BCX8"> as the automated testing </span><span class="NormalTextRun SCXW199315262 BCX8">and validation layer. Together, they cover governance from </span><span class="NormalTextRun SpellingErrorV2Themed SCXW199315262 BCX8">cataloging</span><span class="NormalTextRun SCXW199315262 BCX8"> through to record-level verification, creating a complete trust architecture.</span></span><span class="EOP SCXW199315262 BCX8"> </span></p></div>
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<div id="faq-3" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="3" aria-controls="elementor-tab-content-1533"><h3 class="eael-accordion-tab-title">Does Datagaps DataOps Suite support Microsoft Fabric and Azure? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1533" class="eael-accordion-content clearfix" data-tab="3" aria-labelledby="faq-3"><p><span class="TextRun SCXW58138077 BCX8"><span class="NormalTextRun SpellingErrorV2Themed SCXW58138077 BCX8">Datagaps</span> <span class="NormalTextRun SpellingErrorV2Themed SCXW58138077 BCX8">DataOps</span><span class="NormalTextRun SCXW58138077 BCX8"> Suite supports Microsoft Fabric, Azure Synapse, Azure SQL, Azure Data Factory, and SQL Server natively. It also supports over 200 </span><span class="NormalTextRun SCXW58138077 BCX8">additional</span><span class="NormalTextRun SCXW58138077 BCX8"> data sources including Snowflake, Databricks, Oracle, Informatica, and Amazon Redshift.</span></span><span class="EOP Selected SCXW58138077 BCX8"> </span></p></div>
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<div id="faq-4" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="4" aria-controls="elementor-tab-content-1534"><h3 class="eael-accordion-tab-title">How does Datagaps validate data differently from sampling-based tools? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1534" class="eael-accordion-content clearfix" data-tab="4" aria-labelledby="faq-4"><p><span class="TextRun SCXW51617071 BCX8"><span class="NormalTextRun SpellingErrorV2Themed SCXW51617071 BCX8">Datagaps</span><span class="NormalTextRun SCXW51617071 BCX8"> validates 100% of records at the row level, not a statistical sample. Every row, column, and transformation is verified against expected outcomes. Full-coverage validation is especially important for financial data, regulatory reporting, and migration projects where even small anomalies carry outsized business impact.</span></span><span class="EOP Selected SCXW51617071 BCX8"> </span></p></div>
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RajMohan Achanta </a>
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<p>Associate Product Manager at Datagaps. Shapes the product experience across ETL Validator, BI Validator, and Data Quality Monitor.</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/microsoft-purview-and-datagaps-why-data-trust-needs-both-governance-and-execution/">Microsoft Purview and Datagaps: Why Data Trust Needs Both Governance and Execution</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></content:encoded>
</item>
<item>
<title>How to Test a Data Migration to Microsoft Fabric in 5 Layers</title>
<link>https://www.datagaps.com/blog/microsoft-fabric-migration-testing/</link>
<dc:creator><![CDATA[Raj Mohan Achanta]]></dc:creator>
<pubDate>Mon, 26 Oct 2026 08:53:17 +0000</pubDate>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[Data Validation]]></category>
<category><![CDATA[Data Migration]]></category>
<category><![CDATA[Data Testing]]></category>
<category><![CDATA[Fabric Migration]]></category>
<category><![CDATA[Microsoft Fabric]]></category>
<guid isPermaLink="false">https://www.datagaps.com/?p=64017</guid>
<description><![CDATA[<p>Learn how to test Microsoft Fabric migrations with five layers of data validation, from metadata and data comparison to transformations and BI reports.</p>
<p>The post <a href="https://www.datagaps.com/blog/microsoft-fabric-migration-testing/">How to Test a Data Migration to Microsoft Fabric in 5 Layers</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></description>
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<p><span class="TextRun SCXW166660788 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW166660788 BCX8">Microsoft Fabric migration testing verifies that data moved into Fabric is structurally and factually </span><span class="NormalTextRun SCXW166660788 BCX8">accurate</span><span class="NormalTextRun SCXW166660788 BCX8">. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW166660788 BCX8">Datagaps</span><span class="NormalTextRun SCXW166660788 BCX8"> validates migrations through five no-code dataflow layers: metadata comparison, data profile comparison, source-to-target data comparison, transformation </span><span class="NormalTextRun SCXW166660788 BCX8">validation</span><span class="NormalTextRun SCXW166660788 BCX8"> and BI report validation.</span></span><span class="EOP Selected SCXW166660788 BCX8" data-ccp-props="{}"> </span></p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li><span data-contrast="auto">Fabric’s Migration Assistant helps execute migrations but does not automatically verify that migrated data is accurate.</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Start with metadata comparison to catch data type, column length, foreign key and nullability mismatches.</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Profile comparison confirms row counts, null percentages, distributions and distinct counts match across both systems.</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Data Compare reports matched rows, source-only and target-only rows, duplicates and column-level differences.</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">BI Analytics components compare Power BI and Tableau reports visually and at the data level.</span><span data-ccp-props="{}"> </span></li></ul> </div>
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<p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/request-a-demo/" target="_blank" rel="noopener"><span class="NormalTextRun SCXW117171178 BCX8">See how the </span><span class="NormalTextRun SpellingErrorV2Themed SCXW117171178 BCX8">Datagaps Validates</span><span class="NormalTextRun SCXW117171178 BCX8"> your Fabric Migration Layer by layer →</span></a></span></p> </div>
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<p><span class="TextRun SCXW234432753 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW234432753 BCX8">To test a <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-migration-testing-complete-guide/" target="_blank" rel="noopener">data migration</a></span> to Microsoft Fabric, </span><span class="NormalTextRun SCXW234432753 BCX8">validate</span><span class="NormalTextRun SCXW234432753 BCX8"> it in layers: compare metadata between source and target, compare data profiles, compare records row by row, validate transformations, and confirm BI reports still return </span><span class="NormalTextRun SCXW234432753 BCX8">accurate</span><span class="NormalTextRun SCXW234432753 BCX8"> data. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW234432753 BCX8">Datagaps</span><span class="NormalTextRun SCXW234432753 BCX8"> runs all five layers in a <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/dataops-suite/" target="_blank" rel="noopener">dataflow-based framework</a></span> without custom code.</span></span><span class="EOP Selected SCXW234432753 BCX8" data-ccp-props="{}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Why does a Microsoft Fabric migration need separate validation? </h2> </div>
</div>
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<p><span class="TextRun SCXW254287140 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW254287140 BCX8">Organizations across industries are increasingly moving their data workloads to Microsoft Fabric – some from SQL Server, others from <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/azure-synapse-testing/" target="_blank" rel="noopener">Synapse</a></span>, and many from a mix of legacy enterprise data warehouses, Azure Data Factory pipelines, or hybrid systems that have grown over time.</span></span></p> </div>
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<p><span class="TextRun SCXW16184191 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW16184191 BCX8">Microsoft Fabric brings together data engineering, warehousing, </span><span class="NormalTextRun SpellingErrorV2Themed SCXW16184191 BCX8">lakehouses</span><span class="NormalTextRun SCXW16184191 BCX8">, Power BI, notebooks, pipelines, AI, and analytics in a unified platform, which explains much of the momentum behind this shift. But moving to a new platform involves more than executing the migration itself. Validating that the outcome is </span><span class="NormalTextRun SCXW16184191 BCX8">accurate</span><span class="NormalTextRun SCXW16184191 BCX8"> matters just as much.</span></span><span class="EOP Selected SCXW16184191 BCX8" data-ccp-props="{}"> </span></p> </div>
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<p><span class="TextRun SCXW262146606 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW262146606 BCX8">This guide walks through a practical approach to data migration testing for Microsoft Fabric, including how to use </span><span class="NormalTextRun SpellingErrorV2Themed SCXW262146606 BCX8">Datagaps</span><span class="NormalTextRun SCXW262146606 BCX8"> to </span><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-testing-concepts/data-migration-testing/" target="_blank" rel="noopener"><span><span class="NormalTextRun SCXW262146606 BCX8">validate</span></span></a> </span></span><span style="text-decoration: underline; color: #1967d2;">migrations</span><span class="TextRun SCXW262146606 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW262146606 BCX8"> in a structured way instead of relying on manual SQL checks alone.</span></span><span class="EOP Selected SCXW262146606 BCX8" data-ccp-props="{}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">What does Microsoft Fabric already offer for migration? </h2> </div>
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<p><span data-contrast="auto">Microsoft Fabric includes several built-in capabilities that help simplify the migration process. The Fabric Migration Assistant for Data Warehouse migrations, for example, helps organizations move workloads into Fabric with significantly less manual effort.</span><span data-ccp-props="{}"> </span></p><p><span data-contrast="auto">For teams migrating from SQL Server or Synapse, the Migration Assistant can identify which objects migrate directly and which ones need adjustments – reducing the overhead of rebuilding schemas from scratch.</span><span data-ccp-props="{}"> </span></p> </div>
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<img loading="lazy" decoding="async" width="1120" height="803" src="https://www.datagaps.com/wp-content/uploads/Mgration-Assistant.1.png" class="attachment-full size-full wp-image-64051" alt="Mgration Assistant" srcset="https://www.datagaps.com/wp-content/uploads/Mgration-Assistant.1.png 1120w, https://www.datagaps.com/wp-content/uploads/Mgration-Assistant.1-300x215.png 300w, https://www.datagaps.com/wp-content/uploads/Mgration-Assistant.1-1024x734.png 1024w, https://www.datagaps.com/wp-content/uploads/Mgration-Assistant.1-768x551.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> </div>
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<p><span class="TextRun SCXW202591587 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW202591587 BCX8">While Fabric </span><span class="NormalTextRun SCXW202591587 BCX8">assists</span><span class="NormalTextRun SCXW202591587 BCX8"> with executing the migration, it does not automatically verify that the migrated data is fully </span><span class="NormalTextRun SCXW202591587 BCX8">accurate</span><span class="NormalTextRun SCXW202591587 BCX8"> from a testing standpoint. Migration testing tends to be <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/whitepaper/the-cost-benefit-of-data-migration-to-the-cloud/" target="_blank" rel="noopener">more involved than teams initially expect</a></span>, and the right tooling helps them </span><span class="NormalTextRun SCXW202591587 BCX8">identify</span><span class="NormalTextRun SCXW202591587 BCX8"> and resolve issues more efficiently.</span></span><span class="EOP Selected SCXW202591587 BCX8" data-ccp-props="{}"> </span></p> </div>
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<img loading="lazy" decoding="async" width="1094" height="525" src="https://www.datagaps.com/wp-content/uploads/Migration-steps.png" class="attachment-full size-full wp-image-64063" alt="Migration steps" srcset="https://www.datagaps.com/wp-content/uploads/Migration-steps.png 1094w, https://www.datagaps.com/wp-content/uploads/Migration-steps-300x144.png 300w, https://www.datagaps.com/wp-content/uploads/Migration-steps-1024x491.png 1024w, https://www.datagaps.com/wp-content/uploads/Migration-steps-768x369.png 768w" sizes="(max-width: 1094px) 100vw, 1094px" /> </div>
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<h2 class="elementor-heading-title elementor-size-default">What should be tested during a Microsoft Fabric migration? </h2> </div>
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<p><span class="TextRun SCXW199127089 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-testing-concepts/data-warehouse-testing-checklist/" target="_blank" rel="noopener"><span class="NormalTextRun SCXW199127089 BCX8">Test structure, statistics, records, business </span><span class="NormalTextRun SCXW199127089 BCX8">logic</span></a></span><span class="NormalTextRun SCXW199127089 BCX8"><a href="_wp_link_placeholder"> and reports.</a> The core concepts of migration testing remain consistent regardless of which platform is being migrated to. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW199127089 BCX8">Datagaps</span><span class="NormalTextRun SCXW199127089 BCX8"> approaches this in five layers, each addressing a different aspect of data accuracy and consistency.</span></span><span class="EOP Selected SCXW199127089 BCX8" data-ccp-props="{}"> </span></p> </div>
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<td style="text-align:left;">1. Metadata comparison</td>
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<td style="text-align:left;">2. Data profile comparison</td>
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<td style="text-align:left;">5. BI report validation</td>
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<h2 class="elementor-heading-title elementor-size-default">Layer 1: How does metadata comparison catch structural differences? </h2> </div>
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<p><span data-contrast="auto">A practical starting point is comparing the structure of the source and target databases before examining the data itself. Metadata comparison covers data type consistency, nullability rules, and whether <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-testing-concepts/database-testing/" target="_blank" rel="noopener">primary keys and constraints</a></span> are correctly defined.</span><span data-ccp-props="{}"> </span></p><p><span data-contrast="auto">Data type inconsistency is one of the more common issues encountered when moving between platforms. If a DECIMAL(18,4) field is mapped to a slightly different type in the target, downstream calculations may behave differently in ways that are not immediately visible.</span><span data-ccp-props="{}"> </span></p> </div>
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<p><span data-contrast="auto">In Datagaps, this can be done using the <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-validation-etl-testing-tools/" target="_blank" rel="noopener">Metadata Compare component</a></span> within a dataflow. After selecting the source and target databases and the relevant schema, Datagaps automatically maps the tables, with the option to adjust mappings for any renamed or deprecated tables.</span><span data-ccp-props="{}"> </span></p> </div>
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<p><span data-contrast="auto">Running this activity produces a detailed output that covers:</span><span data-ccp-props="{}"> </span></p><ul><li><span data-contrast="auto">Data type mismatches</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Columns that exist only in the source</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Column length mismatches</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Foreign key and nullability inconsistencies</span><span data-ccp-props="{}"> </span></li></ul> </div>
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<p><span class="TextRun SCXW188736474 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW188736474 BCX8">This first layer provides a clear picture of structural differences between the two databases, and resolving these issues before moving to data-level checks helps avoid cascading errors in later validation steps.</span></span><span class="EOP Selected SCXW188736474 BCX8" data-ccp-props="{}"> </span></p> </div>
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<img loading="lazy" decoding="async" width="1120" height="223" src="https://www.datagaps.com/wp-content/uploads/data-type-mismatch.png" class="attachment-full size-full wp-image-64082" alt="data type mismatch" srcset="https://www.datagaps.com/wp-content/uploads/data-type-mismatch.png 1120w, https://www.datagaps.com/wp-content/uploads/data-type-mismatch-300x60.png 300w, https://www.datagaps.com/wp-content/uploads/data-type-mismatch-1024x204.png 1024w, https://www.datagaps.com/wp-content/uploads/data-type-mismatch-768x153.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> <figcaption class="widget-image-caption wp-caption-text">data type mismatch</figcaption>
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<span >
Layer 2: How does data profile comparison confirm volume and distribution? </span>
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Once the structure has been verified, the next step is comparing the statistical profile of the data across both databases. This includes row counts, null percentages, minimum and maximum values, distinct counts, standard deviation, and related metrics. </p>
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<p><span data-contrast="auto">The goal is to confirm that the distribution and volume of data is consistent between source and target. Discrepancies at this level can indicate data that was lost, duplicated, or transformed unexpectedly during the migration process.</span><span data-ccp-props="{}"> </span></p><p><span data-contrast="auto">Datagaps’ <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/blog/data-profiling-in-etl-types-and-best-practices/" target="_blank" rel="noopener">Data Profile component</a></span> supports configurable aggregates, sampling options, and <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-observability-tool/" target="_blank" rel="noopener">observability metrics</a></span></span>. The comparison runs across both systems without requiring custom code, and results are available alongside the broader dataflow output.</span></p> </div>
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<img loading="lazy" decoding="async" width="1120" height="635" src="https://www.datagaps.com/wp-content/uploads/data-profile-comparison.png" class="attachment-full size-full wp-image-64091" alt="data profile comparison" srcset="https://www.datagaps.com/wp-content/uploads/data-profile-comparison.png 1120w, https://www.datagaps.com/wp-content/uploads/data-profile-comparison-300x170.png 300w, https://www.datagaps.com/wp-content/uploads/data-profile-comparison-1024x581.png 1024w, https://www.datagaps.com/wp-content/uploads/data-profile-comparison-768x435.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> <figcaption class="widget-image-caption wp-caption-text">Data profile comparison</figcaption>
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Layer 3: How does source-to-target data comparison find row-level mismatches? </span>
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This layer compares the data records between source and target row by row, based on mapped columns, to identify exact matches and mismatches. </p>
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<p><span class="TextRun SCXW212174370 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW212174370 BCX8">Implementing this manually in Fabric would typically involve building a separate notebook connecting both source and target systems, writing comparison logic, and handling multiple edge cases around data types and nulls. In </span><span class="NormalTextRun SpellingErrorV2Themed SCXW212174370 BCX8">Datagaps</span><span class="NormalTextRun SCXW212174370 BCX8">, this is handled through the <span style="text-decoration: underline;"><a href="https://www.datagaps.com/data-reconciliation/" target="_blank" rel="noopener"><span style="color: #1967d2; text-decoration: underline;">Data Compare</span> </a></span></span><span class="NormalTextRun SCXW212174370 BCX8">component</span><span class="NormalTextRun SCXW212174370 BCX8">, which can be added to an existing dataflow alongside the other validation steps. The </span><span class="NormalTextRun SCXW212174370 BCX8">component</span><span class="NormalTextRun SCXW212174370 BCX8"> supports options including mismatched row detection, column-wise comparisons, null and empty space handling, automatic data type conversion for comparison purposes, and configurable thresholds.</span></span><span class="EOP Selected SCXW212174370 BCX8" data-ccp-props="{}"> </span></p> </div>
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<p><span class="TextRun SCXW96290373 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW96290373 BCX8">One practical consideration: if metadata issues </span><span class="NormalTextRun SCXW96290373 BCX8">identified</span><span class="NormalTextRun SCXW96290373 BCX8"> in Layer 1 are not resolved before running data comparison, they are likely to surface here as errors. For example, if certain columns exist in the target but not the source, the comparison will fail at the mapping validation stage. Working through the layers in order is </span><span class="NormalTextRun SCXW96290373 BCX8">generally the</span><span class="NormalTextRun SCXW96290373 BCX8"> more efficient approach.</span></span><span class="EOP Selected SCXW96290373 BCX8" data-ccp-props="{}"> </span></p> </div>
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<img loading="lazy" decoding="async" width="1120" height="623" src="https://www.datagaps.com/wp-content/uploads/failure-in-case-metadata-issue-is-not-fixed.png" class="attachment-full size-full wp-image-64114" alt="Failure in case metadata issue is not fixed" srcset="https://www.datagaps.com/wp-content/uploads/failure-in-case-metadata-issue-is-not-fixed.png 1120w, https://www.datagaps.com/wp-content/uploads/failure-in-case-metadata-issue-is-not-fixed-300x167.png 300w, https://www.datagaps.com/wp-content/uploads/failure-in-case-metadata-issue-is-not-fixed-1024x570.png 1024w, https://www.datagaps.com/wp-content/uploads/failure-in-case-metadata-issue-is-not-fixed-768x427.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> <figcaption class="widget-image-caption wp-caption-text">Failure in case metadata issue is not fixed</figcaption>
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<p><span class="TextRun SCXW135716935 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW135716935 BCX8">Once mapping issues are addressed, the data comparison produces a report covering matched rows, rows only in source, rows only in target, duplicates, and column-level differences – all available as downloadable datasets for further investigation.</span></span></p> </div>
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Layer 4: How do you validate transformations and business logic? </span>
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Three layers of validation are sufficient for many migrations. For organizations that also need to verify business logic, a fourth layer covers transformation correctness. </p>
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<p><span class="TextRun SCXW24704174 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW24704174 BCX8">If transformations are applied at the source database as part of the existing data pipeline, this layer checks whether equivalent transformations at the target database produce the same output. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW24704174 BCX8">Datagaps</span><span class="NormalTextRun SCXW24704174 BCX8"> Transform </span><span class="NormalTextRun SCXW24704174 BCX8">component</span><span class="NormalTextRun SCXW24704174 BCX8"> in Dataflow allows teams to define <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/blog/generate-complex-sql-queries-using-dataops-suite-query-builder/" target="_blank" rel="noopener">custom queries</a></span> based on their business logic and compare the resulting datasets directly against the target.</span></span><span class="EOP SCXW24704174 BCX8" data-ccp-props="{}"> </span></p> </div>
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<img loading="lazy" decoding="async" width="1120" height="368" src="https://www.datagaps.com/wp-content/uploads/dataflow-for-data-transformation-validation.png" class="attachment-full size-full wp-image-64134" alt="dataflow for data transformation validation" srcset="https://www.datagaps.com/wp-content/uploads/dataflow-for-data-transformation-validation.png 1120w, https://www.datagaps.com/wp-content/uploads/dataflow-for-data-transformation-validation-300x99.png 300w, https://www.datagaps.com/wp-content/uploads/dataflow-for-data-transformation-validation-1024x336.png 1024w, https://www.datagaps.com/wp-content/uploads/dataflow-for-data-transformation-validation-768x252.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> <figcaption class="widget-image-caption wp-caption-text">Dataflow for data transformation validation</figcaption>
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<p><span class="TextRun SCXW102635206 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW102635206 BCX8">This matters most for migrations that go beyond moving data and <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/etl-testing-complete-guide/" target="_blank" rel="noopener">re-implement ETL logic</a></span> in </span><span class="NormalTextRun SCXW102635206 BCX8">a new environment</span><span class="NormalTextRun SCXW102635206 BCX8">. After the dataflow runs, you can review all the comparison datasets: matched results, differences, duplicates, and column-level discrepancies. That helps teams find and fix issues faster.</span></span><span class="EOP Selected SCXW102635206 BCX8" data-ccp-props="{}"> </span></p> </div>
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<img loading="lazy" decoding="async" width="1120" height="484" src="https://www.datagaps.com/wp-content/uploads/data-transformation-testing-1.png" class="attachment-full size-full wp-image-64138" alt="" srcset="https://www.datagaps.com/wp-content/uploads/data-transformation-testing-1.png 1120w, https://www.datagaps.com/wp-content/uploads/data-transformation-testing-1-300x130.png 300w, https://www.datagaps.com/wp-content/uploads/data-transformation-testing-1-1024x443.png 1024w, https://www.datagaps.com/wp-content/uploads/data-transformation-testing-1-768x332.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> <figcaption class="widget-image-caption wp-caption-text">Data transformation testing</figcaption>
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Layer 5: How do you validate BI reports after migrating to Fabric? </span>
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Migration affects the entire data lifecycle, and dashboards are a significant part of that. Organizations migrating <a href="https://www.datagaps.com/power-bi-automation-testing-tools/" target="_blank" rel="noopener noreferrer" style="color: #1967d2;text-decoration: underline;font-weight: 400">Power BI</a> or <a href="https://www.datagaps.com/automate-tableau-testing/" target="_blank" rel="noopener noreferrer" style="color: #1967d2;text-decoration: underline;font-weight: 400">Tableau reports</a> from SQL Server to Fabric need to verify that those reports continue to render correctly and return accurate data after the migration. </p>
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<p><span class="NormalTextRun SpellingErrorV2Themed SCXW139614263 BCX8">Datagaps</span><span class="NormalTextRun SCXW139614263 BCX8"> supports this through <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/bi-testing-tools/" target="_blank" rel="noopener">dedicated BI Analytics</a></span> components, including Power BI Regression, Power BI Upgrade, Tableau Regression, Tableau Upgrade, and Cross BI comparison. These allow reports from source and target environments to be compared both visually and at the data level, covering everything from summary totals to drill-down behaviour.</span></p> </div>
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Closing Thoughts </span>
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Microsoft Fabric’s Migration Assistant and related tooling make moving data workloads more manageable than it used to be. Validating that the migration produced accurate results is a separate concern, and it benefits from a structured, repeatable approach. </p>
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<p><span class="NormalTextRun SCXW158162858 BCX8">Fabric helps with migration execution. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW158162858 BCX8">Datagaps</span><span class="NormalTextRun SCXW158162858 BCX8"> helps with migration validation.</span></p> </div>
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<p><span class="NormalTextRun SpellingErrorV2Themed SCXW92331912 BCX8">Datagaps</span><span class="NormalTextRun SCXW92331912 BCX8"> dataflow-based testing framework supports all five layers described above and can be configured without writing custom code, so both engineering and analytics teams can use it. For organizations managing data migration projects of any scale, a layered validation process gives them the confidence to confirm that the migration was completed successfully.</span></p> </div>
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<p><span class="NormalTextRun SpellingErrorV2Themed SCXW81889293 BCX8">Datagaps</span><span class="NormalTextRun SCXW81889293 BCX8"> is a data testing and observability platform that helps data teams </span><span class="NormalTextRun SCXW81889293 BCX8">validate</span><span class="NormalTextRun SCXW81889293 BCX8">, monitor, and trust their data pipelines.</span></p> </div>
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<p><span style="color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/request-a-demo/" target="_blank" rel="noopener"><span style="text-decoration: underline;"><span class="NormalTextRun SCXW211184476 BCX8">See how </span><span class="NormalTextRun SpellingErrorV2Themed SCXW211184476 BCX8">Datagaps runs all five migration validation layers on your Fabric project</span><span class="NormalTextRun SCXW211184476 BCX8"> →</span></span></a></span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Frequently Asked Questions </h2> </div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="1" aria-controls="elementor-tab-content-1531"><h3 class="eael-accordion-tab-title">Does the Microsoft Fabric Migration Assistant validate migrated data?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1531" class="eael-accordion-content clearfix" data-tab="1" aria-labelledby="faq-1"><p><span class="NormalTextRun SCXW16673374 BCX8">No. The Fabric Migration Assistant helps move workloads into Fabric and </span><span class="NormalTextRun SCXW16673374 BCX8">identifies</span><span class="NormalTextRun SCXW16673374 BCX8"> which objects migrate directly and which need adjustments, reducing the overhead of rebuilding schemas. It does not automatically verify that migrated data is fully </span><span class="NormalTextRun SCXW16673374 BCX8">accurate</span><span class="NormalTextRun SCXW16673374 BCX8"> from a testing standpoint, so a separate, structured validation step is still </span><span class="NormalTextRun SCXW16673374 BCX8">required</span><span class="NormalTextRun SCXW16673374 BCX8">.</span></p></div>
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<div id="faq-2" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="2" aria-controls="elementor-tab-content-1532"><h3 class="eael-accordion-tab-title">What are the five layers of Microsoft Fabric migration testing?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1532" class="eael-accordion-content clearfix" data-tab="2" aria-labelledby="faq-2"><p><span class="NormalTextRun SpellingErrorV2Themed SCXW265290570 BCX8">Datagaps</span><span class="NormalTextRun SCXW265290570 BCX8"> approaches migration testing in five layers: metadata comparison of structure, data profile comparison of statistical metrics, source-to-target comparison of records row by row, transformation validation of business logic, and BI report validation for Power BI and Tableau dashboards. Each layer addresses a different aspect of data accuracy and consistency.</span></p></div>
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<div id="faq-3" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="3" aria-controls="elementor-tab-content-1533"><h3 class="eael-accordion-tab-title">Why should metadata comparison come first in a Fabric migration test? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1533" class="eael-accordion-content clearfix" data-tab="3" aria-labelledby="faq-3"><p><span class="TextRun SCXW56763168 BCX8"><span class="NormalTextRun SCXW56763168 BCX8">Metadata issues left unresolved tend to surface later as errors. If columns exist in the target but not the source, data comparison fails at the mapping validation stage. Checking data types, nullability, keys and constraints first, such as a </span><span class="NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW56763168 BCX8">DECIMAL(</span><span class="NormalTextRun SCXW56763168 BCX8">18,4) field mapped to a different type, avoids cascading errors in later validation steps.</span></span><span class="EOP Selected SCXW56763168 BCX8"> </span></p></div>
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<div id="faq-4" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="4" aria-controls="elementor-tab-content-1534"><h3 class="eael-accordion-tab-title">How does Datagaps compare source and target data row by row? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1534" class="eael-accordion-content clearfix" data-tab="4" aria-labelledby="faq-4"><p><span class="NormalTextRun SCXW244387459 BCX8">The Data Compare </span><span class="NormalTextRun SCXW244387459 BCX8">component</span><span class="NormalTextRun SCXW244387459 BCX8">, added to a dataflow, compares records based on mapped columns. It supports mismatched row detection, column-wise comparisons, null and empty space handling, automatic data type </span><span class="NormalTextRun SCXW244387459 BCX8">conversion</span><span class="NormalTextRun SCXW244387459 BCX8"> and configurable thresholds. The report covers matched rows, rows only in source or target, </span><span class="NormalTextRun SCXW244387459 BCX8">duplicates</span><span class="NormalTextRun SCXW244387459 BCX8"> and column-level differences as downloadable datasets.</span></p></div>
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<div id="faq-5" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="5" aria-controls="elementor-tab-content-1535"><h3 class="eael-accordion-tab-title">Can Datagaps validate Power BI and Tableau reports after a Fabric migration? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1535" class="eael-accordion-content clearfix" data-tab="5" aria-labelledby="faq-5"><p><span class="NormalTextRun SCXW255623493 BCX8">Yes. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW255623493 BCX8">Datagaps</span><span class="NormalTextRun SCXW255623493 BCX8"> provides BI Analytics components including Power BI Regression, Power BI Upgrade, Tableau Regression, Tableau Upgrade and Cross BI comparison. They compare reports from source and target environments both visually and at the data level, covering everything from summary totals to drill-down behaviour.</span></p></div>
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<a href="https://www.linkedin.com/in/raj-mohan-achanta-41454b1a2/" >
RajMohan Achanta </a>
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Associate Product Manager, Datagaps </p>
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<p>Associate Product Manager at Datagaps. Shapes the product experience across ETL Validator, BI Validator, and Data Quality Monitor.</p> </div>
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Avinash Keshri </a>
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Head, Product Marketing </p>
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<p>Head of Product Marketing at Datagaps and IIM Bangalore alumnus. 13+ years of experience in commercializing AI and data platforms across global markets.</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/microsoft-fabric-migration-testing/">How to Test a Data Migration to Microsoft Fabric in 5 Layers</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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<title>How to Generate Synthetic Test Data for Microsoft Fabric with TDM</title>
<link>https://www.datagaps.com/blog/synthetic-test-data-generation-microsoft-fabric/</link>
<dc:creator><![CDATA[Raj Mohan Achanta]]></dc:creator>
<pubDate>Thu, 22 Oct 2026 12:27:27 +0000</pubDate>
<category><![CDATA[Data Quality]]></category>
<category><![CDATA[Data Validation]]></category>
<category><![CDATA[DataOps]]></category>
<category><![CDATA[Datagaps TDM]]></category>
<category><![CDATA[Microsoft Fabric]]></category>
<category><![CDATA[PII Masking]]></category>
<category><![CDATA[Synthetic Data]]></category>
<category><![CDATA[Test Data Management]]></category>
<guid isPermaLink="false">https://www.datagaps.com/?p=63404</guid>
<description><![CDATA[<p>Generate synthetic test data for Microsoft Fabric with Datagaps TDM. Use AI to create masked, realistic data for secure, scalable testing.</p>
<p>The post <a href="https://www.datagaps.com/blog/synthetic-test-data-generation-microsoft-fabric/">How to Generate Synthetic Test Data for Microsoft Fabric with TDM</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></description>
<content:encoded><![CDATA[ <div data-elementor-type="wp-post" data-elementor-id="63404" class="elementor elementor-63404" data-elementor-post-type="post">
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<p><span class="TextRun SCXW99550397 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SpellingErrorV2Themed SCXW99550397 BCX8">Datagaps</span><span class="NormalTextRun SCXW99550397 BCX8"><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/test-data-manager/" target="_blank" rel="noopener"> Test Data Manager</a></span> (TDM) is an AI-powered synthetic data tool that learns patterns from sample data to generate privacy-safe test data for Microsoft Fabric </span><span class="NormalTextRun SpellingErrorV2Themed SCXW99550397 BCX8">lakehouses</span><span class="NormalTextRun SCXW99550397 BCX8"> and warehouses, masking PII automatically and reducing work that could take weeks to under an hour.</span></span><span class="EOP Selected SCXW99550397 BCX8" data-ccp-props="{}"> </span></p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li><span data-contrast="auto">Manually copying or creating test data breaks down at 30 to 40 or more tables per testing cycle.</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">TDM has two components: Data Models configure generation, and Datasets generate and load the data.</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">AI auto-detects entities, treats classified columns as PII or confidential, and masks them during generation.</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Sample data is a prerequisite; by default TDM takes 5,000 rows from the connected source.</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Generating 100 or 1,000 rows completes in about five seconds, with multiple tables processed in parallel.</span></li></ul> </div>
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<div class="elementor-element elementor-element-2bb8934 elementor-widget elementor-widget-text-editor" data-id="2bb8934" data-element_type="widget" data-e-type="widget" data-widget_type="text-editor.default">
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<p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/test-data-manager-trial-request/" target="_blank" rel="noopener"><span class="NormalTextRun SCXW77785008 BCX8">See how </span><span class="NormalTextRun SpellingErrorV2Themed SCXW77785008 BCX8">Datagaps</span><span class="NormalTextRun SCXW77785008 BCX8"> TDM generates masked test data for your Fabric tables →</span></a></span></p> </div>
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<p><span class="TextRun SCXW95773873 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW95773873 BCX8">To generate synthetic test data for Microsoft Fabric, configure a </span></span><span class="TextRun SCXW95773873 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW95773873 BCX8">Data Model</span></span><span class="TextRun SCXW95773873 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW95773873 BCX8"> in </span><span class="NormalTextRun SpellingErrorV2Themed SCXW95773873 BCX8">Datagaps</span><span class="NormalTextRun SCXW95773873 BCX8"> Test Data Manager (TDM) — connect your </span><span class="NormalTextRun SpellingErrorV2Themed SCXW95773873 BCX8">lakehouse</span><span class="NormalTextRun SCXW95773873 BCX8"> or warehouse, let AI detect and mask entities, load sample data and confirm target data types — then use a </span></span><span class="TextRun SCXW95773873 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW95773873 BCX8">Dataset</span></span><span class="TextRun SCXW95773873 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW95773873 BCX8"> to generate the volume you need and load it into tables or files.</span></span><span class="EOP Selected SCXW95773873 BCX8" data-ccp-props="{}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Why is test data for Microsoft Fabric pipelines hard to manage? </h2> </div>
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<p><span class="TextRun SCXW193107267 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW193107267 BCX8">In <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/microsoft-fabric-testing-automation/" target="_blank" rel="noopener">Microsoft Fabric</a></span>, pipelines and notebooks are only as good as the data used to test them. In a typical enterprise setting, developers work with minimal or dummy data, and QA teams manually copy production data or create new entries for staging. In large setups with 30 to 40 or more tables per testing cycle, this becomes unsustainable fast, especially when PII masking and relational integrity are also in scope.</span></span><span class="EOP SCXW193107267 BCX8" data-ccp-props="{}"> </span></p> </div>
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<p><span class="TextRun SCXW45042379 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SpellingErrorV2Themed SCXW45042379 BCX8">Datagaps</span><span class="NormalTextRun SCXW45042379 BCX8"> Test Data Manager (TDM)</span></span><span class="TextRun SCXW45042379 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW45042379 BCX8"> addresses this directly. Its AI capabilities generate synthetic data that covers all production-level nuances without manual effort, reducing what could take weeks to under an hour.</span></span><span class="EOP Selected SCXW45042379 BCX8" data-ccp-props="{}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">How does Datagaps TDM generate synthetic data for Microsoft Fabric? </h2> </div>
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<p><span class="TextRun SCXW68732214 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SpellingErrorV2Themed SCXW68732214 BCX8">Datagaps</span><span class="NormalTextRun SCXW68732214 BCX8"> TDM has two core components: </span></span><strong><span class="TextRun SCXW68732214 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW68732214 BCX8">Data Models</span></span></strong><span class="TextRun SCXW68732214 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW68732214 BCX8"> and </span></span><strong><span class="TextRun SCXW68732214 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW68732214 BCX8">Datasets</span></span><span class="TextRun SCXW68732214 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW68732214 BCX8">.</span></span></strong></p> </div>
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<p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/test-data-manager/" target="_blank" rel="noopener"><strong><span class="TextRun SCXW78120396 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW78120396 BCX8">Data Models</span></span></strong></a></span><span class="TextRun SCXW78120396 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW78120396 BCX8"> are where you configure the data you want to generate for each table. This includes masking data, fetching sample data for generation, defining the target data type, and all other configuration requirements.</span></span></p> </div>
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<p><strong><span class="TextRun SCXW117414207 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW117414207 BCX8">Datasets</span></span></strong><span class="TextRun SCXW117414207 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW117414207 BCX8"> use the configured data model and generate data using AI/ML capabilities. You define the volume of data to be generated and then choose to either save it to an external location or load it directly into tables for testing.</span></span></p> </div>
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<img loading="lazy" decoding="async" width="1120" height="677" src="https://www.datagaps.com/wp-content/uploads/image-1-1.png" class="attachment-full size-full wp-image-63571" alt="Data Models & Datasets of DQ in Datagaps" srcset="https://www.datagaps.com/wp-content/uploads/image-1-1.png 1120w, https://www.datagaps.com/wp-content/uploads/image-1-1-300x181.png 300w, https://www.datagaps.com/wp-content/uploads/image-1-1-1024x619.png 1024w, https://www.datagaps.com/wp-content/uploads/image-1-1-768x464.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> <figcaption class="widget-image-caption wp-caption-text">Test Data Manager — Data Models and Datasets</figcaption>
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<h2 class="elementor-heading-title elementor-size-default">How do you configure a TDM data model for Microsoft Fabric tables?</h2> </div>
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<p><span class="TextRun SCXW19038420 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW19038420 BCX8">For demonstration purposes, two schemas were connected into the data model, </span><span class="NormalTextRun SCXW19038420 BCX8">containing</span><span class="NormalTextRun SCXW19038420 BCX8"> 5 to 10 dimension and fact tables, each with </span><span class="NormalTextRun SCXW19038420 BCX8">approximately 20</span><span class="NormalTextRun SCXW19038420 BCX8"> to 30 columns. When you connect your Fabric data source, you connect your specific </span><span class="NormalTextRun SpellingErrorV2Themed SCXW19038420 BCX8">lakehouse</span><span class="NormalTextRun SCXW19038420 BCX8"> or warehouse. The moment a data source is connected, </span><span class="NormalTextRun SpellingErrorV2Themed SCXW19038420 BCX8">Datagaps</span><span class="NormalTextRun SCXW19038420 BCX8"> fetches relevant metadata and automatically reflects it in the interface.</span></span><span class="EOP Selected SCXW19038420 BCX8" data-ccp-props="{}"> </span></p> </div>
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How are entities classified and PII masked? </span>
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There is an additional column called Entity, which is used to define the type of a column and whether it needs to be masked. When any column is classified as one of the defined entities, Datagaps treats that data as PII or confidential and masks it during generation. Datagaps already supports GDPR, CCPA, and HIPAA-aligned workflows, which means data security concerns are addressed at the platform level. </p>
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<p><span class="TextRun SCXW151286073 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW151286073 BCX8">Entity detection can be done manually, or </span><span class="NormalTextRun SpellingErrorV2Themed SCXW151286073 BCX8">Datagaps</span><span class="NormalTextRun SCXW151286073 BCX8"> can handle it automatically using AI. There is a built-in </span><span class="NormalTextRun SCXW151286073 BCX8">option</span><span class="NormalTextRun SCXW151286073 BCX8"> to auto-detect entities for your tables.</span></span><span class="EOP Selected SCXW151286073 BCX8" data-ccp-props="{}"> </span></p> </div>
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<img loading="lazy" decoding="async" width="1120" height="529" src="https://www.datagaps.com/wp-content/uploads/image-3-1.png" class="attachment-full size-full wp-image-63587" alt="Auto-detect entities" srcset="https://www.datagaps.com/wp-content/uploads/image-3-1.png 1120w, https://www.datagaps.com/wp-content/uploads/image-3-1-300x142.png 300w, https://www.datagaps.com/wp-content/uploads/image-3-1-1024x484.png 1024w, https://www.datagaps.com/wp-content/uploads/image-3-1-768x363.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> </div>
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<p><span class="NormalTextRun SCXW16242282 BCX8">When auto-detect entities </span><span class="NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW16242282 BCX8">is</span><span class="NormalTextRun SCXW16242282 BCX8"> used, </span><span class="NormalTextRun SpellingErrorV2Themed SCXW16242282 BCX8">Datagaps</span><span class="NormalTextRun SCXW16242282 BCX8"> applies its internal AI/ML models to classify each column into one of its predefined entity categories. Columns </span><span class="NormalTextRun SCXW16242282 BCX8">containing</span><span class="NormalTextRun SCXW16242282 BCX8"> names, payment details, addresses, date-time values, and similar data are auto-classified without requiring manual intervention. This is a particularly powerful feature when connecting tables for the first time and configuring them at scale. The platform can generate millions of records across formats with built-in masking.</span></p> </div>
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<img loading="lazy" decoding="async" width="1120" height="516" src="https://www.datagaps.com/wp-content/uploads/image-4-1.png" class="attachment-full size-full wp-image-63591" alt="" srcset="https://www.datagaps.com/wp-content/uploads/image-4-1.png 1120w, https://www.datagaps.com/wp-content/uploads/image-4-1-300x138.png 300w, https://www.datagaps.com/wp-content/uploads/image-4-1-1024x472.png 1024w, https://www.datagaps.com/wp-content/uploads/image-4-1-768x354.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> </div>
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Why is sample data required? </span>
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<p class="elementor-icon-box-description">
The only prerequisite is that some sample data must be loaded before using this feature. To auto-detect entities and generate synthetic data, existing data is required so the system can identify patterns in those values. You can filter that sample however you choose, whether using a specific set of values or a random selection. This sample enables the AI to understand the patterns needed for accurate synthetic data generation. </p>
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<img loading="lazy" decoding="async" width="1120" height="537" src="https://www.datagaps.com/wp-content/uploads/image-5-1.png" class="attachment-full size-full wp-image-63599" alt="synthetic data generation" srcset="https://www.datagaps.com/wp-content/uploads/image-5-1.png 1120w, https://www.datagaps.com/wp-content/uploads/image-5-1-300x144.png 300w, https://www.datagaps.com/wp-content/uploads/image-5-1-1024x491.png 1024w, https://www.datagaps.com/wp-content/uploads/image-5-1-768x368.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> </div>
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How do you preserve referential integrity between tables? </span>
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<p class="elementor-icon-box-description">
For projects with relationships between tables, these need to be accounted for carefully. A foreign key, for example, must be mapped correctly and must be available in its source table. Otherwise, the generated data will lack referential integrity. Datagaps allows you to define these relationships and map columns to their parent column in another table. </p>
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<img loading="lazy" decoding="async" width="1120" height="482" src="https://www.datagaps.com/wp-content/uploads/image-6.png" class="attachment-full size-full wp-image-63600" alt="Relation - child, parent column" srcset="https://www.datagaps.com/wp-content/uploads/image-6.png 1120w, https://www.datagaps.com/wp-content/uploads/image-6-300x129.png 300w, https://www.datagaps.com/wp-content/uploads/image-6-1024x441.png 1024w, https://www.datagaps.com/wp-content/uploads/image-6-768x331.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> </div>
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How much sample data does TDM use? </span>
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<p class="elementor-icon-box-description">
By default, Datagaps takes 5,000 rows of sample data from the connected source. This can be increased or decreased after initial testing. It also allows you to add a WHERE clause to filter specific data for sample generation, which is useful when testing for a particular region or segment. </p>
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<img loading="lazy" decoding="async" width="1120" height="617" src="https://www.datagaps.com/wp-content/uploads/image-7.png" class="attachment-full size-full wp-image-63605" alt="" srcset="https://www.datagaps.com/wp-content/uploads/image-7.png 1120w, https://www.datagaps.com/wp-content/uploads/image-7-300x165.png 300w, https://www.datagaps.com/wp-content/uploads/image-7-1024x564.png 1024w, https://www.datagaps.com/wp-content/uploads/image-7-768x423.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> </div>
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<p><span class="TextRun SCXW128503419 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW128503419 BCX8">When you generate your sample data, you will notice that certain columns are masked. These are the same columns for which an entity was defined in the configuration step.</span></span><span class="EOP Selected SCXW128503419 BCX8" data-ccp-props="{}"> </span></p> </div>
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<div class="swiper-slide" role="group" aria-roledescription="slide" aria-label="1 of 2"><figure class="swiper-slide-inner"><img decoding="async" class="swiper-slide-image" src="https://www.datagaps.com/wp-content/uploads/image-8-1-1024x585.png" alt="Sample data" /></figure></div><div class="swiper-slide" role="group" aria-roledescription="slide" aria-label="2 of 2"><figure class="swiper-slide-inner"><img decoding="async" class="swiper-slide-image" src="https://www.datagaps.com/wp-content/uploads/image-9-1-1024x595.png" alt="Synthetic data" /></figure></div> </div>
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How are target data types defined? </span>
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<p class="elementor-icon-box-description">
Once that configuration is complete, the next step is defining the quality of your synthetic data. This is where you define the target data types for generation. For entity columns, there are predefined data types that Datagaps refers to during generation. This too does not need to be done manually. The auto-recommend feature assigns entities and correct target data types for each table. All that is required is a review to confirm accuracy. </p>
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<div class="elementor-image-carousel-wrapper swiper" role="region" aria-roledescription="carousel" aria-label="Image Carousel" dir="ltr">
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<div class="swiper-slide" role="group" aria-roledescription="slide" aria-label="1 of 2"><figure class="swiper-slide-inner"><img decoding="async" class="swiper-slide-image" src="https://www.datagaps.com/wp-content/uploads/image-10-1024x474.png" alt="Data Type in Synthetic data" /></figure></div><div class="swiper-slide" role="group" aria-roledescription="slide" aria-label="2 of 2"><figure class="swiper-slide-inner"><img decoding="async" class="swiper-slide-image" src="https://www.datagaps.com/wp-content/uploads/image-11-1024x571.png" alt="Generator data in synthetic data" /></figure></div> </div>
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<i aria-hidden="true" class="eicon-chevron-left"></i> </div>
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<i aria-hidden="true" class="eicon-chevron-right"></i> </div>
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What is Fake Data in Datagaps TDM? </span>
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<p class="elementor-icon-box-description">
The real capability of synthetic data generation becomes apparent at this step. For columns containing names, payment details, or other entity types, the system automatically classifies the target type and sets the data up as realistic fake data. This is what Datagaps refers to as Fake Data. </p>
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<img loading="lazy" decoding="async" width="444" height="574" src="https://www.datagaps.com/wp-content/uploads/image-13.png" class="attachment-large size-large wp-image-63624" alt="Fake data consists of AI or machine-generated values" srcset="https://www.datagaps.com/wp-content/uploads/image-13.png 444w, https://www.datagaps.com/wp-content/uploads/image-13-232x300.png 232w" sizes="(max-width: 444px) 100vw, 444px" /> </div>
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<p><span class="NormalTextRun SCXW39084132 BCX8">Fake data consists of AI or machine-generated values defined across multiple categories. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW39084132 BCX8">Datagaps</span><span class="NormalTextRun SCXW39084132 BCX8"> maintains a predefined list of categories aligned with real-world data, and it classifies columns into one of those categories before generation. While the AI handles classification accurately in most cases, it is strongly recommended to review the target type of each column at this step. Verify that a column is not misclassified, for example, as an address when it </span><span class="NormalTextRun AdvancedProofingIssueV2Themed SCXW39084132 BCX8">actually contains</span><span class="NormalTextRun SCXW39084132 BCX8"> a name.</span></p> </div>
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<img loading="lazy" decoding="async" width="462" height="748" src="https://www.datagaps.com/wp-content/uploads/image-12.png" class="attachment-medium_large size-medium_large wp-image-63620" alt="Fake Data in TDM" srcset="https://www.datagaps.com/wp-content/uploads/image-12.png 462w, https://www.datagaps.com/wp-content/uploads/image-12-185x300.png 185w" sizes="(max-width: 462px) 100vw, 462px" /> </div>
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<p><span class="TextRun SCXW199547311 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW199547311 BCX8">Almost any category of data you can think of is predefined within TDM, including blood group, card details, age, email, contact information, and more. All of them can be mapped correctly to your tables without manual classification, and the AI generates these values as part of the synthetic data generation process.</span></span></p> </div>
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<img loading="lazy" decoding="async" width="1120" height="826" src="https://www.datagaps.com/wp-content/uploads/image-14.png" class="attachment-full size-full wp-image-63626" alt="Fake Data providers" srcset="https://www.datagaps.com/wp-content/uploads/image-14.png 1120w, https://www.datagaps.com/wp-content/uploads/image-14-300x221.png 300w, https://www.datagaps.com/wp-content/uploads/image-14-1024x755.png 1024w, https://www.datagaps.com/wp-content/uploads/image-14-768x566.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> </div>
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<span >
Can you apply constraints, gender and locale options? </span>
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<p class="elementor-icon-box-description">
Yes. Constraints can also be applied during generation. For datetime columns, for example, you can define a specific range to ensure the generated values fall within a desired window. </p>
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<img loading="lazy" decoding="async" width="439" height="707" src="https://www.datagaps.com/wp-content/uploads/image-15.png" class="attachment-large size-large wp-image-63633" alt="Fake data valid till date" srcset="https://www.datagaps.com/wp-content/uploads/image-15.png 439w, https://www.datagaps.com/wp-content/uploads/image-15-186x300.png 186w" sizes="(max-width: 439px) 100vw, 439px" /> </div>
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<p><span class="TextRun SCXW232524628 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW232524628 BCX8">If you want generated names to reflect a particular gender, that can be configured as well. And if you need the synthetic data to reflect details from a specific locale, that option is available too.</span></span><span class="EOP Selected SCXW232524628 BCX8" data-ccp-props="{}"> </span></p> </div>
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<div class="elementor-image-carousel-wrapper swiper" role="region" aria-roledescription="carousel" aria-label="Image Carousel" dir="ltr">
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<div class="swiper-slide" role="group" aria-roledescription="slide" aria-label="1 of 2"><figure class="swiper-slide-inner"><img decoding="async" class="swiper-slide-image" src="https://www.datagaps.com/wp-content/uploads/image16-1024x413.png" alt="preview of synthetic data in TDM" /></figure></div><div class="swiper-slide" role="group" aria-roledescription="slide" aria-label="2 of 2"><figure class="swiper-slide-inner"><img decoding="async" class="swiper-slide-image" src="https://www.datagaps.com/wp-content/uploads/image-17-1.png" alt="Locale Type" /></figure></div> </div>
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<span >
Data model configuration at a glance </span>
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<p class="elementor-icon-box-description">
All of the above configuration is done within the Data Model component of TDM. This can be done across all tables in a single pass before moving to the generation phase. <br><br>1. Go to the data model <br>
2. Use AI to auto-detect entities and verify them <br>
3. Load sample data <br>
4. Let AI define target types for synthetic data and verify again <br>
5. Make adjustments as required for enterprise needs </p>
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<span >
How do you generate and load synthetic data with TDM datasets? </span>
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<p class="elementor-icon-box-description">
Once configuration is complete, the process moves to the Dataset component of TDM, where you choose the output destination, select tables, generate the data and load it into the target. </p>
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<span >
Choose where the generated data is saved </span>
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<p class="elementor-icon-box-description">
Here, you define where you want to save the generated data and how. This includes choosing whether to save the synthetic data to CSV files, load it directly into tables in an existing database, into a specific schema, and so on. </p>
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<img loading="lazy" decoding="async" width="1120" height="514" src="https://www.datagaps.com/wp-content/uploads/image-18.png" class="attachment-full size-full wp-image-63644" alt="Properties of test data" srcset="https://www.datagaps.com/wp-content/uploads/image-18.png 1120w, https://www.datagaps.com/wp-content/uploads/image-18-300x138.png 300w, https://www.datagaps.com/wp-content/uploads/image-18-1024x470.png 1024w, https://www.datagaps.com/wp-content/uploads/image-18-768x352.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> </div>
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<span >
Select tables and run generation </span>
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Next, you select the tables for which you want to generate data, with the option to do this for all tables or only selected ones. </p>
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<p><span class="NormalTextRun SCXW29224212 BCX8">Data generation typically takes a few seconds to a few minutes, depending on the volume configured. For 100 or 1,000 rows, generation completes in approximately 5 seconds. When generating across multiple tables, </span><span class="NormalTextRun SpellingErrorV2Themed SCXW29224212 BCX8">Datagaps</span><span class="NormalTextRun SCXW29224212 BCX8"> processes them in parallel within the same </span><span class="NormalTextRun SCXW29224212 BCX8">time frame</span><span class="NormalTextRun SCXW29224212 BCX8">.</span></p> </div>
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Load data into the target table or file </span>
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Once data is generated, it can be loaded into the target table or file. If a table is selected as the target, the schemas must be clearly defined to avoid conflicts during insertion. </p>
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<img loading="lazy" decoding="async" width="1120" height="269" src="https://www.datagaps.com/wp-content/uploads/image-22.png" class="attachment-full size-full wp-image-63652" alt="Load data into the target table" srcset="https://www.datagaps.com/wp-content/uploads/image-22.png 1120w, https://www.datagaps.com/wp-content/uploads/image-22-300x72.png 300w, https://www.datagaps.com/wp-content/uploads/image-22-1024x246.png 1024w, https://www.datagaps.com/wp-content/uploads/image-22-768x184.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> </div>
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What happens if sample data is missing? </span>
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If sample data was not loaded for some tables during configuration, the tool will fail during generation and prompt you to go back and add sample data. This is by design. Sample data is what enables the system to identify patterns within your data so the output remains relevant to your use case.<br><br>When all items are configured correctly, data generation will succeed consistently. </p>
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Choose save behavior and confirm the load </span>
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When loading into the target table, you can choose the save behavior: append, drop, or truncate. The appropriate choice depends on how you intend to proceed with testing once the data is available. </p>
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<img loading="lazy" decoding="async" width="445" height="304" src="https://www.datagaps.com/wp-content/uploads/Append-the-result.png" class="attachment-large size-large wp-image-63662" alt="Append the result of generated data" srcset="https://www.datagaps.com/wp-content/uploads/Append-the-result.png 445w, https://www.datagaps.com/wp-content/uploads/Append-the-result-300x205.png 300w" sizes="(max-width: 445px) 100vw, 445px" /> </div>
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<ul><li><span class="TextRun SCXW170806998 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW170806998 BCX8">If the load is successful, a confirmation is shown. If not, </span><span class="NormalTextRun SpellingErrorV2Themed SCXW170806998 BCX8">Datagaps</span><span class="NormalTextRun SCXW170806998 BCX8"> provides logs so you can trace and resolve the exact issue. Loading of final synthetic data is straightforward whether you are saving to a file such as CSV or directly to a table. For table targets, </span><span class="NormalTextRun SCXW170806998 BCX8">validate</span><span class="NormalTextRun SCXW170806998 BCX8"> the schema and the process is complete.</span></span><span class="EOP Selected SCXW170806998 BCX8" data-ccp-props="{}"> </span></li></ul> </div>
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<img loading="lazy" decoding="async" width="1120" height="213" src="https://www.datagaps.com/wp-content/uploads/Load-data-complete.png" class="attachment-full size-full wp-image-63666" alt="Load data complete in TDM" srcset="https://www.datagaps.com/wp-content/uploads/Load-data-complete.png 1120w, https://www.datagaps.com/wp-content/uploads/Load-data-complete-300x57.png 300w, https://www.datagaps.com/wp-content/uploads/Load-data-complete-1024x195.png 1024w, https://www.datagaps.com/wp-content/uploads/Load-data-complete-768x146.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /> </div>
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<h2 class="elementor-heading-title elementor-size-default">Conclusion </h2> </div>
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<p><span data-contrast="auto">That is the complete process for generating synthetic data at scale for enterprise environments, regardless of the data source, whether Microsoft Fabric or others. Datagaps supports each step of the testing process and provides all the tools required for testing any part of an analytical workflow.</span><span data-ccp-props="{}"> </span></p><p><span data-contrast="auto">The key differentiator is AI integration at every step. The same work done manually could take weeks to replicate data correctly, and even then, it would be difficult to introduce the same level of diversity, especially for edge cases and unexpected patterns.</span><span data-ccp-props="{}"> </span></p><p><span data-contrast="auto">With AI-powered generation, teams can produce large volumes of synthetic data, introduce varied patterns and data rules, and run comprehensive testing of pipelines before anything reaches production.</span></p> </div>
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<p><span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/test-data-manager-trial-request/" target="_blank" rel="noopener"><span class="EOP Selected SCXW265958618 BCX8" data-ccp-props="{}"><span class="TextRun SCXW10738096 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW10738096 BCX8">See how TDM generates masked, relationally consistent test data for Fabric →</span></span> </span></a></span></span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Frequently Asked Questions </h2> </div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="1" aria-controls="elementor-tab-content-1531"><h3 class="eael-accordion-tab-title">How does Datagaps TDM generate synthetic data for Microsoft Fabric? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1531" class="eael-accordion-content clearfix" data-tab="1" aria-labelledby="faq-1"><p><span class="TextRun SCXW3880589 BCX8"><span class="NormalTextRun SCXW3880589 BCX8">TDM uses two components. In the Data Model you connect a Fabric </span><span class="NormalTextRun SpellingErrorV2Themed SCXW3880589 BCX8">lakehouse</span><span class="NormalTextRun SCXW3880589 BCX8"> or warehouse, classify entities, load sample </span><span class="NormalTextRun SCXW3880589 BCX8">data</span><span class="NormalTextRun SCXW3880589 BCX8"> and set target data types. In the Dataset you choose the volume, select </span><span class="NormalTextRun SCXW3880589 BCX8">tables</span><span class="NormalTextRun SCXW3880589 BCX8"> and save the output to files such as CSV or load it directly into tables. AI/ML capabilities generate the data from patterns in the sample.</span></span><span class="EOP Selected SCXW3880589 BCX8"> </span></p></div>
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<div id="faq-2" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="2" aria-controls="elementor-tab-content-1532"><h3 class="eael-accordion-tab-title">Does Datagaps TDM mask PII in synthetic test data?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1532" class="eael-accordion-content clearfix" data-tab="2" aria-labelledby="faq-2"><p><span class="NormalTextRun SCXW208588374 BCX8">Yes. When a column is classified as one of TDM’s defined entities, </span><span class="NormalTextRun SpellingErrorV2Themed SCXW208588374 BCX8">Datagaps</span><span class="NormalTextRun SCXW208588374 BCX8"> treats it as PII or confidential and masks it during generation. Entities can be set manually or auto-detected with AI, which classifies names, payment details, addresses and date-time values. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW208588374 BCX8">Datagaps</span><span class="NormalTextRun SCXW208588374 BCX8"> supports GDPR, CCPA and HIPAA-aligned workflows.</span></p></div>
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<div id="faq-3" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="3" aria-controls="elementor-tab-content-1533"><h3 class="eael-accordion-tab-title">Why does TDM need sample data before generating synthetic data?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1533" class="eael-accordion-content clearfix" data-tab="3" aria-labelledby="faq-3"><p><span class="NormalTextRun SCXW221225326 BCX8">Sample data lets the AI </span><span class="NormalTextRun SCXW221225326 BCX8">identify</span><span class="NormalTextRun SCXW221225326 BCX8"> patterns in existing values so generated output stays relevant to your use case. By </span><span class="NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW221225326 BCX8">default</span><span class="NormalTextRun SCXW221225326 BCX8"> TDM takes 5,000 rows from the connected source, which you can increase, </span><span class="NormalTextRun SCXW221225326 BCX8">decrease</span><span class="NormalTextRun SCXW221225326 BCX8"> or filter with a WHERE clause. If sample data is missing for a table, generation fails by design and prompts you to add it.</span></p></div>
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<div id="faq-4" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="4" aria-controls="elementor-tab-content-1534"><h3 class="eael-accordion-tab-title">How does TDM keep referential integrity across related Fabric tables? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1534" class="eael-accordion-content clearfix" data-tab="4" aria-labelledby="faq-4"><p><span class="TextRun SCXW18591511 BCX8"><span class="NormalTextRun SCXW18591511 BCX8">TDM lets you define relationships between tables and map columns to their parent column in another table. A foreign key, for example, must be mapped correctly and must be available in its source table; </span><span class="NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW18591511 BCX8">otherwise</span><span class="NormalTextRun SCXW18591511 BCX8"> the generated data will lack referential integrity. Defining these mappings during data model configuration keeps related tables consistent.</span></span><span class="EOP Selected SCXW18591511 BCX8"> </span></p></div>
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<div id="faq-5" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="5" aria-controls="elementor-tab-content-1535"><h3 class="eael-accordion-tab-title">How fast is synthetic data generation with Datagaps TDM? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1535" class="eael-accordion-content clearfix" data-tab="5" aria-labelledby="faq-5"><p><span class="TextRun SCXW45868271 BCX8"><span class="NormalTextRun SCXW45868271 BCX8">Generation typically takes a few seconds to a few minutes, depending on the configured volume. For 100 or 1,000 rows it completes in approximately five seconds, and multiple tables are processed in parallel within the same </span><span class="NormalTextRun SCXW45868271 BCX8">time frame</span><span class="NormalTextRun SCXW45868271 BCX8">. The full workflow reduces what could take weeks of manual effort to under an hour.</span></span><span class="EOP Selected SCXW45868271 BCX8"> </span></p></div>
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<p>The post <a href="https://www.datagaps.com/blog/synthetic-test-data-generation-microsoft-fabric/">How to Generate Synthetic Test Data for Microsoft Fabric with TDM</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></content:encoded>
</item>
<item>
<title>How to Generate Data Quality Rules for Microsoft Fabric with AI</title>
<link>https://www.datagaps.com/blog/ai-data-quality-rules-microsoft-fabric/</link>
<dc:creator><![CDATA[Raj Mohan Achanta]]></dc:creator>
<pubDate>Wed, 21 Oct 2026 09:09:26 +0000</pubDate>
<category><![CDATA[Data Quality]]></category>
<category><![CDATA[Data Validation]]></category>
<category><![CDATA[DataOps]]></category>
<category><![CDATA[AI Rule Generation]]></category>
<category><![CDATA[dataops]]></category>
<category><![CDATA[Microsoft Fabric]]></category>
<guid isPermaLink="false">https://www.datagaps.com/?p=63199</guid>
<description><![CDATA[<p>Generate data quality rules for Microsoft Fabric using AI. Learn how AI simplifies rule creation, improves data accuracy, and strengthens data governance.</p>
<p>The post <a href="https://www.datagaps.com/blog/ai-data-quality-rules-microsoft-fabric/">How to Generate Data Quality Rules for Microsoft Fabric with AI</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></description>
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<p><span class="TextRun SCXW26841463 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW26841463 BCX8">AI-assisted data quality rule generation in </span><span class="NormalTextRun SpellingErrorV2Themed SCXW26841463 BCX8">Datagaps</span><span class="NormalTextRun SCXW26841463 BCX8"> is a prompt-driven workflow that creates validation rules, including rule names, descriptions, quality dimensions and SQL logic, for selected Microsoft Fabric tables and columns, then executes them to produce column-level DQ scores, pass/fail results and sample bad records.</span></span><span class="EOP Selected SCXW26841463 BCX8" data-ccp-props="{}"> </span></p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li><span data-contrast="auto">Successful Fabric pipelines do not guarantee output trustworthy enough for reporting, operations and AI use cases.</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Defining checks manually across hundreds of tables and thousands of columns is hard to standardize and sustain.</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Datagaps generates rules from selected columns plus a generic prompt and a business-specific domain prompt.</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Each AI-generated rule includes SQL logic and dimension mapping and should be reviewed before execution.</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Results show column-wise DQ scores, rule pass/fail outcomes, sample bad records and an overall run summary.</span><span data-ccp-props="{}"> </span></li></ul> </div>
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<p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/data-ops-suite-trial-request/" target="_blank" rel="noopener"><span class="TextRun SCXW176770195 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW176770195 BCX8">See how </span><span class="NormalTextRun SpellingErrorV2Themed SCXW176770195 BCX8">Datagaps</span><span class="NormalTextRun SCXW176770195 BCX8"> generates data quality rules for your Fabric tables →</span></span></a></span></p> </div>
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<p><span class="TextRun SCXW195147135 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW195147135 BCX8">To generate <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-quality-monitoring-tools/" target="_blank" rel="noopener">data quality rules</a></span> for Microsoft Fabric at scale, connect Fabric as a data source in </span><span class="NormalTextRun SpellingErrorV2Themed SCXW195147135 BCX8">Datagaps</span><span class="NormalTextRun SCXW195147135 BCX8">, create a data model over the tables you want to validate, and use the Data Rules Wizard’s AI-assisted mode to generate rules from your selected columns and prompts. You then review the rules, run </span><span class="NormalTextRun SCXW195147135 BCX8">validations</span><span class="NormalTextRun SCXW195147135 BCX8"> and use the DQ score and bad-record samples to prioritize remediation.</span></span><span class="EOP Selected SCXW195147135 BCX8" data-ccp-props="{}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Why does data quality matter once Microsoft Fabric pipelines are running? </h2> </div>
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<p><span class="TextRun SCXW184782545 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW184782545 BCX8">As organizations adopt <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/microsoft-fabric-testing-automation/" target="_blank" rel="noopener">Microsoft Fabric</a></span> to modernize analytics and accelerate AI-driven use cases, the focus quickly shifts from pipeline execution to outcome reliability. Pipelines may run successfully, but the real question is whether the output is trustworthy enough for reporting, operations, and AI experiences.</span></span><span class="EOP Selected SCXW184782545 BCX8" data-ccp-props="{}"> </span></p> </div>
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<p><span class="TextRun SCXW102124142 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW102124142 BCX8">This becomes even more important when Fabric data powers automated dashboards and natural language use cases such as text to SQL. In these scenarios, quality issues can silently propagate and impact decisions at scale.</span></span><span class="EOP Selected SCXW102124142 BCX8" data-ccp-props="{}"> </span></p> </div>
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<p><span class="NormalTextRun SpellingErrorV2Themed SCXW249505822 BCX8">Datagaps</span><span class="NormalTextRun SCXW249505822 BCX8"> helps address this by providing a structured workflow for data quality and validation, including AI-assisted rule generation to accelerate coverage and improve consistency.</span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Why is data quality rule generation difficult in Fabric environments? </h2> </div>
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<p><span class="TextRun SCXW35418886 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW35418886 BCX8">Rule generation is difficult because enterprise data estates often include hundreds of tables and thousands of columns, each with business meaning and validation expectations. Defining and </span><span class="NormalTextRun SCXW35418886 BCX8">maintaining</span><span class="NormalTextRun SCXW35418886 BCX8"> tests manually across that footprint is time-consuming, difficult to standardize, and hard to sustain as schemas evolve.</span></span><span class="EOP Selected SCXW35418886 BCX8" data-ccp-props="{}"> </span></p> </div>
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<p><span class="TextRun SCXW225488128 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW225488128 BCX8">Many projects follow layered architectures across environments like DEV, STG, and PROD. Data changes at each layer due to ingestion and transformations. To </span><span class="NormalTextRun SCXW225488128 BCX8">maintain</span><span class="NormalTextRun SCXW225488128 BCX8"> trust, teams typically need validation at table level and column level, and sometimes across layers.</span></span><span class="EOP Selected SCXW225488128 BCX8" data-ccp-props="{}"> </span></p> </div>
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<p class="elementor-heading-title elementor-size-default">A typical gold layer might include dozens of tables with dozens of columns per table. Common checks include:</p> </div>
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<ul><li><span data-contrast="auto">Numeric constraints such as revenue greater than zero</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Format checks such as valid email patterns</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Null checks for required attributes</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Uniqueness checks to prevent duplicate keys</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Cross-table consistency checks to ensure reconciliation across datasets</span><span data-ccp-props="{}"> </span></li></ul><p><span data-contrast="auto">Manually implementing these checks at scale can become difficult to manage, especially when new columns appear and business logic changes.</span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">How do you generate and validate data quality rules for Microsoft Fabric in Datagaps? </h2> </div>
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<p><span class="TextRun SCXW262476204 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW262476204 BCX8">The end-to-end workflow has ten steps: connect Fabric, open the <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-quality-monitoring-tools/" target="_blank" rel="noopener">Data Quality section</a></span>, build a data model, baseline the DQ score, create rules with the Data Rules Wizard, generate them with AI, scope them with prompts, review them, run validations, and interpret the results.</span></span><span class="EOP Selected SCXW262476204 BCX8" data-ccp-props="{}"> </span></p> </div>
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Step 1: Connect Microsoft Fabric as a data source </span>
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Start by adding Microsoft Fabric as a data source in Datagaps using the available connectors and the integration flow. </p>
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<img loading="lazy" decoding="async" width="1790" height="800" src="https://www.datagaps.com/wp-content/uploads/Modern-Data-Sources-Connector-Dashboard-1.png" class="attachment-full size-full wp-image-63428" alt="Modern Data Sources Connector Dashboard" srcset="https://www.datagaps.com/wp-content/uploads/Modern-Data-Sources-Connector-Dashboard-1.png 1790w, https://www.datagaps.com/wp-content/uploads/Modern-Data-Sources-Connector-Dashboard-1-300x134.png 300w, https://www.datagaps.com/wp-content/uploads/Modern-Data-Sources-Connector-Dashboard-1-1024x458.png 1024w, https://www.datagaps.com/wp-content/uploads/Modern-Data-Sources-Connector-Dashboard-1-768x343.png 768w, https://www.datagaps.com/wp-content/uploads/Modern-Data-Sources-Connector-Dashboard-1-1536x686.png 1536w" sizes="(max-width: 1790px) 100vw, 1790px" /> <figcaption class="widget-image-caption wp-caption-text">Connect Microsoft Fabric as a data source using available connectors</figcaption>
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Step 2: Move to the Data Quality section to access DQ capabilities </span>
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After the Fabric integration is set up, move to the Data Quality section in Datagaps. This area provides multiple data quality related capabilities, such as: </p>
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<ul><li><span data-contrast="auto">Viewing and generating a </span><b><span data-contrast="auto">Data Quality Score</span></b><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Exploring </span><b><span data-contrast="auto">Data Lineage</span></b><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Reviewing data quality issues and related artifacts</span><span data-ccp-props="{}"> </span></li></ul> </div>
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<p><span class="TextRun SCXW57799901 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW57799901 BCX8">This step is important because it is where you begin the quality workflow before building rules.</span></span><span class="EOP Selected SCXW57799901 BCX8" data-ccp-props="{}"> </span></p> </div>
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<img decoding="async" src="https://www.datagaps.com/wp-content/uploads/elementor/thumbs/Data-quality-sections-for-DQ-Score-rv7seeo4ugzmvk7w51gibr8mz21lhwwv158py0myb4.png" title="Data quality sections for DQ Score" alt="Data quality sections for DQ Score" loading="lazy" /> <figcaption class="widget-image-caption wp-caption-text">The Data Quality section provides access to DQ Score, Data Lineage, and other quality workflows.</figcaption>
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Step 3: Create a data model and select tables </span>
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Create a data model as the base layer on which rules are generated and executed. Select the Fabric tables you want to validate. This can scale from a single table to hundreds depending on your scope. </p>
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<img decoding="async" src="https://www.datagaps.com/wp-content/uploads/elementor/thumbs/Data-Model-to-organise-tables-for-rules-s3-rv7sherkoezci9sq3a35t77keivaiy04av1sxn8dlw.png" title="Data Model to organise tables for rules" alt="Data Model to organise tables for rules" loading="lazy" /> <figcaption class="widget-image-caption wp-caption-text">Create a data model to organize tables for rule creation and scoring.</figcaption>
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Step 4: Generate and review baseline DQ score and trends </span>
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Once the data model is set up, generate the DQ score to establish a baseline. Review trends and drilldowns to understand quality distribution across tables and dimensions. </p>
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Step 5: Create rules using the Data Rules Wizard </span>
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Datagaps provides a Data Rules Wizard that supports both manual rule definition and AI-assisted rule generation. </p>
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Step 6: Use AI-assisted rule generation to accelerate rule creation </span>
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Switch the rule type to the AI-assisted mode and define execution criteria like success conditions and severity. This enables Datagaps to generate rule suggestions based on your selected columns and the prompt you provide. </p>
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Step 7: Select tables and columns, then provide prompts </span>
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Select the table and columns to include in rule generation. Use two prompts to improve output quality: <br><br>
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Step 8: Review AI-generated rules before execution </span>
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AI-generated rules typically include rule names, descriptions, mapped quality dimensions, and SQL logic that identifies bad records according to the chosen success criteria. Review these rules before running them to ensure they align with internal standards and domain expectations. </p>
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Step 9: Configure and run validations </span>
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Before execution, configure how the run should behave, including where to store bad records, parallel execution settings, and sampling limits for captured bad data. </p>
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Step 10: Interpret results and prioritize remediation </span>
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After execution, results can be reviewed across multiple levels, including: </p>
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<ul><li><span data-contrast="auto">Column-wise DQ score based on executed rules</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Rule-level pass or fail outcomes</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Sample bad records for investigation</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Summary view across rules and columns</span><span data-ccp-props="{}"> </span></li></ul> </div>
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<p><span class="TextRun SCXW185869050 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW185869050 BCX8"><strong>Work through the results top-down:</strong> start with the high-level scoring view, then review one passing rule and one failing rule, and close with the overall summary.</span></span><span class="EOP Selected SCXW185869050 BCX8" data-ccp-props="{}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">How can teams operationalize AI-generated data quality rules in production? </h2> </div>
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<p><span data-contrast="auto">To operationalize this at enterprise scale, standardize how rules are generated, reviewed and gated:</span><span data-ccp-props="{}"> </span></p><ul><li><span data-contrast="auto">Standardize prompts for repeatable rule generation across datasets</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Maintain domain prompts in a shared repository per business domain</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Add a review workflow for AI-generated rules, especially for cross-table logic</span><span data-ccp-props="{}"> </span></li><li><span data-contrast="auto">Track DQ score trends and define thresholds for release gates</span><span data-ccp-props="{}"> </span><span data-contrast="auto">Integrate validation runs into pipeline schedules for continuous monitoring</span><span data-ccp-props="{}"> </span></li><li><span class="TextRun SCXW108358148 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW108358148 BCX8">Integrate validation runs into pipeline schedules for continuous monitoring</span></span><span class="EOP Selected SCXW108358148 BCX8" data-ccp-props="{}"> </span></li><li style="list-style-type: none;"> </li></ul> </div>
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<h2 class="elementor-heading-title elementor-size-default">Conclusion </h2> </div>
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<p><span class="TextRun SCXW181035878 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SCXW181035878 BCX8">Microsoft Fabric enables fast analytics and AI experiences, but reliable outcomes depend on trustworthy data. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW181035878 BCX8">Datagaps</span><span class="NormalTextRun SCXW181035878 BCX8"> supports Fabric data quality workflows by helping teams generate rules faster, execute validations consistently, and interpret results with actionable detail. This reduces risk, improves reliability, and helps scale data quality practices across teams and environments.</span></span><span class="EOP Selected SCXW181035878 BCX8" data-ccp-props="{}"> </span></p> </div>
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<p><span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/request-a-demo/" target="_blank" rel="noopener"><span class="TextRun SCXW265958618 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW265958618 BCX8">See how </span><span class="NormalTextRun SpellingErrorV2Themed SCXW265958618 BCX8">Datagaps</span><span class="NormalTextRun SCXW265958618 BCX8"> AI rule generation scores your Fabric tables → </span></span><span class="EOP Selected SCXW265958618 BCX8" data-ccp-props="{}"> </span></a></span></span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Frequently Asked Questions </h2> </div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="1" aria-controls="elementor-tab-content-1531"><h3 class="eael-accordion-tab-title">How does Datagaps generate data quality rules for Microsoft Fabric tables? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1531" class="eael-accordion-content clearfix" data-tab="1" aria-labelledby="faq-1"><p><span class="TextRun SCXW197349388 BCX8"><span class="NormalTextRun SpellingErrorV2Themed SCXW197349388 BCX8">Datagaps</span><span class="NormalTextRun SCXW197349388 BCX8"> generates rules through the AI-assisted mode of its Data Rules Wizard. You select the Fabric table and columns, define success criteria and severity, and provide a generic prompt plus a domain prompt. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW197349388 BCX8">Datagaps</span><span class="NormalTextRun SCXW197349388 BCX8"> then returns rule suggestions with rule names, descriptions, mapped quality </span><span class="NormalTextRun SCXW197349388 BCX8">dimensions</span><span class="NormalTextRun SCXW197349388 BCX8"> and SQL logic that </span><span class="NormalTextRun SCXW197349388 BCX8">identifies</span><span class="NormalTextRun SCXW197349388 BCX8"> bad records.</span></span><span class="EOP Selected SCXW197349388 BCX8"> </span></p></div>
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<div id="faq-2" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="2" aria-controls="elementor-tab-content-1532"><h3 class="eael-accordion-tab-title">What data quality checks are typical for a Microsoft Fabric gold layer?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1532" class="eael-accordion-content clearfix" data-tab="2" aria-labelledby="faq-2"><p><span class="TextRun SCXW99668558 BCX8"><span class="NormalTextRun SCXW99668558 BCX8">Common checks include numeric constraints such as revenue greater than zero, format checks such as valid email patterns, null checks for required attributes, uniqueness checks to prevent duplicate keys, and cross-table consistency checks that reconcile datasets. A gold layer often holds dozens of tables with dozens of columns each, which is why manual implementation becomes hard to manage.</span></span></p></div>
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<div id="faq-3" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="3" aria-controls="elementor-tab-content-1533"><h3 class="eael-accordion-tab-title">Should AI-generated data quality rules be reviewed before they run?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1533" class="eael-accordion-content clearfix" data-tab="3" aria-labelledby="faq-3"><p><span class="TextRun SCXW47403614 BCX8"><span class="NormalTextRun SCXW47403614 BCX8">Yes. AI-generated rules should be reviewed before execution to confirm they align with internal standards and domain expectations. Review covers the rule name, description, mapped </span><span class="NormalTextRun SCXW47403614 BCX8">dimension</span><span class="NormalTextRun SCXW47403614 BCX8"> and SQL logic. For production adoption, add a formal review workflow, especially for cross-table logic, and keep domain prompts in a shared repository per business domain.</span></span><span class="EOP Selected SCXW47403614 BCX8"> </span></p></div>
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<div id="faq-4" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="4" aria-controls="elementor-tab-content-1534"><h3 class="eael-accordion-tab-title">What can you configure before running data quality validations on Fabric data? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1534" class="eael-accordion-content clearfix" data-tab="4" aria-labelledby="faq-4"><p><span class="TextRun SCXW15312659 BCX8"><span class="NormalTextRun SCXW15312659 BCX8">Before execution, you configure how the run behaves: where bad records should be stored, parallel execution settings, and sampling limits for the bad data that is captured. These settings are applied to the rules managed centrally within the data model, so validations run consistently each time they are triggered.</span></span></p></div>
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<div id="faq-5" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="5" aria-controls="elementor-tab-content-1535"><h3 class="eael-accordion-tab-title">How do you interpret Datagaps data quality results for Microsoft Fabric? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-1535" class="eael-accordion-content clearfix" data-tab="5" aria-labelledby="faq-5"><p><span class="NormalTextRun SCXW220250160 BCX8">Results are reviewed at several levels: a column-wise DQ score based on executed rules, rule-level pass or fail outcomes, sample bad records for investigation, and a summary across rules, </span><span class="NormalTextRun SCXW220250160 BCX8">columns</span><span class="NormalTextRun SCXW220250160 BCX8"> and dimensions. Start with the high-level scoring view, then examine a passing and a failing rule, and finish with the overall run summary.</span></p></div>
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</div>
<div class="elementor-icon-box-content">
<h5 class="elementor-icon-box-title">
<a href="https://www.linkedin.com/in/raj-mohan-achanta-41454b1a2/" >
RajMohan Achanta </a>
</h5>
<p class="elementor-icon-box-description">
Associate Product Manager, Datagaps </p>
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</div>
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<div class="elementor-widget-container">
<p>Associate Product Manager at Datagaps. Shapes the product experience across ETL Validator, BI Validator, and Data Quality Monitor.</p> </div>
</div>
</div>
</div>
<div class="elementor-element elementor-element-a7960cb e-con-full e-flex e-con e-child" data-id="a7960cb" data-element_type="container" data-e-type="container">
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<div class="elementor-widget-container">
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<i aria-hidden="true" class="fab fa-linkedin-in"></i> </a>
</div>
<div class="elementor-icon-box-content">
<h5 class="elementor-icon-box-title">
<a href="https://www.linkedin.com/in/avinashkeshri-iimb/" >
Avinash Keshri </a>
</h5>
<p class="elementor-icon-box-description">
Head, Product Marketing </p>
</div>
</div>
</div>
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<p>Head of Product Marketing at Datagaps and IIM Bangalore alumnus. 13+ years of experience in commercializing AI and data platforms across global markets.</p> </div>
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</div>
</div>
<p>The post <a href="https://www.datagaps.com/blog/ai-data-quality-rules-microsoft-fabric/">How to Generate Data Quality Rules for Microsoft Fabric with AI</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></content:encoded>
</item>
<item>
<title>Datagaps vs iCEDQ: An Honest Guide to End-to-End Data Quality & Validation</title>
<link>https://www.datagaps.com/blog/datagaps-vs-icedq/</link>
<comments>https://www.datagaps.com/blog/datagaps-vs-icedq/#respond</comments>
<dc:creator><![CDATA[Raj Mohan Achanta]]></dc:creator>
<pubDate>Fri, 16 Oct 2026 06:22:18 +0000</pubDate>
<category><![CDATA[BI Testing]]></category>
<category><![CDATA[Data Validation]]></category>
<guid isPermaLink="false">https://www.datagaps.com/?p=62921</guid>
<description><![CDATA[<p>Datagaps vs iCEDQ: An Overview Datagaps vs iCEDQ: Both platforms automate data validation at full volume, but iCEDQ stops at the data behind reports. Datagaps DataOps Suite also scores data quality, generates synthetic test data, and tests BI dashboards as users see them. It is named in two Gartner Market Guides. The Verdict at a […]</p>
<p>The post <a href="https://www.datagaps.com/blog/datagaps-vs-icedq/">Datagaps vs iCEDQ: An Honest Guide to End-to-End Data Quality & Validation</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></description>
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<span style="color: #ffffff;font-family: Manrope, sans-serif;font-size: 44px;font-weight: 700">
Datagaps vs iCEDQ: An Overview
</span>
</h2></h2> </div>
</div>
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<div class="elementor-widget-container">
<p><span style="color: #1f9d55;"><strong>Datagaps vs iCEDQ: </strong></span>Both platforms automate data validation at full volume, but iCEDQ stops at the data behind reports. Datagaps DataOps Suite also scores data quality, generates synthetic test data, and tests BI dashboards as users see them. It is named in two Gartner Market Guides.</p> </div>
</div>
</div>
</div>
</div>
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<div class="e-con-inner">
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<div class="elementor-widget-container">
<h2 class="elementor-heading-title elementor-size-default">The Verdict at a Glance </h2> </div>
</div>
<div class="elementor-element elementor-element-1808360 e-con-full e-flex e-con e-child" data-id="1808360" data-element_type="container" data-e-type="container">
<div class="elementor-element elementor-element-5eb3414 e-grid e-con-full e-con e-child" data-id="5eb3414" data-element_type="container" data-e-type="container">
<div class="elementor-element elementor-element-472aec0 e-con-full bi-testing-report e-flex e-con e-child" data-id="472aec0" data-element_type="container" data-e-type="container" data-settings="{"background_background":"classic"}">
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<div class="elementor-widget-container">
<p>Both reconcile source and target at full volume; Datagaps carries validation into data quality and BI.</p> </div>
</div>
</div>
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<div class="elementor-widget-container">
<p>Across BI reports, Datagaps tests reports as users see them: visuals, filters, security, load and pixel-level regression.</p> </div>
</div>
</div>
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<p>Datagaps turns data quality into a score with drill-down; iCEDQ runs quality rules without a scorecard.</p> </div>
</div>
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<p>Datagaps generates complete validation workflows, for data and for BI, across hundreds of tables and reports at once.</p> </div>
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<p>Datagaps is named in the Gartner Market Guides for DataOps Tools and Data Observability Tools, with more recent reviews.</p> </div>
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<p>This guide compares <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/dataops-suite/" target="_blank" rel="noopener">Datagaps DataOps Suite</a></span></span> and iCEDQ for teams choosing a platform for data validation, data quality, and BI testing. It starts with how the category is changing and what analysts now expect from it. It then compares the two platforms feature by feature, checks iCEDQ’s own claims, and closes with three questions to take into any evaluation.</p><p><a href="https://www.datagaps.com/request-a-demo/" target="_blank" rel="noopener"><span style="text-decoration: underline; color: #1967d2;">Book a walkthrough with your own data →</span></a></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">What Is Changing in How Enterprises Validate Data?</h2> </div>
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<p>Data validation started with one question: did the target receive what the source sent? Enterprises still ask it. Four shifts have widened what they expect a platform to do beyond that.</p> </div>
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<div class="elementor-image-box-wrapper"><div class="elementor-image-box-content"><h3 class="elementor-image-box-title">Validation is moving to the dashboard</h3><p class="elementor-image-box-description">The dashboard is where the business acts on data, and it is the least tested layer. Power BI and Microsoft Fabric have no native testing feature, as SQLBI has pointed out. Of 95 enterprise data teams Datagaps worked with, 73% needed BI report validation alongside ETL reconciliation.</p></div></div> </div>
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<div class="elementor-image-box-wrapper"><div class="elementor-image-box-content"><h3 class="elementor-image-box-title">Buyers want fewer tools</h3><p class="elementor-image-box-description">Gartner's 2026 Market Guide for Data Observability Tools describes a market moving toward unified platforms that combine observability, quality, and governance. Buyers put it in their own terms: test cases sit in one tool, runs in another, and tracking in a third. They want one platform instead.</p></div></div> </div>
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<div class="elementor-image-box-wrapper"><div class="elementor-image-box-content"><h3 class="elementor-image-box-title">AI is taking on whole tasks</h3><p class="elementor-image-box-description">Gartner's 2026 research on augmented data quality points to agentic automation across the quality lifecycle. Buyers now ask how much of the validation work agents can generate, run, and explain, with a person approving each change.</p></div></div> </div>
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<div class="elementor-image-box-wrapper"><div class="elementor-image-box-content"><h3 class="elementor-image-box-title">Migrations put every layer under test at once</h3><p class="elementor-image-box-description">A move to Snowflake, Databricks, or Fabric changes pipelines, data quality, and reports in one go, turning hundreds of tables and reports into hundreds of tests. Forty percent of the teams Datagaps worked with needed migration validation, yet 65% were still validating data by hand.</p></div></div> </div>
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<p>These four shifts are the yardstick for the comparison that follows.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">What Should a Data Validation and Data Quality Platform Cover Today?</h2> </div>
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<p>Analyst definitions have caught up with these shifts. Gartner’s DataOps Tools research defines test automation as business-rule validation, test-script management, regression testing, and test data management. Test data sits inside the definition, not beside it.</p> </div>
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<p>On data quality, Gartner’s Augmented Data Quality research expects profiling, rule management, rules written from plain language, and a metrics view built on critical data elements, used by business and BI teams as well as engineers. Gartner expects 70% of organizations to adopt modern data quality solutions by 2027 (Gartner, cited by Informatica, 2025).</p> </div>
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<p>Put the two together with the shift toward the dashboard and the requirement becomes four jobs. A platform has to reconcile pipelines, measure quality, supply safe test data, and validate what the business sees. Datagaps DataOps Suite is built around exactly those four jobs, one component for each, with AI agents working across all of them.</p> </div>
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<th style="text-align:left;">The job</th>
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<td style="text-align:left;">Reconcile pipelines</td>
<td style="text-align:left;">Full-volume source-to-target and transformation testing, regression packs, CI/CD</td>
<td style="text-align:left;">ETL Validator</td>
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<td style="text-align:left;">Measure quality</td>
<td style="text-align:left;">Profiling, business rules, quality scores by dimension, anomaly detection</td>
<td style="text-align:left;">Data Quality Monitor</td>
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<td style="text-align:left;">Supply test data</td>
<td style="text-align:left;">Privacy-safe synthetic data that behaves like production</td>
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<td style="text-align:left;">Validate the dashboard</td>
<td style="text-align:left;">Report data, visuals, filters, performance, and BI governance</td>
<td style="text-align:left;">BI Validator</td>
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<td style="text-align:left;">Automate across all four</td>
<td style="text-align:left;">Agentic AI that generates, runs, and explains tests, with human approval</td>
<td style="text-align:left;">AI/ML agents and MCP server</td>
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<p><span data-teams="true">The components share one connector layer and one audit trail, so a single validation flow can start at a source table, apply data quality rules along the way, and end at a dashboard visual. Before comparing features, it is worth seeing how independent analysts have assessed that scope.</span></p> </div>
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<p><strong>What this means for buyers: </strong>check all four jobs, not only the first. A platform that misses one leaves manual work where errors cost the most.</p> </div>
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<p>Gartner publishes two Market Guides that map directly onto this evaluation. Each defines a market, sets out the capabilities buyers should expect, and names Representative Vendors based on analyst assessment of market presence and functional coverage. Vendors cannot nominate themselves, so a listing is an outside view of what a product actually does.</p> </div>
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<p>The Market Guide for DataOps Tools covers pipeline orchestration, observability, environment management, deployment automation, and test automation. The Market Guide for Data Observability Tools covers how platforms monitor data content, pipelines, and infrastructure. One is about testing data as it moves; the other is about watching its quality continuously.</p> </div>
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<th style="text-align:left;">Datagaps</th>
<th style="text-align:left;">iCEDQ</th>
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<td style="text-align:left;">Gartner Market Guide for DataOps Tools (Oct 2025)</td>
<td style="text-align:left;">✓ Representative Vendor, categorised as a specialist in data pipeline test automation</td>
<td style="text-align:left;">Cites a DataOps guide mention on its own site; edition not named</td>
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<td style="text-align:left;">Gartner Market Guide for Data Observability Tools (Feb 2026)</td>
<td style="text-align:left;">✓ Representative Vendor for Data Quality Monitor: data content observability, pipeline observability, and lineage</td>
<td style="text-align:left;">No listing found in public sources</td>
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<td style="text-align:left;">Gartner Peer Insights</td>
<td style="text-align:left;">4.6 from 8 ratings; newest June 2026</td>
<td style="text-align:left;">4.0 from 1 rating; April 2024</td>
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<p>In the DataOps guide, Gartner points to enterprise deployments of Datagaps in financial services, healthcare, technology, and higher education. In the Data Observability guide, Data Quality Monitor is recognised for validating data where it resides rather than moving it.</p> </div>
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<p>Being named in both matters because they are separate markets with separate criteria. Analysts recognised Datagaps both for testing pipelines and for monitoring data quality continuously, which is the same scope buyers now expect from one platform. Check the current vendor lists yourself: recognition is the one claim no vendor should ask you to accept on trust.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">How Do Datagaps and iCEDQ Compare Feature by Feature?</h2> </div>
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<p>This is where iCEDQ is measured against the four jobs. Rows marked Neutral are areas where both platforms deliver; the rest show where they part ways.</p> </div>
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<th style="text-align:left;">Capability</th>
<th style="text-align:left;">Datagaps</th>
<th style="text-align:left;">iCEDQ</th>
<th style="text-align:left;">Advantage</th>
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<td style="text-align:left;">Source-to-target reconciliation</td>
<td style="text-align:left;">100% of records on a Spark engine</td>
<td style="text-align:left;">100% of records; in-memory engine plus Spark</td>
<td style="text-align:left;">Neutral</td>
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<td style="text-align:left;">Many sources and transformations in one test</td>
<td style="text-align:left;">One visual dataflow chains sources, transformations, comparisons and actions; SQL, Python, Scala</td>
<td style="text-align:left;">Rule-based comparisons, including multi-source; Groovy or Java for complex logic</td>
<td class="win" style="text-align:left;">Datagaps</td>
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<td style="text-align:left;">Bulk test generation</td>
<td style="text-align:left;">Hundreds of complete dataflows and BI tests at once</td>
<td style="text-align:left;">Rules created in bulk or by AI from mapping documents; no bulk BI tests</td>
<td class="win" style="text-align:left;">Datagaps</td>
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<td style="text-align:left;">BI report data validation</td>
<td style="text-align:left;">Report vs database, report vs report</td>
<td style="text-align:left;">Report data via BI APIs, including Power BI semantic models</td>
<td style="text-align:left;">Neutral</td>
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<tr>
<td style="text-align:left;">BI visual regression</td>
<td style="text-align:left;">Pixel-to-pixel and textual comparison; side-by-side PDFs</td>
<td style="text-align:left;">None; bypasses the visual layer</td>
<td class="win" style="text-align:left;">Datagaps</td>
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<td style="text-align:left;">BI functional testing</td>
<td style="text-align:left;">Filters, slicers, parameters, bookmarks, row-level security</td>
<td style="text-align:left;">Filters and parameters applied to extract data</td>
<td class="win" style="text-align:left;">Datagaps</td>
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<tr>
<td style="text-align:left;">BI performance and stress testing</td>
<td style="text-align:left;">Concurrent-user load testing</td>
<td style="text-align:left;">None</td>
<td class="win" style="text-align:left;">Datagaps</td>
</tr>
<tr>
<td style="text-align:left;">BI governance</td>
<td style="text-align:left;">Finds unused measures, duplicate reports and orphaned tables; compares KPIs across platforms</td>
<td style="text-align:left;">None</td>
<td class="win" style="text-align:left;">Datagaps</td>
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<tr>
<td style="text-align:left;">Data quality rules and monitoring</td>
<td style="text-align:left;">Rules, profiling, monitoring</td>
<td style="text-align:left;">Rules, profiling, monitoring</td>
<td style="text-align:left;">Neutral</td>
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<td style="text-align:left;">Data quality scoring</td>
<td style="text-align:left;">Scores by dimension; scorecard with drill-down to column</td>
<td style="text-align:left;">No quality score or scorecard; a few pre-packaged dashboards</td>
<td class="win" style="text-align:left;">Datagaps</td>
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<td style="text-align:left;">Observability</td>
<td style="text-align:left;">ML anomaly detection, profiling, lineage</td>
<td style="text-align:left;">ML and rule-based anomaly detection, auto-profiling</td>
<td style="text-align:left;">Neutral</td>
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<td style="text-align:left;">Test data management</td>
<td style="text-align:left;">Synthetic data; PII and PHI masking</td>
<td style="text-align:left;">None</td>
<td class="win" style="text-align:left;">Datagaps</td>
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<td style="text-align:left;">AI and agents</td>
<td style="text-align:left;">AI agents across ETL, data quality, BI and test data, plus an MCP server</td>
<td style="text-align:left;">AI rule generation and an MCP server for data rules, workflows and profiling</td>
<td class="win" style="text-align:left;">Datagaps</td>
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<td style="text-align:left;">CI/CD and deployment</td>
<td style="text-align:left;">REST API, CLI, Azure DevOps, Jenkins; on-premises or your cloud, on Kubernetes, Databricks or EMR</td>
<td style="text-align:left;">REST API, CLI, Jenkins, Azure Pipelines; SaaS on AWS or on-premises</td>
<td style="text-align:left;">Neutral</td>
</tr>
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<p><em>iCEDQ entries reflect its public product documentation as of October 2026.</em></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">How Do iCEDQ's Public Claims Hold Up?</h2> </div>
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<p>iCEDQ markets itself as the only integrated platform for data testing, monitoring, and observability, covering the entire data development lifecycle. We checked its main claims against its own documentation and independent reviews.</p> </div>
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<th>#</th>
<th>iCEDQ claim </th>
<th>What the evidence shows</th>
<th>Verdict</th>
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<td>1</td>
<td>The only integrated testing, monitoring and observability platform</td>
<td>One rule engine runs from QA into production, so the integration is real. It is not the only one: Datagaps DataOps Suite also combines testing, monitoring and observability, and adds test data management and dashboard testing.</td>
<td>Integrated, not complete</td>
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<td>2</td>
<td>Covers the entire data lifecycle</td>
<td>It spans development, QA and production, but coverage ends at report data. iCEDQ’s own BI page says it works from the data behind reports, not what the dashboard displays.</td>
<td>Overstated</td>
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<td>3</td>
<td>BI and report testing</td>
<td>Validates report numbers across major BI tools, including Power BI semantic models. No visual or load testing.</td>
<td>Data only</td>
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<td>4</td>
<td>Full-volume validation, no sampling</td>
<td>True. Datagaps validates 100% of records as well.</td>
<td>True, not unique</td>
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<td>5</td>
<td>Data reliability</td>
<td>Writes at length about data quality dimensions. Its product offers rules and observability metrics, but no data quality scorecard.</td>
<td>No quality scorecard</td>
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<p class="MsoNormal" style="margin-bottom: 7.0pt;">iCEDQ’s claims hold at the data layer. Its “only” and “entire lifecycle” claims break where the business looks: the dashboard.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Where Does Datagaps Go Further Than iCEDQ?</h2> </div>
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<p>Seven capabilities separate the two platforms. None of them can be switched on with a configuration change.</p> </div>
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<div class="elementor-image-box-wrapper"><div class="elementor-image-box-content"><h3 class="elementor-image-box-title">1. Pixel-to-Pixel Visual Regression</h3><p class="elementor-image-box-description">A report can hold correct data and still be wrong: a series disappears after an upgrade, a label truncates, a chart reorders. BI Validator compares report renders pixel by pixel and as text across versions and environments, and produces side-by-side PDFs for sign-off. iCEDQ pulls report data through APIs and skips the visual layer, so these defects go to manual review.</p></div></div> </div>
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<div class="elementor-image-box-wrapper"><div class="elementor-image-box-content"><h3 class="elementor-image-box-title">2. Functional and Load Testing of Dashboards</h3><p class="elementor-image-box-description">BI Validator applies filters, slicers, parameters, and bookmarks, tests row-level security, and simulates concurrent users to measure load times and find bottlenecks before rollout. iCEDQ has no functional or load testing for dashboards.<br><br>
<a href="https://www.datagaps.com/bi-testing-tools/" style="color: #1967d2;text-decoration: underline;font-weight: 400">See how BI Validator load-tests a dashboard before rollout →</a></p></div></div> </div>
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<div class="elementor-image-box-wrapper"><div class="elementor-image-box-content"><h3 class="elementor-image-box-title">3. Hundreds of Workflows Generated at Once</h3><p class="elementor-image-box-description">Datagaps wizards generate tests for hundreds of tables at once and cut configuration time by up to 70%. Mapping Manager turns a mapping document into a test suite, and Generate DB Flows builds one from schema metadata. Each output is a complete dataflow with sources, transformations, comparisons, and actions, and BI Validator does the same for report regression. iCEDQ generates rules, in bulk or with AI; it does not generate BI tests.</p></div></div> </div>
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<div class="elementor-image-box-wrapper"><div class="elementor-image-box-content"><h3 class="elementor-image-box-title">4. Many Sources and Real Logic in One Test</h3><p class="elementor-image-box-description">One Datagaps dataflow can join S3 files, Oracle, Snowflake, and an API, transform the result in SQL, Python, or Scala, and reconcile it against a target on a drag-and-drop canvas. Mismatch insights point to the root cause. iCEDQ builds tests as rules, and complex logic goes into Groovy or Java, a learning curve its own reviewers mention.</p></div></div> </div>
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<div class="elementor-image-box-wrapper"><div class="elementor-image-box-content"><h3 class="elementor-image-box-title">5. Data Quality You Can Measure</h3><p class="elementor-image-box-description"><a href="https://www.datagaps.com/data-quality-monitoring-tools/" style="color: #1967d2;text-decoration: underline;font-weight: 400">Data Quality Monitor</a> scores data at rest and in motion for accuracy, consistency, completeness, unicity, and conformity. Scores roll up into an enterprise scorecard with drill-down to column level. Alerts fire on threshold breaches, and failures can open Jira or ServiceNow tickets. iCEDQ writes about quality dimensions at length but gives users no scorecard to measure them.</p></div></div> </div>
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<div class="elementor-image-box-wrapper"><div class="elementor-image-box-content"><h3 class="elementor-image-box-title">6. Test Data Management</h3><p class="elementor-image-box-description">Gartner's definition of DataOps test automation includes test data management. <a href="https://www.datagaps.com/test-data-manager/" style="color: #1967d2;text-decoration: underline;font-weight: 400">Test Data Manager</a> generates synthetic data that keeps production patterns and relationships, masks PII and PHI for GDPR and HIPAA, and produces millions of rows in minutes. iCEDQ has no test data capability.</p></div></div> </div>
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<div class="elementor-image-box-wrapper"><div class="elementor-image-box-content"><h3 class="elementor-image-box-title">7. AI Across the Whole Stack</h3><p class="elementor-image-box-description">Both vendors offer an MCP server; iCEDQ's is built around rules, workflows and profiling. Datagaps has 15 AI and ML capabilities, its MCP server among them, spanning ETL, data quality, BI, and test data. Agents write tests from mapping documents, recommend DQ rules, build BI tests from plain English, compare KPIs and visuals across BI platforms, convert DAX to SQL, and flag unused measures and duplicate reports. Every change waits for human approval.</p></div></div> </div>
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<h2 class="elementor-heading-title elementor-size-default">Where Do Testing, Data Quality, and BI Validation Come Together?</h2> </div>
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<p><span data-teams="true">These differences matter most when they appear together, and a migration is where they do. A move to Snowflake, Databricks, or Fabric, one of the four shifts described earlier, puts pipelines, data quality, and dashboards under test at once.</span></p> </div>
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<p>A migration is complete only when the data has moved, its quality holds on the new platform, and the reports still show the same numbers. iCEDQ covers the data and its rules. Datagaps covers all three from one platform: tests generated in bulk for the move, quality reconciled across systems and scored on the target, and reports compared down to the visual, with DAX-to-SQL validation for BI migrations.</p> </div>
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<p><span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/dataops-suite/" target="_blank" rel="noopener">See how DataOps Suite tests, measures and validates a migration in one platform →</a></span></span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">What Do Teams Moving Off iCEDQ Report?</h2> </div>
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<p>The same issues come up across recent evaluations with iCEDQ users. These are anonymized patterns, set against what Datagaps provides.</p> </div>
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<th>What teams report</th>
<th>What Datagaps provides</th>
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<td>Hundreds of long, complex SQL rules that are hard to read and maintain</td>
<td>Visual dataflows, generated in bulk from mapping documents or schema metadata</td>
</tr>
<tr>
<td>Failure alerts without detail, so teams build their own dashboards on exported error records</td>
<td>Built-in DQ dashboards, row-level exception outputs, and Jira or ServiceNow tickets with context</td>
</tr>
<tr>
<td>Test lifecycle in one tool, execution in iCEDQ, tracking in a third</td>
<td>One platform for test case lifecycle and execution, connected to Jira and Azure Test Plans</td>
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<tr>
<td>Long runs, and detail lost when logic is pushed into the warehouse</td>
<td>Spark horizontal scaling; row-level mismatch output under pushdown</td>
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<tr>
<td>Comparison insights that make root cause easier to find</td>
<td>Comparison insights and AI-guided root-cause analysis</td>
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<h3 class="elementor-heading-title elementor-size-default">What Do Independent Reviews Say?</h3> </div>
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<p><span data-teams="true">On the two platforms where both vendors are reviewed, Datagaps has the more recent feedback. As of October 2026, Datagaps has ten G2 reviews, the newest from August 2026, and a 4.6 rating from eight Gartner Peer Insights reviewers, the newest from June 2026. iCEDQ’s newest G2 review is from August 2024, and it has a single Gartner Peer Insights rating, from April 2024.</span></p> </div>
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<p><span data-teams="true">Datagaps reviewers point to the BI layer. One G2 reviewer uses BI Validator for Tableau regression and stress testing, which Tableau does not offer natively, and a Gartner reviewer notes that it catches changes in both data and visuals. A recent industry guide also highlights Datagaps for multi-source migration QA (Testriq, 2026).</span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Is Your Data Tested, Measured, and Governed, from Source to Screen?</h2> </div>
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<p>Choosing a platform now means answering three questions, not one. Each comes with signs that a team has outgrown data-level validation.</p> </div>
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<th style="text-align:left;">Question</th>
<th style="text-align:left;">Signs you need it</th>
<th style="text-align:left;">How Datagaps answers it</th>
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<td style="text-align:left;">1. Is the data right?<br>(Test it)</td>
<td style="text-align:left;">Hundreds of rules that are hard to maintain; migrations of hundreds of tables; test data copied from production</td>
<td style="text-align:left;">Full-volume reconciliation; complete dataflows generated in bulk; many sources in one visual test; synthetic test data generation with masking of sensitive fields</td>
</tr>
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<td style="text-align:left;">2. How good is the data?<br>(Measure it)</td>
<td style="text-align:left;">Leadership asks for a data quality number and nobody has one; alerts arrive without context</td>
<td style="text-align:left;">Quality scores by dimension; enterprise scorecard down to column level; ML anomaly detection; tickets with context</td>
</tr>
<tr>
<td style="text-align:left;">3. Can the business trust what it sees?<br>(Govern it)</td>
<td style="text-align:left;">Dashboards checked by eye after every release; duplicate reports and measures piling up; a BI migration under way</td>
<td style="text-align:left;">Pixel-to-pixel regression; performance and security validation; KPI, visual, filter and metadata comparison; unused measures, duplicate reports and orphaned tables found; DAX-to-SQL validation</td>
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<p>Pipeline reconciliation answers the first question, and both platforms handle it. A quality score answers the second. Governing every visual, filter, permission, and KPI answers the third. Datagaps DataOps Suite answers all three, from source to screen, in one platform.</p> </div>
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<p>Check it on your own data. Teams on iCEDQ can run both platforms side by side during a 14-day trial, extendable by 14 days, and generate dataflows in bulk from existing mapping documents instead of rebuilding rules one by one.</p> </div>
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<p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/request-a-demo/" target="_blank" rel="noopener"><strong>Test, measure and govern an iCEDQ workload on your own data →</strong></a></span></p> </div>
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<p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-quality-monitoring-tools/" target="_blank" rel="noopener">Explore Data Quality Monitor →</a></span> | <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-ops-suite-trial-request/" target="_blank" rel="noopener">Start a 14-day DataOps Suite trial →</a></span> | <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"> <a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/blog/datagaps-vs-querysurge/" target="_blank" rel="noopener">Read: Datagaps vs QuerySurge →</a></span></span></p> </div>
</div>
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<span class="elementor-heading-title elementor-size-default">Get Started Today</span> </div>
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<h2 class="elementor-heading-title elementor-size-default">See Datagaps validate a iCEDQ-style workload end-to-end on your data </h2> </div>
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<p>Our team will run a live demo against your specific use case — your stack, your data problem, your pipeline.</p> </div>
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<a class="elementor-button elementor-button-link elementor-size-sm" href="https://www.datagaps.com/request-a-demo/" target="_blank">
<span class="elementor-button-content-wrapper">
<span class="elementor-button-text">Explore the full DataOps Suite</span>
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<p>SOC 2 Type II certified | ISO 27001 certified | No credit card required for trial | Your data never leaves your environment</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Frequently Asked Questions</h2> </div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="1" aria-controls="elementor-tab-content-2261"><h3 class="eael-accordion-tab-title">Is Datagaps an alternative to iCEDQ?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-2261" class="eael-accordion-content clearfix" data-tab="1" aria-labelledby="faq-1"><p>Yes. Datagaps DataOps Suite covers what iCEDQ does, including full-volume source-to-target reconciliation, data quality rules, monitoring, and anomaly detection. It adds pixel-to-pixel BI regression, dashboard load testing, data quality scoring, BI governance, and synthetic test data. Teams evaluating iCEDQ usually shortlist Datagaps when validation has to reach the dashboard.</p></div>
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<div id="faq-2" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="2" aria-controls="elementor-tab-content-2262"><h3 class="eael-accordion-tab-title">What is the main difference between Datagaps and iCEDQ?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-2262" class="eael-accordion-content clearfix" data-tab="2" aria-labelledby="faq-2"><p>Scope. Both validate data pipelines at full volume, but iCEDQ stops at the data behind reports. Datagaps also tests what users see, including visuals, filters, row-level security, and performance under load. It scores data quality, governs the BI layer, and generates synthetic test data, all in one platform.</p></div>
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<div id="faq-3" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="3" aria-controls="elementor-tab-content-2263"><h3 class="eael-accordion-tab-title">Can iCEDQ run visual regression tests on Power BI or Tableau dashboards?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-2263" class="eael-accordion-content clearfix" data-tab="3" aria-labelledby="faq-3"><p>No. iCEDQ validates report data through BI platform APIs, including Power BI semantic models, and bypasses the visual layer. Datagaps BI Validator compares report renders pixel by pixel and as text across versions and environments for Power BI, Tableau, Oracle Analytics, and other platforms, with side-by-side regression PDFs.</p></div>
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<div id="faq-4" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="4" aria-controls="elementor-tab-content-2264"><h3 class="eael-accordion-tab-title">Which platform scores data quality, Datagaps or iCEDQ?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-2264" class="eael-accordion-content clearfix" data-tab="4" aria-labelledby="faq-4"><p>Datagaps. Data Quality Monitor scores data for accuracy, consistency, completeness, unicity, and conformity, rolls the scores into an enterprise scorecard with drill-down to column level, and alerts when thresholds are breached. iCEDQ runs data quality rules and writes about quality dimensions, but has no scorecard to measure them.</p></div>
</div><div class="eael-accordion-list">
<div id="faq-5" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="5" aria-controls="elementor-tab-content-2265"><h3 class="eael-accordion-tab-title">Can Datagaps generate tests for hundreds of tables at once?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-2265" class="eael-accordion-content clearfix" data-tab="5" aria-labelledby="faq-5"><p>Yes. Datagaps wizards generate tests for hundreds of tables at once. One customer created comparison tests for 400 SQL Server tables of more than 500 million rows each in a few hours. Mapping Manager turns mapping documents into test suites, and BI Validator creates report regression tests in bulk.</p></div>
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<div id="faq-6" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="6" aria-controls="elementor-tab-content-2266"><h3 class="eael-accordion-tab-title">Can a team move from iCEDQ without rebuilding every test by hand?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-2266" class="eael-accordion-content clearfix" data-tab="6" aria-labelledby="faq-6"><p><span>Yes. Teams can run Datagaps alongside iCEDQ on the same pipelines during a 14-day trial, extendable by 14 days, and compare results. Instead of recreating rules one at a time, Datagaps generates dataflows in bulk from mapping documents or schema metadata, and connects to existing schedulers through its REST API and CLI.</span></p></div>
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<p>The post <a href="https://www.datagaps.com/blog/datagaps-vs-icedq/">Datagaps vs iCEDQ: An Honest Guide to End-to-End Data Quality & Validation</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></content:encoded>
<wfw:commentRss>https://www.datagaps.com/blog/datagaps-vs-icedq/feed/</wfw:commentRss>
<slash:comments>0</slash:comments>
</item>
<item>
<title>Datagaps vs QuerySurge: A Buyer’s Guide to Data Validation</title>
<link>https://www.datagaps.com/blog/datagaps-vs-querysurge/</link>
<comments>https://www.datagaps.com/blog/datagaps-vs-querysurge/#respond</comments>
<dc:creator><![CDATA[Raj Mohan Achanta]]></dc:creator>
<pubDate>Mon, 17 Aug 2026 17:16:27 +0000</pubDate>
<category><![CDATA[Data Validation]]></category>
<guid isPermaLink="false">https://www.datagaps.com/?p=60475</guid>
<description><![CDATA[<p>Datagaps vs QuerySurge An Honest Buyer’s Guide to Enterprise Data Validation Datagaps DataOps Suite is an end-to-end data validation and test automation platform that validates 100% of records across ETL pipelines, BI dashboards, and cloud migrations. It is the only data validation vendor recognised in both the Gartner Market Guide for DataOps Tools and the […]</p>
<p>The post <a href="https://www.datagaps.com/blog/datagaps-vs-querysurge/">Datagaps vs QuerySurge: A Buyer’s Guide to Data Validation</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></description>
<content:encoded><![CDATA[ <div data-elementor-type="wp-post" data-elementor-id="60475" class="elementor elementor-60475" data-elementor-post-type="post">
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<h2 class="elementor-heading-title elementor-size-default">Datagaps vs QuerySurge An Honest Buyer's Guide to Enterprise Data Validation </h2> </div>
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<p><span style="text-decoration: underline; color: #1eb473;"><a style="color: #1eb473; text-decoration: underline;" href="https://www.datagaps.com/dataops-suite/" target="_blank" rel="noopener"><span class="TextRun MacChromeBold SCXW34210726 BCX0" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SpellingErrorV2Themed SCXW34210726 BCX0">Datagaps</span> <span class="NormalTextRun SpellingErrorV2Themed SCXW34210726 BCX0">DataOps</span><span class="NormalTextRun SCXW34210726 BCX0"> Suite </span></span></a></span><span class="TextRun SCXW34210726 BCX0" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW34210726 BCX0">is an end-to-end data validation and test automation platform that </span><span class="NormalTextRun SCXW34210726 BCX0">validates</span><span class="NormalTextRun SCXW34210726 BCX0"> 100% of records across ETL pipelines, BI dashboards, and cloud migrations. It is the </span></span><span class="TextRun SCXW34210726 BCX0" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW34210726 BCX0">only data validation vendor recognised in both the Gartner Market Guide for </span><span class="NormalTextRun SpellingErrorV2Themed SCXW34210726 BCX0">DataOps</span><span class="NormalTextRun SCXW34210726 BCX0"> Tools and</span></span><span class="TextRun SCXW34210726 BCX0" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW34210726 BCX0"> the Gartner Market Guide for Data Observability Tools. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW34210726 BCX0">QuerySurge</span><span class="NormalTextRun SCXW34210726 BCX0"> is listed in neither.</span></span><span class="EOP Selected SCXW34210726 BCX0" data-ccp-props="{"335557856":15856352,"335559685":360,"335559737":360,"335559738":100,"335559739":100,"335572083":14,"335572084":0,"335572085":6056192,"469789810":"thick"}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">The Verdict at a Glance </h2> </div>
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<div class="elementor-image-box-wrapper"><div class="elementor-image-box-content"><h3 class="elementor-image-box-title">Analyst recognition</h3><p class="elementor-image-box-description">Datagaps holds dual Gartner Market Guide listings — DataOps Tools and Data Observability Tools. QuerySurge holds neither. This is the most independently verifiable signal available to buyers in this category.</p></div></div> </div>
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<div class="elementor-element elementor-element-bb5448c elementor-widget elementor-widget-image-box" data-id="bb5448c" data-element_type="widget" data-e-type="widget" data-widget_type="image-box.default">
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<div class="elementor-image-box-wrapper"><div class="elementor-image-box-content"><h3 class="elementor-image-box-title"> Full-stack platform, not ETL-only</h3><p class="elementor-image-box-description">DataOps Suite spans four products — ETL Validator, BI Validator, Data Quality Monitor, Test Data Manager — under one platform. QuerySurge's own "alternatives" whitepaper describes Datagaps as only "ETL Validator," omitting three of the four products entirely.</p></div></div> </div>
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<div class="elementor-image-box-wrapper"><div class="elementor-image-box-content"><h3 class="elementor-image-box-title"> Platform vs tool</h3><p class="elementor-image-box-description">QuerySurge validates SQL-to-SQL at the data layer. Datagaps validates across ETL, BI dashboards, continuous data quality, and synthetic test data — from one platform, on one audit trail.</p></div></div> </div>
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<div class="elementor-image-box-wrapper"><div class="elementor-image-box-content"><h3 class="elementor-image-box-title">AI depth</h3><p class="elementor-image-box-description">Datagaps runs five autonomous agentic AI capabilities across the full test lifecycle. QuerySurge offers generative AI for SQL query writing. These are different categories of AI capability.</p></div></div> </div>
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<div class="elementor-image-box-wrapper"><div class="elementor-image-box-content"><h3 class="elementor-image-box-title"> Scale</h3><p class="elementor-image-box-description">Apache Spark engine, 200+ native connectors, billion-record validation. G2 reviewers note QuerySurge lacks native JSON and REST API support — requiring pre-transformation before validation.</p></div></div> </div>
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<div class="elementor-image-box-wrapper"><div class="elementor-image-box-content"><h3 class="elementor-image-box-title">Verify independently</h3><p class="elementor-image-box-description">QuerySurge's public comparison awards itself 14 advantages in 14 categories. Every advantage is self-declared. None are independently verified. The clinical review below addresses each claim.</p></div></div> </div>
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<h2 class="elementor-heading-title elementor-size-default">The Market Has Moved. The Category Question Has Changed. </h2> </div>
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<p><span data-contrast="none">The data validation category was built around a single problem: does the target system contain what the source system sent? SQL-to-SQL row comparison answered that question adequately for batch pipelines feeding relational warehouses. That problem has not disappeared. But it now represents only a fraction of what enterprise data validation must cover.</span><span data-ccp-props="{"335559685":240,"335559738":60,"335559739":90}"> </span></p><p><span data-contrast="none">Modern data estates span cloud warehouses, data lakes, streaming ingestion, SaaS integrations, BI dashboards, and AI model training pipelines. Each layer introduces distinct failure modes — schema drift, transformation errors, distribution shift, silent quality degradation, stale features — that SQL row comparison cannot detect because the failure does not produce a row-count mismatch.</span><span data-ccp-props="{"335559685":240,"335559738":60,"335559739":90}"> </span></p><p><span data-contrast="none">The validation boundary has expanded accordingly. Leading enterprises now validate pipelines from ingestion through BI output, score data quality continuously between runs, and assess datasets for AI readiness before training begins. Gartner reflects this: the DataOps Tools market grew 21% and the Data Observability market reached $346.4M — both trending toward platforms that combine orchestration, observability, validation, and governance rather than single-function tools.</span><span data-ccp-props="{"335559685":240,"335559738":60,"335559739":90}"> </span></p> </div>
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<p><span class="TextRun MacChromeBold SCXW51228377 BCX0" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW51228377 BCX0">The implication for buyers: </span></span><span class="TextRun SCXW51228377 BCX0" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW51228377 BCX0">A tool that solves only the ETL row-comparison problem is not competing in the same category as a platform that solves the full data reliability lifecycle. Evaluate which problem you are </span><span class="NormalTextRun AdvancedProofingIssueV2Themed SCXW51228377 BCX0">actually buying</span><span class="NormalTextRun SCXW51228377 BCX0"> a solution for — then verify which vendors are independently recognised as solving it.</span></span><span class="EOP Selected SCXW51228377 BCX0" data-ccp-props="{"335557856":15203839,"335559685":360,"335559737":360,"335559738":100,"335559739":100,"335572083":16,"335572084":0,"335572085":755384,"469789810":"thick"}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">What Analyst Recognition Actually Tells Buyers </h2> </div>
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<p><span class="TextRun SCXW69494111 BCX0" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW69494111 BCX0">Gartner publishes two Market Guides directly relevant to this evaluation: the Market Guide for </span><span class="NormalTextRun SpellingErrorV2Themed SCXW69494111 BCX0">DataOps</span><span class="NormalTextRun SCXW69494111 BCX0"> Tools and the Market Guide for Data Observability Tools. Each </span><span class="NormalTextRun SCXW69494111 BCX0">identifies</span><span class="NormalTextRun SCXW69494111 BCX0"> Representative Vendors based on market presence, client inquiry volume, and analyst-verified functional coverage. Inclusion is not self-nominated — it reflects independent analyst assessment.</span></span><span class="EOP Selected SCXW69494111 BCX0" data-ccp-props="{"335559685":240,"335559738":60,"335559739":90}"> </span></p> </div>
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<thead>
<tr>
<th>Gartner Market Guide</th>
<th>Datagaps</th>
<th>QuerySurge</th>
</tr>
</thead>
<tbody>
<tr>
<td>DataOps Tools Market Guide (Oct 2025)</td>
<td class="listed">
<span class="check-icon">✓</span>
Listed as Representative Vendor
</td>
<td class="not-listed">
<span class="cross-icon">✗</span>
Not listed
</td>
</tr>
<tr>
<td>Data Observability Tools Market Guide (Feb 2026)</td>
<td class="listed">
<span class="check-icon">✓</span>
Listed for data content observability, pipeline observability, and data lineage
</td>
<td class="not-listed">
<span class="cross-icon">✗</span>
Not listed
</td>
</tr>
</tbody>
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<p><span class="TextRun SCXW143839929 BCX0" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SpellingErrorV2Themed SCXW143839929 BCX0">In the </span><span class="NormalTextRun SpellingErrorV2Themed SCXW143839929 BCX0">DataOps</span><span class="NormalTextRun SCXW143839929 BCX0"> Tools Market Guide, </span><span class="NormalTextRun SpellingErrorV2Themed SCXW143839929 BCX0">Datagaps</span> <span class="NormalTextRun SpellingErrorV2Themed SCXW143839929 BCX0">is categ</span><span class="NormalTextRun SCXW143839929 BCX0">orised as a specialist in data pipeline test automation, with Gartner citing enterprise customers across financial services, healthcar</span><span class="NormalTextRun SpellingErrorV2Themed SCXW143839929 BCX0">e, tech</span><span class="NormalTextRun SCXW143839929 BCX0">nology, and higher education. In the Data Observability</span><span class="NormalTextRun SpellingErrorV2Themed SCXW143839929 BCX0"> Market Gu</span><span class="NormalTextRun SCXW143839929 BCX0">ide, </span><span class="NormalTextRun SpellingErrorV2Themed SCXW143839929 BCX0">Datagaps</span><span class="NormalTextRun SCXW143839929 BCX0"> is listed for its <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-quality-monitoring-tools/" target="_blank" rel="noopener">Data Quality Monitor</a></span> covering data content observability, pipeline observability, and lineage — validating data where it </span><span class="NormalTextRun SCXW143839929 BCX0">resides</span><span class="NormalTextRun SCXW143839929 BCX0"> rather than moving it.</span></span><span class="EOP Selected SCXW143839929 BCX0" data-ccp-props="{"335559685":240,"335559738":60,"335559739":90}"> </span></p> </div>
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<p><span class="TextRun SCXW72362536 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW72362536 BCX8"><strong>The bottom line:</strong> </span></span><span class="TextRun SCXW72362536 BCX8" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW72362536 BCX8">Dual Gartner listing is not a badge. It is evidence that analysts, tracking two distinct and fast-growing markets, independently verified Datagaps’ capability in both. A vendor that appears in neither guide is asking buyers to accept its capability claims on its own authority.</span></span></p> </div>
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<p><span class="TextRun SCXW118929433 BCX0" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SpellingErrorV2Themed SCXW118929433 BCX0">Datagaps</span><span class="NormalTextRun SCXW118929433 BCX0"> is also </span><span class="NormalTextRun SCXW118929433 BCX0">a certifi</span><span class="NormalTextRun SCXW118929433 BCX0">ed <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/snowflake-testing-automation/" target="_blank" rel="noopener">Snowflake</a></span> Select Technology Partner and <span style="text-decoration: underline; color: #1967d2;"><span><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/databricks-testing-automation/" target="_blank" rel="noopener">Data</a></span></span></span><span class="NormalTextRun SpellingErrorV2Themed SCXW118929433 BCX0"><span style="text-decoration: underline; color: #1967d2;">bricks</span> Technology P</span><span class="NormalTextRun SCXW118929433 BCX0">artner. Seven technology partners (AWS, Azure, Snowflake, Databricks, Oracle, Tableau, Cloudera) and 17+ SI partners including Tech Mahindra, Virtusa, CitiusTech, and </span><span class="NormalTextRun SpellingErrorV2Themed SCXW118929433 BCX0">Qualitest</span><span class="NormalTextRun SCXW118929433 BCX0"> round out the ecosystem. Both platforms are listed on AWS and Microsoft Azure Marketplaces.</span></span><span class="EOP Selected SCXW118929433 BCX0" data-ccp-props="{"335559685":240,"335559738":60,"335559739":90}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">A Clinical Review of QuerySurge's Public Claims </h2> </div>
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<p><span class="TextRun SCXW61385726 BCX0" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SpellingErrorV2Themed SCXW61385726 BCX0">QuerySurge</span><span class="NormalTextRun SCXW61385726 BCX0"> publishes a competitive comparison page that awards itself the advantage in all 14 categories it evaluates (querysurge.com/product-tour/competitive-analysis/</span><span class="NormalTextRun SpellingErrorV2Themed SCXW61385726 BCX0">datagaps</span><span class="NormalTextRun SCXW61385726 BCX0">). Every advantage is self-declared. We reviewed each claim against product documentation, analyst evidence, and independent G2 reviews. The results:</span></span><span class="EOP Selected SCXW61385726 BCX0" data-ccp-props="{"335559685":240,"335559738":60,"335559739":90}"> </span></p> </div>
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<th>#</th>
<th>Category</th>
<th>QuerySurge's Claim About Datagaps</th>
<th>What Independent Evidence Shows</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td>Primary Focus</td>
<td>Broader suite; less depth in regression and DevOps</td>
<td>Datagaps is an end-to-end platform with dedicated regression capabilities and native Azure DevOps integration. Gartner's Market Guide cites it specifically for pipeline test automation depth.</td>
</tr>
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<td>2</td>
<td>Deployment</td>
<td>Fewer hybrid configurations</td>
<td>Supports on-premises, cloud, and hybrid with containerised installs and engine clustering on EMR, YARN, and Databricks.</td>
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<td>3</td>
<td>Target Users</td>
<td>Primarily data engineers/analysts; less QA focus</td>
<td>Serves QA teams, data engineers, DQ analysts, and business users via no-code/low-code test creation — across all pipeline types.</td>
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<td>4</td>
<td>Ease of Use</td>
<td>Requires more manual configuration</td>
<td>No-code/low-code wizards, AI-assisted test generation from mapping documents or natural language, guided setup, and certification programme. G2 reviewers cite steep learning curve for QuerySurge.</td>
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<td>5</td>
<td>Test Automation</td>
<td>Less robust regression and DevOps API</td>
<td>Apache Spark engine validates billions of records with auto-scaling. End-to-end automation with scheduling, regression packs for ETL and BI, and native CI/CD integration. QuerySurge's own handout rates CI/CD as comparable.</td>
</tr>
<tr>
<td>6</td>
<td>AI Capabilities</td>
<td>No AI-powered test creation</td>
<td>Factually incorrect. Five agentic AI capabilities: test generation from ETL mappings, anomaly detection, continuous DQ scoring, synthetic test data generation, and self-adapting rules on schema change. QuerySurge's AI is generative SQL writing only.</td>
</tr>
<tr>
<td>7</td>
<td>CI/CD Integration</td>
<td>Lacks dedicated DevOps API</td>
<td>Native Azure DevOps task with JUnit publishing, REST API, CLI, and GitHub support. QuerySurge's own materials describe CI/CD as comparable — contradicting this claim.</td>
</tr>
<tr>
<td>8</td>
<td>Audit & Compliance</td>
<td>Less depth in compliance and lineage</td>
<td>Full audit logs with export, integration with governance and catalog platforms. Aligned with SOC 2 Type II, HIPAA, GDPR, CCPA, and PCI DSS.</td>
</tr>
<tr>
<td>9</td>
<td>BI Testing</td>
<td>Higher-level; less cell-by-cell granularity</td>
<td>Datagaps BI Validator performs cell-level comparison against source SQL, pixel-to-pixel PDF regression, filter/slicer testing, and stress testing across several BI platforms (Power BI, Tableau, QuickSight, Oracle Analytics, BusinessObjects, Cognos, Looker, and MicroStrategy). QuerySurge's focus is data-layer validation. This is a category gap, not a depth gap.</td>
</tr>
<tr>
<td>10</td>
<td>Reusable Assets</td>
<td>Does not offer reusable components</td>
<td>Factually incorrect. Reusable test cases, rule sets, templates, bulk baseline creation, and centrally clonable DQ rules across projects.</td>
</tr>
<tr>
<td>11</td>
<td>Connectors</td>
<td>Smaller connector library</td>
<td>200+ source and target systems via Apache Spark — JDBC, NoSQL, cloud warehouses, flat files, APIs, cloud object storage (S3, ADLS, GCS), and native BI connectors for 7 platforms. G2 reviewers note QuerySurge lacks native JSON and REST API support.</td>
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<tr>
<td>12</td>
<td>Reporting</td>
<td>Fewer visualization features</td>
<td>Fully customisable stakeholder dashboards for DQ scores and test results. DQ scorecards with drill-downs, PDF/Excel export, and row-level downloadable exception outputs.</td>
</tr>
<tr>
<td>13</td>
<td>Customers</td>
<td>Primarily mid-market; tied to specific stacks</td>
<td>Gartner cites enterprise customers across financial services, healthcare, technology, and higher education. Deployments span multiple cloud and on-premises platforms.</td>
</tr>
<tr>
<td>14</td>
<td>Partner Ecosystem</td>
<td>Smaller, limited partner network</td>
<td>7 technology partners, 17+ SI partners, AWS and Azure Marketplace listings. QuerySurge does not publish an equivalent partner roster.</td>
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<p><span class="TextRun MacChromeBold SCXW148062267 BCX0" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW148062267 BCX0">The conclusion: </span></span><span class="TextRun SCXW148062267 BCX0" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW148062267 BCX0">Fourteen categories. Fourteen self-awarded advantages. Zero independently verified. Two claims — AI capabilities and Reusable Assets — are demonstrably incorrect against product documentation. Two others (BI Testing, Co</span><span class="NormalTextRun SCXW148062267 BCX0">nnectors</span><span class="NormalTextRun SCXW148062267 BCX0">) mischaracterise capability gaps as parity. Buyers should evaluate any vendor’s self-authored comparison with </span><span class="NormalTextRun SCXW148062267 BCX0">appropriate scepticism</span><span class="NormalTextRun SCXW148062267 BCX0"> and cross-reference against Gartner, G2, and product documentation.</span></span><span class="EOP Selected SCXW148062267 BCX0" data-ccp-props="{"335557856":15203839,"335559685":360,"335559737":360,"335559738":100,"335559739":100,"335572083":16,"335572084":0,"335572085":755384,"469789810":"thick"}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">What does QuerySurge's own "alternatives" whitepaper say about Datagaps? </h2> </div>
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<p><span class="TextRun SCXW45410657 BCX0" lang="EN-IN" xml:lang="EN-IN" data-contrast="auto"><span class="NormalTextRun SpellingErrorV2Themed SCXW45410657 BCX0">QuerySurge’s</span><span class="NormalTextRun SCXW45410657 BCX0"> “Best </span><span class="NormalTextRun SpellingErrorV2Themed SCXW45410657 BCX0">QuerySurge</span><span class="NormalTextRun SCXW45410657 BCX0"> Alternatives for Data Testing in 2026″ whitepaper describes </span><span class="NormalTextRun SpellingErrorV2Themed SCXW45410657 BCX0">Datagaps</span><span class="NormalTextRun SCXW45410657 BCX0"> exclusively as “<span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/automate-oracle-analytics-testing/" target="_blank" rel="noopener">ETL Validator</a></span>,” omitting <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/bi-testing-tools/" target="_blank" rel="noopener">BI Validator</a></span>, <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-quality-monitoring-tools/" target="_blank" rel="noopener">Data Quality Monitor</a></span>, <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/test-data-manager/" target="_blank" rel="noopener">Test Data Manager</a></span>, and the </span><span class="NormalTextRun SpellingErrorV2Themed SCXW45410657 BCX0">DataOps</span><span class="NormalTextRun SCXW45410657 BCX0"> Suite umbrella entirely. It reduces five agentic AI capabilities to “GenAI-assisted rule authoring,” lists only Jenkins and GitHub as CI/CD integrations while omitting the native Azure DevOps task, and claims </span><span class="NormalTextRun SpellingErrorV2Themed SCXW45410657 BCX0">Datagaps</span><span class="NormalTextRun SCXW45410657 BCX0"> is “narrower on BI and migration coverage” despite BI Validator supporting seve</span><span class="NormalTextRun SCXW45410657 BCX0">ral BI</span><span class="NormalTextRun SCXW45410657 BCX0"> platforms with cell-level, pixel-to-pixel, and stress testing. Buyers </span><span class="NormalTextRun SCXW45410657 BCX0">encountering</span><span class="NormalTextRun SCXW45410657 BCX0"> that whitepaper should cross-reference its claims against </span><span class="NormalTextRun SpellingErrorV2Themed SCXW45410657 BCX0">Datagaps</span><span class="NormalTextRun SCXW45410657 BCX0"> product documentation and the two Gartner Market Guides where </span><span class="NormalTextRun SpellingErrorV2Themed SCXW45410657 BCX0">Datagaps</span><span class="NormalTextRun SCXW45410657 BCX0"> is listed and </span><span class="NormalTextRun SpellingErrorV2Themed SCXW45410657 BCX0">QuerySurge</span><span class="NormalTextRun SCXW45410657 BCX0"> is not.</span></span><span class="EOP Selected SCXW45410657 BCX0" data-ccp-props="{"335559738":40,"335559739":40}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Platform vs. Tool — Five Structural Gaps That Cannot Be Configured Away </h2> </div>
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<p><span class="TextRun SCXW176120362 BCX0" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW176120362 BCX0">The comparison above addresses what </span><span class="NormalTextRun SpellingErrorV2Themed SCXW176120362 BCX0">QuerySurge</span><span class="NormalTextRun SCXW176120362 BCX0"> claims. The five gaps below address something more fundamental: capabilities a single-function ETL validation tool structurally cannot offer, because they </span><span class="NormalTextRun SCXW176120362 BCX0">require</span><span class="NormalTextRun SCXW176120362 BCX0"> a platform architecture rather than a point tool.</span></span><span class="EOP Selected SCXW176120362 BCX0" data-ccp-props="{"335559685":240,"335559738":60,"335559739":90}"> </span></p> </div>
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<div class="elementor-image-box-wrapper"><div class="elementor-image-box-content"><h3 class="elementor-image-box-title">1. Agentic AI vs. Generative Assistance </h3><p class="elementor-image-box-description">Datagaps runs five autonomous agentic AI capabilities: test case generation from ETL mapping documents, ML-based anomaly detection, continuous data quality scoring, synthetic test data generation, and rules that self-adapt when schemas change. These are workflows the AI executes autonomously — not suggestions a human must implement. QuerySurge offers generative AI for writing SQL queries. That is AI-assisted authoring, not AI-native automation. The distinction matters because agentic AI eliminates the manual maintenance burden that grows linearly with pipeline count; generative AI reduces authoring time but does not eliminate it. </p></div></div> </div>
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<div class="elementor-image-box-wrapper"><div class="elementor-image-box-content"><h3 class="elementor-image-box-title">2. BI Validation Across Several Platforms </h3><p class="elementor-image-box-description">BI Validator performs cell-level comparison of dashboard values against source SQL, pixel-to-pixel PDF regression, filter and slicer combination testing, and concurrent user stress testing — across Power BI, Tableau, QuickSight, Oracle Analytics, BusinessObjects, Cognos, Looker and MicroStrategy. QuerySurge's focus is data-layer validation. This is not a depth difference — it is a scope difference. A tool that does not validate the BI layer cannot tell you whether what the business sees matches what the data contains. </p></div></div> </div>
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<div class="elementor-image-box-wrapper"><div class="elementor-image-box-content"><h3 class="elementor-image-box-title">3. Business-Accessible Interface </h3><p class="elementor-image-box-description">No-code and low-code wizards, drag-and-drop test builders, and AI-assisted test generation from natural language allow QA teams, data analysts, and business users to author and run validations without writing SQL. This expands validation ownership beyond the data engineering team — which matters for organizations that cannot staff a dedicated validation team for every pipeline. G2 reviewers cite QuerySurge's steep learning curve and requirement for strong data engineering knowledge as adoption barriers. </p></div></div> </div>
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<div class="elementor-image-box-wrapper"><div class="elementor-image-box-content"><h3 class="elementor-image-box-title">4. Enterprise Scale Without Sampling </h3><p class="elementor-image-box-description">The Apache Spark engine with auto-scaling on EMR, YARN, Databricks, and Kubernetes validates billions of source-to-target rows at full volume — not sampled. For regulated workloads (SOX, HIPAA, NAIC MAR), sampling is not an option: an audit finding based on partial validation creates exposure. G2 reviewers note QuerySurge lacks native JSON and REST API support, requiring transformation into a relational database before validation begins. In modern API-first and event-driven architectures, that is a structural limitation, not a configuration gap. <br>
<br>
Field Benchmark: Same Machine, Same Test<br>
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In a direct evaluation, a customer ran identical reconciliation workloads on the same infrastructure: 15 million records from Parquet and 15 million records from Snowflake (10 columns). DataOps Suite reconciled the full dataset in under 6 minutes. QuerySurge crashed on the same test. </p></div></div> </div>
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<div class="elementor-image-box-wrapper"><div class="elementor-image-box-content"><h3 class="elementor-image-box-title">5. Modular Platform Architecture </h3><p class="elementor-image-box-description">DataOps Suite is modular by design. Teams start with ETL Validator for pipeline validation and expand to BI Validator, Data Quality Monitor, or Test Data Manager as their needs grow — without re-platforming. Each module shares the same connector layer, the same audit trail, and the same data lineage graph. Single-function tools cannot expand into adjacent capabilities without replacing themselves with a different tool, adding integration overhead and fragmenting the audit trail. </p></div></div> </div>
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<h2 class="elementor-heading-title elementor-size-default">What Independent Reviews Reveal </h2> </div>
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<p><span class="TextRun SCXW250636786 BCX0" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW250636786 BCX0">G2 reviews of </span><span class="NormalTextRun SpellingErrorV2Themed SCXW250636786 BCX0">QuerySurge</span><span class="NormalTextRun SCXW250636786 BCX0"> (January–June 2026) surface a consistent pattern across four themes. These are not </span><span class="NormalTextRun SpellingErrorV2Themed SCXW250636786 BCX0">Datagaps</span><span class="NormalTextRun SCXW250636786 BCX0">‘ characterizations — they are the words of </span><span class="NormalTextRun SpellingErrorV2Themed SCXW250636786 BCX0">QuerySurge’s</span><span class="NormalTextRun SCXW250636786 BCX0"> own customers:</span></span><span class="EOP Selected SCXW250636786 BCX0" data-ccp-props="{"335559685":240,"335559738":60,"335559739":90}"> </span></p> </div>
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<th>G2 Theme (QuerySurge reviews, H1 2026)</th>
<th>What Datagaps Provides</th>
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<td>Steep learning curve; requires strong data engineering knowledge</td>
<td>No-code/low-code wizards with AI-assisted onboarding; business users and analysts author validations without SQL</td>
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<td>No native JSON or REST API support; requires pre-transformation into a database</td>
<td>200+ native connectors including REST APIs and JSON via Apache Spark — no pre-transformation required</td>
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<tr>
<td>Connecting multiple data sources adds significant onboarding overhead</td>
<td>Guided source and target connection wizards; most environments connected in under 30 minutes</td>
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<td>Enterprise-level licensing cost cited as a barrier</td>
<td>Modular pricing — start with ETL validation; add BI, DQ, or test data modules as needs grow</td>
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<p><span data-contrast="none">On BI testing, the contrast is structural rather than marginal: BI Validator validates at the cell level across several platforms with pixel-to-pixel regression and stress testing. QuerySurge’s validation boundary ends at the data layer. On AI, where QuerySurge offers generative SQL assistance, Datagaps runs five autonomous agents across the full test lifecycle.</span><span data-ccp-props="{"335559685":240,"335559738":60,"335559739":90}"> </span></p><p> </p><p><span data-contrast="none">Buyers do not need to take either vendor’s word for it. The Gartner Market Guides, G2 reviews, and product documentation are all independently accessible. Verification takes less time than a vendor demo.</span><span data-ccp-props="{"335559685":240,"335559738":60,"335559739":90}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">The Three Questions That Should Frame Your Evaluation </h2> </div>
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<p><span class="TextRun SCXW48199917 BCX0" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SCXW48199917 BCX0">The data validation market has </span><span class="NormalTextRun SCXW48199917 BCX0">consolidated</span><span class="NormalTextRun SCXW48199917 BCX0"> around a set of capabilities that point tools wer</span><span class="NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW48199917 BCX0">e not desig</span><span class="NormalTextRun SCXW48199917 BCX0">ned to provide. Buyers entering an evaluation should lead with three questions — and verify the answers through sources other than the vendor:</span></span><span class="EOP Selected SCXW48199917 BCX0" data-ccp-props="{"335559685":240,"335559738":60,"335559739":90}"> </span></p> </div>
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<th>Question</th>
<th>Why It Matters</th>
<th>How to Verify</th>
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<td>Does it validate across the full data lifecycle — ingestion, ETL, BI, and AI inputs — or only at the warehouse layer?</td>
<td>A tool that stops at the ETL layer leaves BI validation, quality monitoring, and AI readiness uncovered — requiring additional tools and fragmenting the audit trail</td>
<td>Request a live demonstration across all layers. Ask specifically about BI cell-level validation and AI training data assessment.</td>
</tr>
<tr>
<td>Is it independently recognised by analysts — not just its own marketing?</td>
<td>Self-awarded advantages are marketing. Gartner Market Guide listings are analyst-verified capability. The difference matters when the audit exposure is yours.</td>
<td>Check the Gartner Market Guide for DataOps Tools and the Market Guide for Data Observability Tools directly.</td>
</tr>
<tr>
<td>Can its claims be verified through product documentation, independent reviews, and analyst publications?</td>
<td>Vendors who make claims they cannot substantiate through verifiable third-party sources are telling buyers something important about how they operate.</td>
<td>Cross-reference G2 reviews, Gartner listings, and product documentation before accepting any feature comparison at face value.</td>
</tr>
</tbody>
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</div>
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<p><span class="TextRun SCXW220818950 BCX0" lang="EN-IN" xml:lang="EN-IN" data-contrast="none"><span class="NormalTextRun SpellingErrorV2Themed SpellingErrorHighlight SCXW220818950 BCX0">Datagaps</span> <span class="NormalTextRun SpellingErrorV2Themed SCXW220818950 BCX0">DataOps</span><span class="NormalTextRun SCXW220818950 BCX0"> Suite was built to answer all three. We invite buyers to verify that independently — not through our materials, but through Gartner, G2, and a proof-of-concept on their own data.</span></span><span class="EOP Selected SCXW220818950 BCX0" data-ccp-props="{"335557856":15203839,"335559685":360,"335559737":360,"335559738":100,"335559739":100,"335572083":16,"335572084":0,"335572085":755384,"469789810":"thick"}"> </span></p> </div>
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<span class="elementor-heading-title elementor-size-default">Get Started Today</span> </div>
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<h2 class="elementor-heading-title elementor-size-default">See Datagaps validate a QuerySurge-style workload end-to-end on your data </h2> </div>
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<p>Our team will run a live demo against your specific use case — your stack, your data problem, your pipeline.</p> </div>
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<span class="elementor-button-text">Explore the full DataOps Suite</span>
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<a class="elementor-button elementor-button-link elementor-size-sm" href="https://www.datagaps.com/data-ops-suite-trial-request/" target="_blank">
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<span class="elementor-button-text">Start Free 14-Day Trial</span>
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<p>SOC 2 Type II certified | ISO 27001 certified | No credit card required for trial | Your data never leaves your environment</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Frequently Asked Questions</h2> </div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="1" aria-controls="elementor-tab-content-2261"><h3 class="eael-accordion-tab-title">Is Datagaps a QuerySurge alternative? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-2261" class="eael-accordion-content clearfix" data-tab="1" aria-labelledby="faq-1"><p><span class="TextRun SCXW154745715 BCX0"><span class="NormalTextRun SCXW154745715 BCX0">Yes. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW154745715 BCX0">Datagaps</span> <span class="NormalTextRun SpellingErrorV2Themed SCXW154745715 BCX0">DataOps</span><span class="NormalTextRun SCXW154745715 BCX0"> Suite is a direct alternative for enterprise ETL testing and data validation, and extends beyond the ETL layer with BI Validator, Data Quality Monitor, and Test Data Manager. Teams evaluating </span><span class="NormalTextRun SpellingErrorV2Themed SCXW154745715 BCX0">QuerySurge</span><span class="NormalTextRun SCXW154745715 BCX0"> typically shortlist </span><span class="NormalTextRun SpellingErrorV2Themed SCXW154745715 BCX0">Datagaps</span><span class="NormalTextRun SCXW154745715 BCX0"> for its no-code interface, agentic AI test generation, and independent recognition in two Gartner Market Guides.</span></span><span class="EOP Selected SCXW154745715 BCX0"> </span></p></div>
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<div id="faq-2" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="2" aria-controls="elementor-tab-content-2262"><h3 class="eael-accordion-tab-title">What is the main difference between Datagaps and QuerySurge? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-2262" class="eael-accordion-content clearfix" data-tab="2" aria-labelledby="faq-2"><p><span class="TextRun SCXW182133747 BCX0"><span class="NormalTextRun SpellingErrorV2Themed SCXW182133747 BCX0">QuerySurge</span><span class="NormalTextRun SCXW182133747 BCX0"> is a single-function ETL testing tool built around SQL query comparison. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW182133747 BCX0">Datagaps</span> <span class="NormalTextRun SpellingErrorV2Themed SCXW182133747 BCX0">DataOps</span><span class="NormalTextRun SCXW182133747 BCX0"> Suite covers ETL, BI dashboards, continuous data quality scoring, and synthetic test data generation from one platform and one audit trail. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW182133747 BCX0">Datagaps</span><span class="NormalTextRun SCXW182133747 BCX0"> runs five autonomous agentic AI capabilities across the test lifecycle. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW182133747 BCX0">QuerySurge’s</span><span class="NormalTextRun SCXW182133747 BCX0"> AI is limited to generative SQL </span><span class="NormalTextRun SCXW182133747 BCX0">assistance</span><span class="NormalTextRun SCXW182133747 BCX0">.</span></span><span class="EOP Selected SCXW182133747 BCX0"> </span></p></div>
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<div id="faq-3" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="3" aria-controls="elementor-tab-content-2263"><h3 class="eael-accordion-tab-title">Is Datagaps only an ETL testing tool? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-2263" class="eael-accordion-content clearfix" data-tab="3" aria-labelledby="faq-3"><p><span class="TextRun SCXW122920085 BCX0"><span class="NormalTextRun SCXW122920085 BCX0">No. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW122920085 BCX0">Datagaps</span> <span class="NormalTextRun SpellingErrorV2Themed SCXW122920085 BCX0">DataOps</span><span class="NormalTextRun SCXW122920085 BCX0"> Suite includes four products: ETL Validator for pipeline testing, BI Validator for dashboard validation across several BI platforms, Data Quality Monitor for continuous data quality scoring and data lineage, Test Data Manager for synthetic test data generation</span><span class="NormalTextRun SCXW122920085 BCX0">. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW122920085 BCX0">QuerySurge’s</span><span class="NormalTextRun SCXW122920085 BCX0"> published materials describe </span><span class="NormalTextRun SpellingErrorV2Themed SCXW122920085 BCX0">Datagaps</span><span class="NormalTextRun SCXW122920085 BCX0"> as “ETL Validator” only, which omits three out of the four products in the suite.</span></span><span class="EOP Selected SCXW122920085 BCX0"> </span></p></div>
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<div id="faq-4" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="4" aria-controls="elementor-tab-content-2264"><h3 class="eael-accordion-tab-title">Which vendor is recognised by Gartner — Datagaps or QuerySurge? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-2264" class="eael-accordion-content clearfix" data-tab="4" aria-labelledby="faq-4"><p><span class="TextRun SCXW122476096 BCX0"><span class="NormalTextRun SpellingErrorV2Themed SCXW122476096 BCX0">Datagaps</span><span class="NormalTextRun SCXW122476096 BCX0"> is a Representative Vendor in both the Gartner Market Guide for </span><span class="NormalTextRun SpellingErrorV2Themed SCXW122476096 BCX0">DataOps</span><span class="NormalTextRun SCXW122476096 BCX0"> Tools and the Gartner Market Guide for Data Observability Tools — the only vendor in the data validation and test automation category to appear in both. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW122476096 BCX0">QuerySurge</span><span class="NormalTextRun SCXW122476096 BCX0"> is not listed in either Gartner Market Guide.</span></span><span class="EOP Selected SCXW122476096 BCX0"> </span></p></div>
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<div id="faq-5" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="5" aria-controls="elementor-tab-content-2265"><h3 class="eael-accordion-tab-title">Does Datagaps validate 100% of records, or does it sample? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-2265" class="eael-accordion-content clearfix" data-tab="5" aria-labelledby="faq-5"><p><span class="TextRun SCXW165588776 BCX0"><span class="NormalTextRun SpellingErrorV2Themed SCXW165588776 BCX0">Datagaps</span><span class="NormalTextRun SCXW165588776 BCX0"> validates 100% of records — not a sample. The platform runs on an Apache Spark engine with auto-scaling on EMR, YARN, Databricks, and Kubernetes, enabling billion-row source-to-target reconciliation. Full-volume validation is </span><span class="NormalTextRun SCXW165588776 BCX0">required</span><span class="NormalTextRun SCXW165588776 BCX0"> for regulated workloads where sampling creates audit exposure under SOX, HIPAA, and NAIC MAR.</span></span><span class="EOP Selected SCXW165588776 BCX0"> </span></p></div>
</div><div class="eael-accordion-list">
<div id="faq-6" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="6" aria-controls="elementor-tab-content-2266"><h3 class="eael-accordion-tab-title">Does Datagaps support BI testing platforms like Tableau, Power BI, and Oracle Analytics? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-2266" class="eael-accordion-content clearfix" data-tab="6" aria-labelledby="faq-6"><p><span class="TextRun SCXW201647421 BCX0"><span class="NormalTextRun SCXW201647421 BCX0">Yes. BI Validator covers several platforms: Power BI, Tableau, </span><span class="NormalTextRun SpellingErrorV2Themed SCXW201647421 BCX0">QuickSight</span><span class="NormalTextRun SCXW201647421 BCX0">, Oracle Analytics, BusinessObjects, Cognos, Looker and MicroStrategy. It performs cell-level comparison against source SQL, pixel-to-pixel PDF regression, filter and slicer combination testing, and concurrent user stress testing. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW201647421 BCX0">QuerySurge’s</span><span class="NormalTextRun SCXW201647421 BCX0"> focus is data-layer validation — this is a scope difference, not a depth difference.</span></span><span class="EOP SCXW201647421 BCX0"> </span></p></div>
</div><div class="eael-accordion-list">
<div id="faq-7" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="7" aria-controls="elementor-tab-content-2267"><h3 class="eael-accordion-tab-title">How does the AI in Datagaps compare to QuerySurge? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-2267" class="eael-accordion-content clearfix" data-tab="7" aria-labelledby="faq-7"><p><span class="TextRun SCXW65828734 BCX0"><span class="NormalTextRun SpellingErrorV2Themed SCXW65828734 BCX0">Datagaps</span><span class="NormalTextRun SCXW65828734 BCX0"> runs five agentic AI capabilities: test case generation from ETL mappings, ML-based anomaly detection, continuous DQ scoring, synthetic test data generation, and rules that self-adapt on schema change. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW65828734 BCX0">QuerySurge</span><span class="NormalTextRun SCXW65828734 BCX0"> offers generative AI for writing SQL queries. Agentic AI executes workflows autonomously; generative AI reduces authoring time but does not </span><span class="NormalTextRun SCXW65828734 BCX0">eliminate</span><span class="NormalTextRun SCXW65828734 BCX0"> the manual maintenance burden.</span></span><span class="EOP Selected SCXW65828734 BCX0"> </span></p></div>
</div><div class="eael-accordion-list">
<div id="faq-8" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="8" aria-controls="elementor-tab-content-2268"><h3 class="eael-accordion-tab-title">How should buyers verify vendor claims in this category? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-down"></i></div><div id="elementor-tab-content-2268" class="eael-accordion-content clearfix" data-tab="8" aria-labelledby="faq-8"><p><span class="TextRun SCXW106592966 BCX0"><span class="NormalTextRun SCXW106592966 BCX0">Cross-reference three independent sources: the Gartner Market Guides for </span><span class="NormalTextRun SpellingErrorV2Themed SCXW106592966 BCX0">DataOps</span><span class="NormalTextRun SCXW106592966 BCX0"> Tools and Data Observability Tools, G2 and Gartner Peer Insights reviews, and product documentation. Any category where a vendor is the only source of its own advantage claim should be treated as a marketing assertion, not verified capability — regardless of which vendor makes the claim.</span></span><span class="EOP Selected SCXW106592966 BCX0"> </span></p></div>
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Raj Mohan Achanta </a>
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Associate Product Manager, Datagaps </p>
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Avinash Keshri </a>
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<p>The post <a href="https://www.datagaps.com/blog/datagaps-vs-querysurge/">Datagaps vs QuerySurge: A Buyer’s Guide to Data Validation</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></content:encoded>
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<title>ERP Implementations Still Fail at Alarming Rates – Here’s Why Testing Automation With Robust Data Validation Is the Fix</title>
<link>https://www.datagaps.com/blog/erp-implementation-failures-testing-automation-data-validation/</link>
<comments>https://www.datagaps.com/blog/erp-implementation-failures-testing-automation-data-validation/#respond</comments>
<dc:creator><![CDATA[Adithya Buddhavarapu]]></dc:creator>
<pubDate>Thu, 04 Jun 2026 14:57:39 +0000</pubDate>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[Data Validation]]></category>
<category><![CDATA[ETL Testing]]></category>
<guid isPermaLink="false">https://www.datagaps.com/?p=49913</guid>
<description><![CDATA[<p>Citing recent ERP Implementation Failure Statistics research, this post explains why most ERP implementations still fail to meet their objectives, even with strong budgets and vendor support. It breaks down the most common root causes—weak change management, poor data migration, and inexperienced implementation teams—and argues that testing automation systematically addresses most of them. The post […]</p>
<p>The post <a href="https://www.datagaps.com/blog/erp-implementation-failures-testing-automation-data-validation/">ERP Implementations Still Fail at Alarming Rates – Here’s Why Testing Automation With Robust Data Validation Is the Fix</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></description>
<content:encoded><![CDATA[ <div data-elementor-type="wp-post" data-elementor-id="49913" class="elementor elementor-49913" data-elementor-post-type="post">
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<p>Citing recent ERP Implementation Failure Statistics research, this post explains why most ERP implementations still fail to meet their objectives, even with strong budgets and vendor support. It breaks down the most common root causes—weak change management, poor data migration, and inexperienced implementation teams—and argues that testing automation systematically addresses most of them. The post highlights how manufacturing complexity escalates migration risk, and recommends treating testing as a continuous, first-class workstream rather than a final-phase checkbox.</p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li><strong>ERP failure rates remain persistently high</strong> — the majority of implementations still fail to meet their stated objectives, a pattern that has held steady across the industry for years despite strong budgets and executive backing.</li><li><strong>Poor data migration is a leading, preventable failure cause</strong> — alongside weak change management and inexperienced implementation teams, it accounts for the bulk of ERP project failures, and is precisely the kind of issue automated validation is built to catch.</li><li><strong>Manufacturing complexity directly escalates migration risk</strong> — simpler models like Make-to-Stock carry lower risk, while highly configurable models like Engineer-to-Order introduce far more custom logic and testing surface area.</li><li><strong>Automation pays for itself well beyond its upfront cost</strong> — a modest investment in test automation can prevent the much larger cost overruns typical of poorly tested ERP migrations, making it a fiduciary decision as much as a technical one.</li></ul> </div>
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<p>Modern ERP transformations require a dual focus on testing automation and datavalidation to ensure quality, accuracy, and long-term system reliability.</p> </div>
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<img loading="lazy" decoding="async" width="640" height="285" src="https://www.datagaps.com/wp-content/uploads/Validation-vs-Migration-Effort-Analytical-View-1-768x342.jpg" class="attachment-medium_large size-medium_large wp-image-52007" alt="Validation vs Migration Effort Analytical View" srcset="https://www.datagaps.com/wp-content/uploads/Validation-vs-Migration-Effort-Analytical-View-1-768x342.jpg 768w, https://www.datagaps.com/wp-content/uploads/Validation-vs-Migration-Effort-Analytical-View-1-300x134.jpg 300w, https://www.datagaps.com/wp-content/uploads/Validation-vs-Migration-Effort-Analytical-View-1-1024x456.jpg 1024w, https://www.datagaps.com/wp-content/uploads/Validation-vs-Migration-Effort-Analytical-View-1.jpg 1200w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p>S/4HANA success is driven by a strong foundation built on both testing automation and data validation, ensuring processes run correctly and data drives the right decisions.</p> </div>
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I recently came across Godlan’s <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://godlan.com/erp-implementation-failure-statistics/" target="_blank" rel="noopener">2025 ERP Implementation Failure Statistics research</a></span></span>, and the numbers stopped me cold. Not because they were surprising — anyone who’s lived through a botched ERP rollout knows the pain — but because the industry keeps repeating the same mistakes, year after year, at an industrial scale. </div>
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<p>Let me walk you through what the data says, why it matters for anyone planning an SAP S/4HANA migration, and what I believe is the single most impactful lever to bend these failure curves: testing automation.</p> </div>
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<img loading="lazy" decoding="async" width="640" height="285" src="https://www.datagaps.com/wp-content/uploads/SAP-Landscape-for-Data-Migration-ECC-to-S-4HANA-768x342.jpg" class="attachment-medium_large size-medium_large wp-image-52008" alt="SAP Landscape for Data Migration ECC to S/4HANA" srcset="https://www.datagaps.com/wp-content/uploads/SAP-Landscape-for-Data-Migration-ECC-to-S-4HANA-768x342.jpg 768w, https://www.datagaps.com/wp-content/uploads/SAP-Landscape-for-Data-Migration-ECC-to-S-4HANA-300x134.jpg 300w, https://www.datagaps.com/wp-content/uploads/SAP-Landscape-for-Data-Migration-ECC-to-S-4HANA-1024x456.jpg 1024w, https://www.datagaps.com/wp-content/uploads/SAP-Landscape-for-Data-Migration-ECC-to-S-4HANA.jpg 1200w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<h2 class="elementor-heading-title elementor-size-default">The Numbers Are Brutal</h2> </div>
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Godlan’s research, drawing on Panorama Consulting Group’s 2025 ERP Report and
Gartner analysis, paints a stark picture: </div>
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<h3 class="elementor-heading-title elementor-size-default">Industry-wide ERP implementation failure rates:</h3> </div>
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<p>• <strong>68%</strong> of ERP implementations fail to meet their objectives — and that’s theaverage <br />• <strong>73%</strong> failure rate for discrete manufacturing specifically <br />• <strong>189%</strong> average budget overrun across all industries <br />• <strong>215%</strong> budget overrun in discrete manufacturing <br />•<strong> 25–30%</strong> timeline extensions beyond original plans <br />• Only<strong> 27–32%</strong> of projects actually achieve their stated objectives</p> </div>
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That last number deserves a pause. Fewer than one in three ERP projects delivers what
was promised. And Gartner’s forward-looking analysis projects that 70% of ERP
implementations over the next three years will fail to meet objectives. </div>
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<p>These aren’t fringe projects failing. These are major enterprise investments often tensof millions of dollars that go sideways despite massive budgets, executive sponsorship, and vendor involvement.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">The Root Causes Are Predictable (and Preventable)</h2> </div>
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Godlan’s analysis of over 2,400 ERP implementations identified consistent failure patterns. The top root causes and their frequency: </div>
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<img loading="lazy" decoding="async" width="640" height="285" src="https://www.datagaps.com/wp-content/uploads/SAP-Data-Migration-Stages-with-Pre-Post-Validation-768x342.jpg" class="attachment-medium_large size-medium_large wp-image-52009" alt="SAP-Data-Migration Stages with Pre & Post Validation" srcset="https://www.datagaps.com/wp-content/uploads/SAP-Data-Migration-Stages-with-Pre-Post-Validation-768x342.jpg 768w, https://www.datagaps.com/wp-content/uploads/SAP-Data-Migration-Stages-with-Pre-Post-Validation-300x134.jpg 300w, https://www.datagaps.com/wp-content/uploads/SAP-Data-Migration-Stages-with-Pre-Post-Validation-1024x456.jpg 1024w, https://www.datagaps.com/wp-content/uploads/SAP-Data-Migration-Stages-with-Pre-Post-Validation.jpg 1200w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p>• <strong>Inadequate change management</strong> — 42% of failures <br />• <strong>Poor data migration</strong> — 38% <br />• <strong>Inexperienced implementation teams</strong> — 35% <br />• <strong>Lack of executive sponsorship</strong> — 31% <br />• <strong>Insufficient end-user training</strong> — 29% <br />•<strong> Scope creep</strong> — 26% <br />• <strong>Over-customization</strong> — 23% <br />• <strong>Vendor selection errors</strong> — 19%</p> </div>
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<p>The top three causes alone – change management, data migration, and team inexperience — account for over 75% of failures. And here’s what struck me: every single one of these failure modes is amplified by inadequate testing, and most of them are detectable through proper test automation before they become production crises.</p> </div>
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<h3 class="elementor-heading-title elementor-size-default">Think about it:</h3> </div>
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Poor data migration (38% of failures) is precisely the problem automated <a href="https://www.datagaps.com/data-reconciliation/" target="_blank" style="color:#1967d2; text-decoration: underline;">data reconciliation</a> catches. When you’re moving hundreds of thousands of material master records, customer masters, vendor records, and BOMs from ECC to S/4HANA, manual spot-checking misses the long tail of data corruption, truncation, and transformation errors.Automated comparison scripts that verify source-to-target integrity field by field, table by table, catch what human eyes cannot. The Complexity Escalation Is Real </div>
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<p>One of the most useful frameworks in Godlan’s research is the business model risk analysis. Implementation risk doesn’t stay flat — it escalates dramatically based on operational complexity:</p> </div>
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<p>• <strong>Make-to-Stock</strong> — Medium risk (65/100) <br />• <strong>Make-to-Order</strong> — High risk (78/100) <br />• <strong>Configure-to-Order</strong> — Very High risk (85/100) <br />•<strong> Engineer-to-Order</strong> — Critical risk (92/100)</p> </div>
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<p>This matters enormously for SAP S/4HANA migrations. The more complex your manufacturing model, the more business logic is encoded in custom code, BOM structures, routing configurations, and pricing rules and the more surface area there is for migration defects.</p> </div>
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Manual testing simply cannot cover this surface area. A configure-to-order
manufacturer might have thousands of configuration variants, each producing different
BOMs and routing sequences. Testing even 5% of those combinations manually would
take months. Automated parameterized tests can cover them in hours. </div>
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<h2 class="elementor-heading-title elementor-size-default">Testing Automation as the Common Denominator </h2> </div>
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<p>Testing automation has emerged as the common denominator across successful ERP implementations especially in complex S/4HANA transformations where speed, scale, and accuracy are critical. In modern implementations, it is most effective when consistently used along with data validation as a standard practice, not an option</p> </div>
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Here’s my thesis: testing automation doesn’t just address one root cause of ERP failure — it systematically mitigates the majority of them. </div>
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<p><strong>Accelerates project timelines</strong>, enabling rapid testing cycles alongside continuous data validation during iterative migrations</p><p><strong>Enables early detection of both system defects and data inconsistencies</strong>, preventing issues from reaching production</p><p><strong>Change management failures?</strong> Automated test suites demonstrate to end users and stakeholders that the new system works. They build confidence through evidence, not promises.</p><p><strong>Data migration failures?</strong> Automated source-to-target validation catches discrepancies at scale before go-live, not after. </p><p><strong>Inexperienced teams?</strong> A well-designed test automation framework provides guardrails. it encodes the business process knowledge that experienced consultants carry in their heads, making it available to the entire project team.<br /><br /><strong>Scope creep?</strong> Automated regression testing gives project leaders the confidence to say “the current scope works” and the data to evaluate whether proposed additions are worth the risk.<br /><strong><br />Over-customization?</strong> Automated tests that validate standard vs. custom behavior help teams identify where customization adds value vs. where it introduces risk. <br /><br />The organizations that beat the 68–73% failure rate aren’t doing anything exotic. They’re investing in structured, automated quality assurance from day one of the project not bolting it on at the end when everything is already on fire.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">The Cost of Inaction vs. The Cost of Automation</h2> </div>
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<p>Let’s put the Godlan numbers in financial context. If the average ERP implementation runs 189–215% over budget, and a mid-market SAP S/4HANA migration typically budgets $5–15 million, the overrun exposure is $9.5–32 million.</p> </div>
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<p>Meanwhile, a well-structured test automation initiative including tool licensing, framework development, and test creation typically runs 5–10% of total project budget and delivers ROI within 4–7 months.</p><p>The Forrester Total Economic Impact study on Tricentis SAP QA solutions documented 403% ROI over three years.</p> </div>
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<p>The asymmetry is stark: spend 5–10% upfront on automation to avoid 100–115% in cost overruns. That’s not a technology decision. That’s a fiduciary one.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">What Should You Do About It?</h2> </div>
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<p>If you’re planning, mid-flight, or recovering from an SAP S/4HANA migration, here’s what the data suggests:</p> </div>
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<span >
1. Treat testing as a first-class workstream, not a phase. </span>
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Testing should start in discovery and run continuously through hypercare. The organizations that succeed embed quality engineering from day one. </p>
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<span >
2. Automate data migration validation early. </span>
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Don't wait until your third mock
migration to discover that 20% of your material masters are corrupted. Build
automated comparison scripts after your first test load. </p>
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3. Invest in end-to-end process automation, not just unit tests. </span>
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The defects that kill ERP go-lives aren't syntax errors — they're cross-module process failures. Order-to-cash, procure-to-pay, plan-to-produce: these need automated end-to
end coverage. </p>
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4. Build the regression suite as a permanent asset. </span>
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S/4HANA updates come faster than ECC. The regression suite you build during migration becomes your insurance policy for every future release. </p>
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5. Choose implementation partners with testing DNA. </span>
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The Godlan research is clear: inexperienced teams are a top-three failure driver. Your implementation partner should have a proven test automation methodology, not a slide deck about one. </p>
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<h2 class="elementor-heading-title elementor-size-default">Conclusion</h2> </div>
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<p>The ERP implementation failure statistics haven’t improved meaningfully in a decade. The industry keeps building billion-dollar systems and testing them with spreadsheets and hope. The organizations that break the pattern are the ones that treat quality as <br />infrastructure – automated, repeatable, and non-negotiable.</p> </div>
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Testing automation with data validation is not optionalit is critical in S/4HANA because: </div>
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<p>• <strong>Systems are real-time and highly integrated,</strong> requiring both automated testing and validated data to ensure accuracy across processes</p><p>• <strong>Errors directly affect business operations,</strong> making it essential to validate both system behavior and the data driving it<br /><br />• <strong>Fixing issues later is costly,</strong> especially when both defects and data inconsistencies are embedded in production<br /><br />• <strong>Clean, validated data combined with automated testing</strong> ensures a successful and stable transformation</p> </div>
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<p>Testing automation with data validation creates a controlled and reliable environment where both system functionality and data accuracy are continuously verified across every stage of the S/4HANA migration. </p> </div>
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<p>“In S/4HANA, testing automation with data validation is not just a technical requirement – it is a business-critical discipline that directly determines the success or failure of the entire implementation”.</p><p>The data is clear. The question is whether you’ll act on it.</p> </div>
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<p><em>Statistics referenced from Godlan’s <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://godlan.com/erp-implementation-failure-statistics/" target="_blank" rel="noopener">2025 ERP Implementation Failure Statistics research</a></span></span>: citing Panorama Consulting Group’s 2025 ERP Report and Gartner analysis.</em></p> </div>
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<p>Also read : <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/blog/sap-material-master-migration-testing-automation-s4hana" target="_blank" rel="noopener">Sap Material Master Migration Testing Automation S4Hana</a></span> </p> </div>
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<h2 class="elementor-heading-title elementor-size-default">FAQ's</h2> </div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="1" aria-controls="elementor-tab-content-1011"><h3 class="eael-accordion-tab-title">Why do last-minute data issues arise in UAT?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1011" class="eael-accordion-content clearfix" data-tab="1" aria-labelledby="faq-1"><p>Because business users identify real-world mismatches not caught in earlier testing.</p></div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="2" aria-controls="elementor-tab-content-1012"><h3 class="eael-accordion-tab-title">Why is incomplete business validation a major mistake in UAT? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1012" class="eael-accordion-content clearfix" data-tab="2" aria-labelledby="faq-1"><p>It allows technically correct but business-incorrect data to move into production.</p></div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="3" aria-controls="elementor-tab-content-1013"><h3 class="eael-accordion-tab-title">Why do critical failures occur post go-live despite successful migrations? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1013" class="eael-accordion-content clearfix" data-tab="3" aria-labelledby="faq-1"><p>Because real transactional loads expose hidden master data inconsistencies.</p></div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="4" aria-controls="elementor-tab-content-1014"><h3 class="eael-accordion-tab-title">Why is dependency on “technical success” instead of “data accuracy” a mistake?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1014" class="eael-accordion-content clearfix" data-tab="4" aria-labelledby="faq-1"><p>Data may load successfully but still fail during actual business execution.</p></div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="5" aria-controls="elementor-tab-content-1015"><h3 class="eael-accordion-tab-title">Why is lack of data consistency across landscapes a common issue? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1015" class="eael-accordion-content clearfix" data-tab="5" aria-labelledby="faq-1"><p>Because changes made in one system (DEV) are not synchronized properly across QA and PRD.</p></div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="6" aria-controls="elementor-tab-content-1016"><h3 class="eael-accordion-tab-title">Why do data inconsistencies originate in the DEV landscape?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1016" class="eael-accordion-content clearfix" data-tab="6" aria-labelledby="faq-1"><p>Because incomplete validation rules in DEV allow incorrect configurations to pass into higher environments.</p></div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="7" aria-controls="elementor-tab-content-1017"><h3 class="eael-accordion-tab-title">Why do migration issues often go unnoticed in QA?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1017" class="eael-accordion-content clearfix" data-tab="7" aria-labelledby="faq-1"><p>Because test data is limited and does not fully simulate real production scenarios.</p></div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="8" aria-controls="elementor-tab-content-1018"><h3 class="eael-accordion-tab-title">Why is pre-migration validation considered a critical success factor?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1018" class="eael-accordion-content clearfix" data-tab="8" aria-labelledby="faq-1"><p>Incorrect data migration leads to faulty transactions, reporting issues, and business disruptions.</p></div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="9" aria-controls="elementor-tab-content-1019"><h3 class="eael-accordion-tab-title">Why is missing reconciliation between legacy and target systems a mistake?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1019" class="eael-accordion-content clearfix" data-tab="9" aria-labelledby="faq-1"><p>It leads to mismatched stock, valuation, and reporting after migration.</p></div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="10" aria-controls="elementor-tab-content-10110"><h3 class="eael-accordion-tab-title">Why is repeated data cleansing ignored across cycles?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-10110" class="eael-accordion-content clearfix" data-tab="10" aria-labelledby="faq-1"><p>Because teams assume initial fixes are sufficient, allowing recurring errors to persist.</p></div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="11" aria-controls="elementor-tab-content-10111"><h3 class="eael-accordion-tab-title">Why is absence of automated validation checks a major gap?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-10111" class="eael-accordion-content clearfix" data-tab="11" aria-labelledby="faq-1"><p>Manual validations miss large-scale inconsistencies in complex datasets.</p></div>
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<p>The post <a href="https://www.datagaps.com/blog/erp-implementation-failures-testing-automation-data-validation/">ERP Implementations Still Fail at Alarming Rates – Here’s Why Testing Automation With Robust Data Validation Is the Fix</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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<title>Testing Automation of Material Master in SAP During Migration to S/4HANA</title>
<link>https://www.datagaps.com/blog/sap-material-master-migration-testing-automation-s4hana/</link>
<comments>https://www.datagaps.com/blog/sap-material-master-migration-testing-automation-s4hana/#respond</comments>
<dc:creator><![CDATA[Adithya Buddhavarapu]]></dc:creator>
<pubDate>Thu, 04 Jun 2026 14:54:51 +0000</pubDate>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[Data Validation]]></category>
<category><![CDATA[ETL Testing]]></category>
<guid isPermaLink="false">https://www.datagaps.com/?p=49939</guid>
<description><![CDATA[<p>As 59% of companies now run S/4HANA (up from 2024), Material Master migration remains a high-risk blind spot—touching procurement, inventory, sales, and finance. This post explains why manual testing can’t scale across hundreds of thousands of records, how S/4HANA’s shift to real-time MATDOC-based stock calculation changes testing requirements, and outlines four test types: data migration […]</p>
<p>The post <a href="https://www.datagaps.com/blog/sap-material-master-migration-testing-automation-s4hana/">Testing Automation of Material Master in SAP During Migration to S/4HANA</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></description>
<content:encoded><![CDATA[ <div data-elementor-type="wp-post" data-elementor-id="49939" class="elementor elementor-49939" data-elementor-post-type="post">
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<p>As 59% of companies now run S/4HANA (up from 2024), Material Master migration remains a high-risk blind spot—touching procurement, inventory, sales, and finance. This post explains why manual testing can’t scale across hundreds of thousands of records, how S/4HANA’s shift to real-time MATDOC-based stock calculation changes testing requirements, and outlines four test types: data migration validation, functional regression, custom code validation, and performance testing. It closes with a five-phase automation framework spanning pre-migration through post-go-live hypercare.</p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li><strong>Material Master is a high-risk, low-visibility migration point</strong> — a single inconsistency in MARA or MARC tables can cascade across procurement, inventory, sales, and finance on day one of go-live.</li><li><strong>S/4HANA fundamentally changes the underlying data model</strong> — stock values are now calculated in real time via MATDOC and CDS views rather than stored statically in MARD/MARC, changing how custom code and reports must be tested.</li><li><strong>Four test types are required for full coverage</strong> — data migration validation, functional regression testing (P2P, O2C, Plan-to-Produce), custom code validation, and performance testing under realistic transaction loads.</li><li><strong>A five-phase framework structures the automation effort</strong> — pre-migration baselining, mock migration cycles, dress rehearsal/mock cutover, go-live validation, and hypercare regression over the following 2-4 weeks.</li></ul> </div>
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<div class="elementor-element elementor-element-fa4d5ac elementor-widget elementor-widget-text-editor" data-id="fa4d5ac" data-element_type="widget" data-e-type="widget" data-widget_type="text-editor.default">
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The clock is ticking. SAP’s 2027 mainstream maintenance deadline for ECC is driving a massive wave of S/4HANA migrations, with <a href="https://www.precisely.com/press-release/new-research-reveals-sap-s-4hana-migration-momentum-despite-ongoing-automation-challenges" target="_blank" style="color:#1967d2; text-decoration: underline;">59% of companies now fully or partially live on S/4HANA as of late 2025 — up 13 points from 2024</a>. Yet one of the most underestimated risks in every migration sits quietly in the background: the Material Master. </div>
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<p>Material Master isn’t glamorous. It doesn’t get keynote stage time. But it touches everything — procurement, inventory, sales, production planning, quality management, finance. A single data inconsistency in your MARA or MARC tables can cascade through your entire supply chain on day one of go-live. And when you’re migrating hundreds of thousands (or millions) of material records from ECC to S/4HANA, manual testing simply doesn’t scale.</p> </div>
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<img loading="lazy" decoding="async" width="1200" height="534" src="https://www.datagaps.com/wp-content/uploads/Material-Master-Integration-Issues-Across-SAP-Modules.jpg" class="attachment-full size-full wp-image-52026" alt="Material Master Integration Issues Across SAP Modules" srcset="https://www.datagaps.com/wp-content/uploads/Material-Master-Integration-Issues-Across-SAP-Modules.jpg 1200w, https://www.datagaps.com/wp-content/uploads/Material-Master-Integration-Issues-Across-SAP-Modules-300x134.jpg 300w, https://www.datagaps.com/wp-content/uploads/Material-Master-Integration-Issues-Across-SAP-Modules-1024x456.jpg 1024w, https://www.datagaps.com/wp-content/uploads/Material-Master-Integration-Issues-Across-SAP-Modules-768x342.jpg 768w" sizes="(max-width: 1200px) 100vw, 1200px" /> </div>
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<p>This blog lays out why Material Master testing automation is non-negotiable during S/4HANA migration, what changes in the data model demand it, and how to approach it practically.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Why Material Master Is the Migration Minefield</h2> </div>
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<img loading="lazy" decoding="async" width="1200" height="572" src="https://www.datagaps.com/wp-content/uploads/Material-Master-Data-Migration-Key-Focus-AreasSimple.jpg" class="attachment-full size-full wp-image-52027" alt="Material Master Data Migration Key Focus Areas(Simple)" srcset="https://www.datagaps.com/wp-content/uploads/Material-Master-Data-Migration-Key-Focus-AreasSimple.jpg 1200w, https://www.datagaps.com/wp-content/uploads/Material-Master-Data-Migration-Key-Focus-AreasSimple-300x143.jpg 300w, https://www.datagaps.com/wp-content/uploads/Material-Master-Data-Migration-Key-Focus-AreasSimple-1024x488.jpg 1024w, https://www.datagaps.com/wp-content/uploads/Material-Master-Data-Migration-Key-Focus-AreasSimple-768x366.jpg 768w" sizes="(max-width: 1200px) 100vw, 1200px" /> </div>
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<p>Material Master is often called a “<strong>migration minefield</strong>” because it is one of the <strong>most complex, interdependent, and business-critical data objects in SAP</strong>. Even small inconsistencies can cascade into major operational issues across procurement, production, sales, and finance.</p> </div>
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<p><span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.sap.com/" target="_blank" rel="noopener">In SAP</a></span></span>, a material master is not a single entity but a collection of multiple views including Basic Data, Sales, Purchasing, MRP, Plant Data, Storage Location, Accounting, Costing, and Quality Management. Each view aligns with specific organizational levels and is supported by different underlying tables, creating a highly distributed data structure. This multi-dimensional complexity makes material master data one of the most sensitive and error-prone areas during migration.</p> </div>
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<h3 class="elementor-heading-title elementor-size-default">During an S/4HANA migration, several things change simultaneously: </h3> </div>
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<p>The data model <strong>has fundamentally shifted in S/4HANA.</strong> While the core Material Master tables (MARA, MARC, MARD, MBEW) still exist, <strong>they are no longer always the primary source of truth for transactional data.</strong> Inventory quantities in tables like <strong>MARD are now derived rather than persistently stored for reporting purposes</strong> when a material document is posted.</p> </div>
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<p>Instead, stock values are <strong>calculated in real time using the MATDOC table and accessed via CDS views.</strong> The old aggregate and index tables <strong>have been removed as part of the S/4HANA data simplification initiative </strong>and replaced by CDS view proxies.</p> </div>
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<p>This means any custom code or reports that read stock fields from MARD or MARC<strong> may now retrieve data through compatibility views or CDS layers rather than direct physical storage,</strong> and <strong>the performance behavior, data accuracy, and read patterns have fundamentally changed.</strong></p> </div>
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The Business Partner migration complicates vendor relationships. </span>
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In ECC, vendor masters lived separately. In S/4HANA, they're merged into the Business Partner framework. Material Master records with vendor-specific info (source lists, purchasing info records, quota arrangements) need their vendor references reconciled against the new BP structure. This is a cross-domain dependency that's easy to miss in isolated Material Master testing. </p>
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Custom fields and Z-tables are everywhere. </span>
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<p class="elementor-icon-box-description">
Most ECC systems are heavily customized. Custom fields appended to MARA, MARC, or MBEW need to be carried forward through the S/4HANA Migration Cockpit (LTMC/LTMOM) using BAPI extension structures like BAPI_TE_E1MARA and BAPI_TE_E1MARC. If the field selection group assignments (T-code OMSR) aren't configured correctly, data simply won't make it to the target database. This is the kind of silent failure that only shows up if you're testing at scale. </p>
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Data quality issues that were tolerable in ECC become blockers in S/4HANA. </span>
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Duplicate materials, incomplete mandatory fields, mismatched units of measure, inconsistent material type assignments — all of these can cause the SUM/DMO conversion process to fail or produce corrupt records. One global food manufacturer found a 20% duplication rate in their Material Master during pre-migration audit. </p>
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<img loading="lazy" decoding="async" width="1200" height="534" src="https://www.datagaps.com/wp-content/uploads/Material-Master-Data-Migration-Top-5-Real-Time-Challenges.jpg" class="attachment-full size-full wp-image-52028" alt="Material Master Data Migration Top 5 Real Time Challenges" srcset="https://www.datagaps.com/wp-content/uploads/Material-Master-Data-Migration-Top-5-Real-Time-Challenges.jpg 1200w, https://www.datagaps.com/wp-content/uploads/Material-Master-Data-Migration-Top-5-Real-Time-Challenges-300x134.jpg 300w, https://www.datagaps.com/wp-content/uploads/Material-Master-Data-Migration-Top-5-Real-Time-Challenges-1024x456.jpg 1024w, https://www.datagaps.com/wp-content/uploads/Material-Master-Data-Migration-Top-5-Real-Time-Challenges-768x342.jpg 768w" sizes="(max-width: 1200px) 100vw, 1200px" /> </div>
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<h2 class="elementor-heading-title elementor-size-default">What Material Master Testing Actually Looks Like </h2> </div>
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<p>Testing Material Master during an S/4HANA migration isn’t a single activity. It spans multiple test types, each of which benefits enormously from automation:</p> </div>
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1. Data Migration Validation </span>
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This is the most obvious layer: verifying that every material record migrated correctly from ECC to S/4HANA. </p>
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<strong>For automated testing, this means</strong> </div>
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<p>• Record count reconciliation across source (ECC) and target (S/4HANA) for every Material Master table — MARA, MARC, MARD, MBEW, MAKT, MVKE, and custom extensions.</p><p>• Field-by-field comparison for a statistically significant sample (or ideally all records), checking that values in every view transferred accurately.</p><p>• Checksum validation helps detect subtle data issues such as truncated descriptions, character encoding problems in the 40-character MAKTX field, and unit of measure mismatches.</p><p>• Cross-referencing material-to-vendor relationships against the migrated Business Partner records.</p><p>• Material type and valuation class validation, ensuring correct account determination and financial postings in S/4HANA.</p><p>• Validation of custom (Z) fields through BAPI extension structures, confirming that enhancements in MARA/MARC are correctly populated in the target system.</p><p>• Integration validation with dependent objects, such as pricing conditions, BOMs, and purchasing info records, to ensure materials function correctly in end-to end processes.</p><p>• Data completeness checks, ensuring mandatory fields required in S/4HANA (e.g., Business Partner linkage, valuation data) are not missing.</p> </div>
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Automating this with tools like Tricentis Tosca, SAP CBTA, or even purpose-built SQL/ABAP comparison scripts can reduce what would be weeks of manual spot checking into hours of comprehensive, repeatable validation. </div>
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2. Functional Regression Testing </span>
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Once the data lands in S/4HANA, does it actually work? Can you create a purchase order for a migrated material? Does MRP run correctly against the migrated plant data? Does the material show up in Fiori apps the way users expect? </div>
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<p>Functional regression for Material Master means automating end-to-end business process scenarios that exercise the migrated data:</p> </div>
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<p>•<strong> Procure-to-Pay (P2P): </strong>Create a purchase requisition → convert to PO → goods receipt → invoice verification, all using migrated materials</p><p><strong>• Order-to-Cash (O2C):</strong> Create a sales order → delivery → billing using migrated materials with sales org data</p><p><strong>• Plan-to-Produce:</strong> Run MRP for migrated materials, verify planned orders, confirm production orders</p><p><strong>• Inventory Management:</strong> Post goods movements (MIGO) for migrated materials, verify stock levels in the new MATDOC-based data model.</p><p><strong>• Account determination validation,</strong> confirming that goods movements and invoices post correctly to the right GL accounts based on valuation class and material type.</p><p><strong>• Cross-module integration validation,</strong> ensuring material data works consistently across MM, SD, PP, and FI without breaks in data flow.</p><p>• <strong>Fiori app validation and user behavior checks,</strong> confirming that migrated materials appear correctly in apps like Manage Product Master Data, Stock Overview, and Create Purchase Order</p><p><strong>• Warehouse and storage integration validation,</strong> ensuring materials function properly with WM/EWM processes, including bin determination and stock placement</p><p><strong>• Tax and compliance validation,</strong> confirming that materials trigger correct tax codes and localization logic across regions</p><p><strong>• Batch management and serial number validation,</strong> ensuring batch-controlled or serialized materials behave correctly in procurement, production, and delivery processes</p><p><strong>• Availability check (ATP) validation,</strong> verifying that stock availability and confirmation logic work correctly with migrated inventory data</p> </div>
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<p>These scenarios should be scripted and parameterized so they can run against hundreds of representative materials, not just the three or four that someone happened to pick for manual testing.</p> </div>
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3. Custom Code Validation </span>
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S/4HANA's Simplification List identifies thousands of changes that affect custom ABAP code. For Material Master specifically, any custom code that directly reads from deprecated tables, uses obsolete function modules, or references fields that have been removed or repurposed needs to be identified and tested. </p>
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Automated custom code scanning (using SAP’s Custom Code Migration app or the ATC checks in Eclipse/ADT) should be followed by automated functional tests of every Z program, Z-report, and user exit that touches Material Master data. The goal is to catch the programs that pass the static code check but still produce wrong results because of the changed data model semantics. </div>
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4. Performance Testing </span>
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This is the layer most teams skip — and pay for dearly after go-live. The shift from statically maintained stock fields to dynamically calculated CDS views means that transactions and reports reading MARD or MARC stock data will behave differently under load. A report that ran in 3 seconds in ECC against pre-aggregated stock tables might take 30 seconds in S/4HANA if the MATDOC table has millions of entries and the CDS view stack isn't optimized. </p>
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<p>Automated performance testing should simulate realistic transaction volumes for key Material Master operations: mass material creation (MM01/API), MRP runs across plant level data, stock overview queries (MMBE), and batch material document postings. Identify the performance cliffs before your users find them.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Building the Automation Framework</h2> </div>
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<p>Here’s a practical approach to structuring Material Master test automation for an S/4HANA migration:</p> </div>
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<strong>Phase 1: Pre-Migration (ECC Side)</strong> Extract baseline data from ECC Material Master
tables. Build automated comparison datasets. Identify the full inventory of custom
fields, custom code, and cross-module dependencies. This is your “source of truth”
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<p><strong>Phase 2: Mock Migration Cycles</strong> Run the migration (via Migration Cockpit or SUM/DMO) in a sandbox environment. Execute the full automated test suite – <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/dataops-suite/" target="_blank" rel="noopener">data validation</a></span></span>, functional regression, custom code validation. Log every discrepancy. Fix, re-migrate, re-test. This cycle typically runs 3–5 times before the data and configuration are clean enough for dress rehearsal.</p> </div>
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<p><strong>Phase 3: Dress Rehearsal / Mock Cutover</strong> Full-scale migration in a production-mirror environment. Complete automated test suite plus performance testing under simulated production load. This is where you validate not just data correctness but also cutover timing and rollback procedures.</p> </div>
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<p><strong>Phase 4: Go-Live Validation</strong> Smoke test suite runs immediately post-cutover. Automated checks confirm record counts, critical material availability, and key transaction execution. Any failures trigger the rollback decision.</p> </div>
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<p><strong>Phase 5: Hypercare Regression</strong> Continuous automated regression during the first 2–4 weeks post-go-live, catching issues that emerge as users interact with migrated data in real business scenarios. SAP delivers S/4HANA updates at a faster cadence than ECC, so the regression suite you build here becomes a permanent asset.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Tool Landscape</h2> </div>
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<p>For <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-migration-testing-automation/" target="_blank" rel="noopener">data migration validation</a></span> specifically, purpose-built SQL comparison scripts (running against both ECC and S/4HANA databases) or tools like Precisely’s Automate Evolve can validate millions of records with checksum and business-rule logic that goes beyond simple row counting.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">The Cost of Not Automating</h2> </div>
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<p>The math is straightforward. A typical mid-size manufacturer has 200,000+ material records across dozens of plants. Each record has 15–20 views. Manual testing of even 1% of records across all views would take months. And a single missed defect – a wrong unit of measure in a purchasing view, a missing MRP profile at one plant – can halt production lines or create procurement chaos on day one.</p> </div>
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The <a href="https://www.precisely.com/press-release/new-research-reveals-sap-s-4hana-migration-momentum-despite-ongoing-automation-challenges" target="_blank" style="color:#1967d2; text-decoration: underline;">2026 ASUG/Precisely survey</a> found that 49% of organizations cite business process change as their top migration barrier, and data quality emerged as a critical but often overlooked challenge.Automation doesn’t just accelerate testing – it’s the only way to achieve the coverage required to de-risk a Material Master migration at enterprise scale. </div>
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<h2 class="elementor-heading-title elementor-size-default">Conclusion</h2> </div>
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<p>Material Master may not command the spotlight in an S/4HANA migration, but its complexity and reach into procurement, inventory, sales, production, and finance make it one of the biggest hidden risks to a successful go-live, especially as the shift to real-time MATDOC-based stock calculation fundamentally changes how custom code, reports, and transactions must be validated; with hundreds of thousands of records and dozens of interdependent views to check, manual testing simply cannot deliver the coverage needed, which is why a structured, automated approach spanning data migration validation, functional regression, custom code checks, and performance testing across all five migration phases — from pre-migration baselining through post-go-live hypercare — is the only way to catch costly defects before they disrupt the business.</p> </div>
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<p>Also read : <span style="color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/blog/erp-implementation-failures-testing-automation-data-validation" target="_blank" rel="noopener"><span style="text-decoration: underline;">Erp Implementation Failures Testing Automation Data Validation</span></a></span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">FAQ's</h2> </div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="1" aria-controls="elementor-tab-content-1241"><h3 class="eael-accordion-tab-title">Why is Material Master validation required before data migration?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1241" class="eael-accordion-content clearfix" data-tab="1" aria-labelledby="faq-1"><p>Validation is required to ensure that only accurate, complete, and consistent data is migrated into the target system. Poor-quality material data leads to downstream failures in procurement, planning, sales, and finance processes.</p></div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="2" aria-controls="elementor-tab-content-1242"><h3 class="eael-accordion-tab-title">Why is cross-module validation (MM, SD, FI) required before migration? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1242" class="eael-accordion-content clearfix" data-tab="2" aria-labelledby="faq-1"><p>Validation is required because material data impacts multiple modules. Even if data appears correct in MM, inconsistencies with SD or FI can result in end-to-end process failures</p></div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="3" aria-controls="elementor-tab-content-1243"><h3 class="eael-accordion-tab-title">Why did MRP fail to generate purchase requisitions for materials? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1243" class="eael-accordion-content clearfix" data-tab="3" aria-labelledby="faq-1"><p>Because procurement type (MARC-BESKZ) was incorrectly assigned in material master, leading to wrong planning behaviour.</p></div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="4" aria-controls="elementor-tab-content-1244"><h3 class="eael-accordion-tab-title">Why is data consistency validation across tables required? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1244" class="eael-accordion-content clearfix" data-tab="4" aria-labelledby="faq-1"><p>Validation is required to maintain referential integrity. Inconsistent data relationships can lead to system errors, incorrect reporting, and transaction failures.</p></div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="5" aria-controls="elementor-tab-content-1245"><h3 class="eael-accordion-tab-title">Why is validation of valuation class and account assignment consistency required during material migration? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1245" class="eael-accordion-content clearfix" data-tab="5" aria-labelledby="faq-1"><p>A batch of raw materials was migrated with an incorrect valuation class (mapped to finished goods accounts). As a result, inventory postings flowed into the wrong GL accounts, causing incorrect cost reporting and audit discrepancies. The issue went unnoticed until month-end financial closing, requiring extensive corrections.</p></div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="6" aria-controls="elementor-tab-content-1246"><h3 class="eael-accordion-tab-title">Why is validation of storage location stock data required before migration?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1246" class="eael-accordion-content clearfix" data-tab="6" aria-labelledby="faq-1"><p>During migration, storage location stock totals were not reconciled with plant-level stock. After go-live, inventory reports showed mismatches, and FI reported stock valuation differences. This resulted in manual adjustments and audit concerns, delaying financial closing</p></div>
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<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="7" aria-controls="elementor-tab-content-1247"><h3 class="eael-accordion-tab-title">Why is validation of automatic account determination (OBYC) required before material master migration?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1247" class="eael-accordion-content clearfix" data-tab="7" aria-labelledby="faq-1"><p>During migration, valuation classes were loaded without validating OBYC configuration. After go-live, goods receipts failed with “Account determination error”, blocking procurement operations. In some cases, postings hit incorrect GL accounts, leading to financial misstatements and manual reclassification efforts.</p></div>
</div><div class="eael-accordion-list">
<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="8" aria-controls="elementor-tab-content-1248"><h3 class="eael-accordion-tab-title">Why did subcontracting fail after material master migration?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1248" class="eael-accordion-content clearfix" data-tab="8" aria-labelledby="faq-1"><p>Because Special Procurement Keys (MARC-SOBSL) were incorrectly migrated, causing MRP to ignore subcontracting requirements.</p></div>
</div><div class="eael-accordion-list">
<div id="faq-1" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="9" aria-controls="elementor-tab-content-1249"><h3 class="eael-accordion-tab-title">Why was batch traceability lost after material master migration?</h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1249" class="eael-accordion-content clearfix" data-tab="9" aria-labelledby="faq-1"><p>Because batch management indicator (MARC-XCHPF) was not properly maintained, breaking material tracking.</p></div>
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<p>Cofounder and Advisor at Datagaps. Deep expertise in enterprise data platforms, BI, and analytics architecture.</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/sap-material-master-migration-testing-automation-s4hana/">Testing Automation of Material Master in SAP During Migration to S/4HANA</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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<title>DataOps Suite Accelerates CI/CD for Data Pipelines Through Testing Automation </title>
<link>https://www.datagaps.com/blog/dataops-suite-accelerates-ci-cd-for-data-pipelines-through-testing-automation/</link>
<comments>https://www.datagaps.com/blog/dataops-suite-accelerates-ci-cd-for-data-pipelines-through-testing-automation/#respond</comments>
<dc:creator><![CDATA[avinash keshri]]></dc:creator>
<pubDate>Tue, 19 May 2026 14:24:00 +0000</pubDate>
<category><![CDATA[Data Validation]]></category>
<category><![CDATA[Dataflow]]></category>
<category><![CDATA[DataOps]]></category>
<guid isPermaLink="false">https://www.datagaps.com/?p=30291</guid>
<description><![CDATA[<p>DataOps Suite can help with your existing CI/CD pipelines and Accelerate CI/CD for Data Pipelines with Testing Automation</p>
<p>The post <a href="https://www.datagaps.com/blog/dataops-suite-accelerates-ci-cd-for-data-pipelines-through-testing-automation/">DataOps Suite Accelerates CI/CD for Data Pipelines Through Testing Automation </a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></description>
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<p>Drawing from a Datagaps webinar, this post explains how DataOps Suite accelerates CI/CD for data pipelines through four capabilities: dual pipeline orchestration across data and code workflows, advanced data observability, deployment automation, and testing automation. It frames catching data bugs early the same way early bug detection works in software development — reducing pressure on data engineers and preventing downstream issues. A case study cites a tech company that achieved a 40% decrease in deployment cycles and a 30% improvement in data and code quality.</p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li><strong>DataOps Suite covers four core CI/CD capabilities</strong> — dual pipeline orchestration, advanced data observability, deployment automation, and testing automation, together streamlining both data and code pipelines.</li><li><strong>Catching data bugs early mirrors software development best practices</strong> — early detection reduces pressure on data engineers, prevents downstream issues, and enables faster resolution.</li><li><strong>Automated testing improves collaboration and reduces MTTR</strong> — running tests continually gives prompt feedback, helps development/testing/operations teams work together more effectively, and shortens mean time to recovery from failures.</li><li><strong>Real-world impact is measurable</strong> — one technology company using DataOps Suite for CI/CD saw a 40% decrease in deployment cycles and a 30% improvement in data and code quality.</li></ul> </div>
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<p>CI/CD for data pipelines means applying continuous integration and continuous deployment principles — automated testing and validation on every change — to data and code, not just application code. The <span style="color: #1967d2;"><a class="underline underline underline-offset-2 decoration-1 decoration-current/40 hover:decoration-current focus:decoration-current" style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/dataops-suite/">DataOps Suite</a></span> brings this to data pipelines, enhancing cloud development’s speed, quality, and reliability.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">What is DataOps?</h2> </div>
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<p><span class="NormalTextRun SCXW148730300 BCX0">As <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.gartner.com/en/information-technology/glossary/dataops" target="_blank" rel="noopener">Gartner</a></span> defines it, “</span><span class="NormalTextRun SpellingErrorV2Themed SCXW148730300 BCX0">DataOps</span><span class="NormalTextRun SCXW148730300 BCX0"> is a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and data consumers across an organization. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW148730300 BCX0">DataOps</span><span class="NormalTextRun SCXW148730300 BCX0"> aims to deliver value faster by creating predictable delivery and change management of data, data models, and related artifacts.”</span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Advantages of Automated Testing in CI/CD Pipelines</h2> </div>
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<th style="padding: 12px; border: 1px solid #ccc;">Advantage</th>
<th style="padding: 12px; border: 1px solid #ccc;">What It Delivers</th>
</tr>
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<td style="padding: 12px; border: 1px solid #ccc;">Prompt Feedback</td>
<td style="padding: 12px; border: 1px solid #ccc;">Provides continuous, fast testing that identifies defects and delivers rapid feedback.</td>
</tr>
<tr style="background: #f8f9fa;">
<td style="padding: 12px; border: 1px solid #ccc;">Improved Collaboration</td>
<td style="padding: 12px; border: 1px solid #ccc;">Enables development, testing, and operations teams to work more efficiently with fewer errors and miscommunications.</td>
</tr>
<tr>
<td style="padding: 12px; border: 1px solid #ccc;">Rapid Deployment</td>
<td style="padding: 12px; border: 1px solid #ccc;">Accelerates build, test, and deployment cycles for applications, data pipelines, and AI models.</td>
</tr>
<tr style="background: #f8f9fa;">
<td style="padding: 12px; border: 1px solid #ccc;">Improved MTTR</td>
<td style="padding: 12px; border: 1px solid #ccc;"><span class="TextRun SCXW148730300 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW148730300 BCX0"><a href="https://en.wikipedia.org/wiki/CI/CD" target="_blank" rel="noopener"><span style="text-decoration: underline; color: #1967d2;">CI/CD</span></a> can support reducing the average time it takes to recover from a probable failure, measured by the MTTR. </span></span><span class="LineBreakBlob BlobObject DragDrop SCXW148730300 BCX0"><span class="SCXW148730300 BCX0"> </span></span></td>
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<tr>
<td style="padding: 12px; border: 1px solid #ccc;">Transparency</td>
<td style="padding: 12px; border: 1px solid #ccc;"><span class="TextRun SCXW148730300 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW148730300 BCX0">Automated <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-observability-tool/" target="_blank" rel="noopener">data observability</a></span> and <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/compliance-solutions/" target="_blank" rel="noopener">compliance</a></span> checks can offer complete transparency by allowing authorized personnel to access updated compliance data immediately. </span></span></td>
</tr>
<tr style="background: #f8f9fa;">
<td style="padding: 12px; border: 1px solid #ccc;">Reduced Manual Effort</td>
<td style="padding: 12px; border: 1px solid #ccc;">Automates repetitive testing tasks, allowing teams to focus on more complex manual validation.</td>
</tr>
<tr>
<td style="padding: 12px; border: 1px solid #ccc;">Boost Data Accuracy</td>
<td style="padding: 12px; border: 1px solid #ccc;">Improves data quality through more precise tests and broader validation coverage.</td>
</tr>
<tr style="background: #f8f9fa;">
<td style="padding: 12px; border: 1px solid #ccc;">Product Consistency</td>
<td style="padding: 12px; border: 1px solid #ccc;">Generates and compares large volumes of test results to ensure consistent application behavior.</td>
</tr>
<tr>
<td style="padding: 12px; border: 1px solid #ccc;">Faster Delivery of High-Quality Software</td>
<td style="padding: 12px; border: 1px solid #ccc;">Uses automated GUI testing to detect and resolve issues earlier, enabling faster software releases.</td>
</tr>
</tbody>
</table> </div>
</div>
</div>
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<h2 class="elementor-heading-title elementor-size-default">Integration and Impact </h2> </div>
</div>
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<p><span class="TextRun SCXW148730300 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW148730300 BCX0">The </span><span class="NormalTextRun SpellingErrorV2Themed SCXW148730300 BCX0">DataOps</span><span class="NormalTextRun SCXW148730300 BCX0"> Suite provides easy-to-use connectivity for data sources and coding environments, thorough testing and validation by development and operational standards, and smooth transitions and upgrades seamlessly integrating into existing CI/CD frameworks. </span></span><span class="LineBreakBlob BlobObject DragDrop SCXW148730300 BCX0"><br class="SCXW148730300 BCX0" /></span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Success Story </h2> </div>
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<p>Customers using DataOps Suite to reconcile data and code pipelines have reported 50% reductions in deployment cycle time and measurable improvements in data and code quality.</p> </div>
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<p>Conclusion</p> </div>
</div>
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<p><span class="TextRun SCXW191101251 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW191101251 BCX0"><span style="color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/dataops-suite/" target="_blank" rel="noopener"><span style="text-decoration: underline;">DataOps Suite</span></a></span> can help with CI/CD to easily streamline the overall cloud development process and </span><span class="NormalTextRun SCXW191101251 BCX0">maintain</span><span class="NormalTextRun SCXW191101251 BCX0"> high data quality and reliability. </span><span class="NormalTextRun SCXW191101251 BCX0">DataGaps</span><span class="NormalTextRun SCXW191101251 BCX0"> recently went live on </span><span class="NormalTextRun SCXW191101251 BCX0">a webinar</span><span class="NormalTextRun SCXW191101251 BCX0"> sharing “Accelerate CI/CD for Data Pipelines with Testing Automation” in which we shared how our Industry Agnostic </span><span class="NormalTextRun SpellingErrorV2Themed SCXW191101251 BCX0">D</span><span class="NormalTextRun SpellingErrorV2Themed SCXW191101251 BCX0">ataOps</span> <span class="NormalTextRun SCXW191101251 BCX0">Suite aids in accelerating data pipelines within CI/CD. They automate repetitive tasks, bring teams together, and give you a clear view of what is happening. This makes your data pipelines run smoother and faster and, most importantly, delivers high-quality data you can trust for your applications and reports. </span></span><span class="LineBreakBlob BlobObject DragDrop SCXW191101251 BCX0"><span class="SCXW191101251 BCX0"> </span><br class="SCXW191101251 BCX0" /></span><span class="LineBreakBlob BlobObject DragDrop SCXW191101251 BCX0"><span class="SCXW191101251 BCX0"> </span><br class="SCXW191101251 BCX0" /></span><span class="TextRun SCXW191101251 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW191101251 BCX0">In CI/CD data pipelines, catching bugs early mimics the benefits of early bug detection in software development. It alleviates pressure on data engineers, fosters higher code quality, prevents downstream issues, and allows for quicker resolution, </span><span class="NormalTextRun SCXW191101251 BCX0">u</span><span class="NormalTextRun SCXW191101251 BCX0">ltimately building</span> <span class="NormalTextRun SCXW191101251 BCX0">a solid foundation for reliable data flow.</span></span><span class="EOP SCXW191101251 BCX0" data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335557856":16777215,"335559738":0,"335559739":240,"335559740":279}"> </span></p> </div>
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<p style="text-align: left;"><span class="TextRun SCXW184731117 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW184731117 BCX0">Hidden Defects in your Data Pipeline?</span></span></p> </div>
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<p><span class="TextRun SCXW184731117 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW184731117 BCX0">Looking for Reduced Downstream Impacts?</span></span></p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/dataops-suite-accelerates-ci-cd-for-data-pipelines-through-testing-automation/">DataOps Suite Accelerates CI/CD for Data Pipelines Through Testing Automation </a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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<title>The Era of Data-Driven Decision Making with Data Reconciliation </title>
<link>https://www.datagaps.com/blog/the-era-of-data-driven-decision-making-with-data-reconciliation/</link>
<dc:creator><![CDATA[avinash keshri]]></dc:creator>
<pubDate>Mon, 04 May 2026 10:38:00 +0000</pubDate>
<category><![CDATA[Data Validation]]></category>
<category><![CDATA[Data and BI Reconciliation]]></category>
<category><![CDATA[Data Reconciliation Tools]]></category>
<guid isPermaLink="false">https://www.datagaps.com/?p=26940</guid>
<description><![CDATA[<p>Explore how Datagaps DataOps Suite revolutionizes Data Reconciliation, ensuring reliable data and accuracy for decision-making in our data-driven world.</p>
<p>The post <a href="https://www.datagaps.com/blog/the-era-of-data-driven-decision-making-with-data-reconciliation/">The Era of Data-Driven Decision Making with Data Reconciliation </a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
]]></description>
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<p>Data reconciliation keeps data trustworthy across systems as businesses lean harder on analytics for strategic decisions. This post covers why it matters, four recurring challenges — data silos, manual processes, growing complexity, and compliance constraints — and how Datagaps’ DataOps Suite automates reconciliation with algorithms and machine learning to catch discrepancies at scale.</p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li><strong>Data reconciliation is foundational to trustworthy decision-making</strong> — it harmonizes information across systems so decision-makers aren’t working from fragmented or conflicting data.</li><li><strong>Data silos are a leading obstacle</strong> — decentralized data management creates barriers to integration, making it harder to align data from disparate sources.</li><li><strong>Manual reconciliation doesn’t scale</strong> — human intervention introduces error risk and slows down comparison of large datasets, directly increasing the chance of flawed decisions.</li><li><strong>Growing data volume and compliance requirements compound the challenge</strong> — as data sources multiply, reconciliation needs sophisticated tooling to keep pace while still meeting privacy and regulatory constraints.</li></ul> </div>
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<p><a class="underline underline underline-offset-2 decoration-1 decoration-current/40 hover:decoration-current focus:decoration-current" href="https://www.datagaps.com/data-reconciliation/"><span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;">Data reconciliation</span></span></a> is the process of comparing data across different systems and sources to confirm it matches, and resolving the discrepancies when it doesn’t. As businesses shift toward data-driven decision-making, this process becomes foundational: it bridges discrepancies and harmonizes information across varied systems and platforms, ensuring the quality and reliability of the data those decisions rest on.</p><p><span data-contrast="none">This meticulous alignment is vital for businesses to leverage their data assets confidently, making informed decisions that drive growth and innovation. As such, data and BI reconciliation is not just a technical necessity but a strategic enabler underpinning the success of modern enterprises in the digital age. It ensures that decision-makers can access consistent and accurate data, enhancing the strategic decision-making process and enabling businesses to navigate the complexities of today’s fast-paced market with agility and precision.</span><span data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335557856":16777215,"335559738":0,"335559739":0,"335559740":279}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">The Importance of Data Reconciliation </h2> </div>
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<p><span data-contrast="none"><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-reconciliation/" target="_blank" rel="noopener">Data reconciliation</a></span> is the unsung hero in data integrity, which is pivotal in ensuring the consistency and reliability of information businesses depend on for making strategic decisions. In today’s data-driven world, where decisions are increasingly guided by analytics and insights, the importance of having accurate and aligned data cannot be overstated. </span><span data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335557856":16777215,"335559738":0,"335559739":0,"335559740":279}"> </span></p><p><span data-contrast="none">Data reconciliation tools ensure that every piece of data, regardless of its source or the system it resides in, tells the same story, thereby eliminating discrepancies and potential conflicts in data interpretation. This harmonization is vital for businesses as it directly impacts their ability to make informed business decisions, forecast trends, understand customer behaviors, and optimize operations. With data reconciliation, companies can avoid making decisions based on fragmented or conflicting information, leading to strategies that may be flawed or misaligned with actual business realities.</span><span data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335557856":16777215,"335559738":0,"335559739":0,"335559740":279}"> </span></p><p><span data-contrast="none">Therefore, <span style="text-decoration: underline;"><span style="color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://en.wikipedia.org/wiki/Data_validation_and_reconciliation" target="_blank" rel="noopener">data reconciliation</a></span></span> acts as a critical foundation for data integrity, underpinning the trustworthiness of data analytics and BI reporting processes and ultimately supporting businesses in their quest to navigate the data complexities of the market with confidence and precision.</span><span data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335557856":16777215,"335559738":0,"335559739":0,"335559740":279}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Challenges in Data Reconciliation</h2> </div>
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<p>The journey towards practical data reconciliation tools is littered with significant obstacles that hamper efficiency and jeopardize data reliability.</p> </div>
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<h3 class="elementor-heading-title elementor-size-default">1. Data Silos</h3> </div>
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<p><span class="TextRun SCXW65428763 BCX8" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun CommentHighlightRest SCXW65428763 BCX8">One of the foremost challenges is the existence of entrenched data silos within organizations. These silos, often a result of decentralized data management practices, create barriers to seamless data integration, leading to inconsistencies and discrepancies across different data sets. This fragmentation complicates the reconciliation process, as aligning data from disparate sources becomes daunting.<br /><br />This isn’t a shrinking problem. According to <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.dataversity.net/articles/data-strategy-trends-in-2025-from-silos-to-unified-enterprise-value/" target="_blank" rel="noopener">DATAVERSITY’s</a></span> 2024 Trends in Data Management survey, 68% of organizations cite data silos as their top data management concern — up 7 percentage points from the year before.<br /></span></span></p> </div>
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<h3 class="elementor-heading-title elementor-size-default">2. Manual Process </h3> </div>
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<p><span class="TextRun SCXW49188251 BCX8" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun CommentHighlightRest SCXW49188251 BCX8">Another significant challenge is the reliance on manual processes for data reconciliation. Manual data testing intervention introduces the risk of human error and significantly slows down the process. The manual comparison of large datasets is time-consuming and prone to inaccuracies, leading to decisions based on flawed data.</span></span></p> </div>
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<h3 class="elementor-heading-title elementor-size-default">3. Volume and Complexity </h3> </div>
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<p><span class="TextRun SCXW191290773 BCX8" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun CommentHighlightRest SCXW191290773 BCX8">T</span><span class="NormalTextRun CommentHighlightRest SCXW191290773 BCX8">he increasing volume and complexity of </span><span class="NormalTextRun CommentHighlightRest SCXW191290773 BCX8">data</span> <span class="NormalTextRun CommentHighlightRest SCXW191290773 BCX8">validation </span><span class="NormalTextRun CommentHighlightRest SCXW191290773 BCX8">pose</span><span class="NormalTextRun CommentHighlightRest SCXW191290773 BCX8"> a challenge. As businesses collect more </span><span class="NormalTextRun CommentHighlightRest SCXW191290773 BCX8">data</span><span class="NormalTextRun CommentHighlightRest SCXW191290773 BCX8"> from a variety of sources, the task of reconciling this </span><span class="NormalTextRun CommentHighlightRest SCXW191290773 BCX8">data</span><span class="NormalTextRun CommentHighlightRest SCXW191290773 BCX8"> becomes increasingly complex. Ensuring the accuracy and consistency of vast </span><span class="NormalTextRun CommentHighlightRest SCXW191290773 BCX8">data</span><span class="NormalTextRun CommentHighlightRest SCXW191290773 BCX8"> requires <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-quality-testing/" target="_blank" rel="noopener">sophisticated tools and methodologies</a></span> to keep pace with the growing </span><span class="NormalTextRun CommentHighlightRest SCXW191290773 BCX8">data</span> <span class="NormalTextRun CommentHighlightRest SCXW191290773 BCX8">validation </span><span class="NormalTextRun CommentHighlightRest SCXW191290773 BCX8">landscape.</span></span><span class="EOP CommentHighlightRest SCXW191290773 BCX8" data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335557856":16777215,"335559738":0,"335559739":0,"335559740":279}"> </span></p> </div>
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<h3 class="elementor-heading-title elementor-size-default">4. Compliance and Data Privacy</h3> </div>
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<p><span data-contrast="none">Furthermore, regulatory <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/compliance-solutions/" target="_blank" rel="noopener">compliance</a></span> and data privacy concerns complicate data reconciliation. Businesses must navigate many regulations that dictate how data is handled, adding further constraints to the reconciliation process.</span></p> </div>
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<p><span data-contrast="none">In summary, while data reconciliation tools are crucial for maintaining data integrity, the path to achieving it is beset with challenges ranging from data silos and manual processes to the sheer volume of data and regulatory compliance issues.</span><span data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335557856":16777215,"335559738":0,"335559739":0,"335559740":279}"> </span></p> </div>
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<p>For a quick understanding of the challenges and why it complicates data reconciliation refer to the table below:</p> </div>
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<td style="padding: 12px; border: 1px solid #ccc;">Data Silos</td>
<td style="padding: 12px; border: 1px solid #ccc;">Decentralized data management creates integration barriers, resulting in inconsistent and disconnected datasets.</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Manual Process</td>
<td style="padding: 12px; border: 1px solid #ccc;">Comparing large datasets manually is time-consuming and increases the likelihood of human error.</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Volume and Complexity</td>
<td style="padding: 12px; border: 1px solid #ccc;">Increasing data volumes and the addition of new data sources make consistent, accurate reconciliation more difficult to maintain.</td>
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<td style="padding: 12px; border: 1px solid #ccc;">Compliance and Data Privacy</td>
<td style="padding: 12px; border: 1px solid #ccc;">Regulatory requirements for data handling and privacy add complexity and constraints to reconciliation processes.</td>
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<h2 class="elementor-heading-title elementor-size-default">DataOps Suite: Revolutionizing Data Reconciliation</h2> </div>
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<p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/dataops-suite/" target="_blank" rel="noopener">Datagaps DataOps Suite</a></span><span data-contrast="none"><span style="color: #0000ff;">,</span> including its exemplary data reconciliation – solution/feature, is a cornerstone for ensuring data accuracy and consistency across organizational data validation ecosystems. This suite elevates the data reconciliation process from a manual, error-prone task to an automated, precise operation. Datagaps’ solution meticulously aligns disparate data sets by focusing on data accuracy and consistency, ensuring uniformity and reliability in business-critical information.</span><span data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335557856":16777215,"335559738":0,"335559739":0,"335559740":279}"> </span></p><p>DataOps Suite harnesses advanced algorithms and machine learning to detect and reconcile discrepancies automatically, replacing the manual comparison process described above with a streamlined workflow. Its capacity to process and analyze large volumes of data from various sources helps keep that data aligned and trustworthy throughout the business decision-making process.</p> </div>
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<p><span class="TextRun SCXW33234057 BCX8" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW33234057 BCX8">In emphasizing </span><span class="NormalTextRun SCXW33234057 BCX8">data</span> <span class="NormalTextRun SCXW33234057 BCX8">validation</span><span class="NormalTextRun SCXW33234057 BCX8">,</span> <span class="NormalTextRun SCXW33234057 BCX8">accuracy and consistency, </span><span class="NormalTextRun SpellingErrorV2Themed SCXW33234057 BCX8">Data</span><span class="NormalTextRun SpellingErrorV2Themed SCXW33234057 BCX8">gaps</span><span class="NormalTextRun SCXW33234057 BCX8">‘ </span><span class="NormalTextRun SpellingErrorV2Themed SCXW33234057 BCX8">Data</span><span class="NormalTextRun SpellingErrorV2Themed SCXW33234057 BCX8">Ops</span><span class="NormalTextRun SCXW33234057 BCX8"> Suite directly addresses the foundational needs of businesses in today’s fast-paced, </span><span class="NormalTextRun SCXW33234057 BCX8">data</span><span class="NormalTextRun SCXW33234057 BCX8">-centric world. It ensures that organizations can rely on their </span><span class="NormalTextRun SCXW33234057 BCX8">data</span><span class="NormalTextRun SCXW33234057 BCX8"> as </span><span class="NormalTextRun SCXW33234057 BCX8">an accurate</span><span class="NormalTextRun SCXW33234057 BCX8"> and consistent asset, paving the way for informed strategic planning and operational excellence.</span></span><span class="EOP SCXW33234057 BCX8" data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335557856":16777215,"335559738":0,"335559739":0,"335559740":279}"> </span></p> </div>
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<p><span style="color: #0000ff;"><span style="text-decoration: underline; color: #1967d2;"><a class="Hyperlink SCXW71602730 BCX8" style="color: #1967d2; text-decoration: underline;" href="https://www.youtube.com/watch?v=5rr8bFMM62U" target="_blank" rel="noreferrer noopener"><span class="TextRun Underlined SCXW71602730 BCX8" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW71602730 BCX8" data-ccp-charstyle="Hyperlink">Witness how DataOps Suite has redefined the landscape of data reconciliation, providing businesses with a robust framework for ensuring data integrity.</span></span></a></span><span class="TextRun SCXW71602730 BCX8" lang="EN-US" style="font-weight: bold;" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW71602730 BCX8"> </span></span><span class="EOP SCXW71602730 BCX8" style="font-weight: bold;" data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335557856":16777215,"335559738":0,"335559739":0,"335559740":279}"> </span></span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Future of Data Reconciliation</h2> </div>
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<p><span data-contrast="none">The future of data reconciliation is bright, marked by relentless innovation and increasingly intelligent solutions like <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/dataops-suite/" target="_blank" rel="noopener">Datagaps DataOps Suite</a></span>. As we look to the horizon, we anticipate groundbreaking advancements that will make data discrepancies in <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-testing-concepts/etl-testing/" target="_blank" rel="noopener">ETL testing</a></span> processes obsolete. The evolution of data reconciliation technologies is set to offer unprecedented precision and efficiency, streamlining how organizations handle their data across various systems. </span></p><p><span data-contrast="none">This progress promises seamless integration of automated testing and data validation, ensuring that data remains an accurate and reliable foundation for decision-making. With tools like the DataOps Suite leading the charge, the future of data reconciliation is geared toward absolute integrity and consistency in data management.</span><span data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335557856":16777215,"335559738":0,"335559739":0,"335559740":279}"> </span></p> </div>
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<h3 class="elementor-heading-title elementor-size-default">Embracing Advanced Solutions for Data Reconciliation</h3> </div>
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<p><span class="TextRun SCXW142531102 BCX8" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW142531102 BCX8">In today’s data-centric world, embracing practical data reconciliation is crucial, transcending mere necessity to become a fundamental requirement. Integrating advanced tools such as </span><span class="NormalTextRun SpellingErrorV2Themed SCXW142531102 BCX8">Datagaps</span><span class="NormalTextRun SCXW142531102 BCX8">‘ </span><span class="NormalTextRun SpellingErrorV2Themed SCXW142531102 BCX8">DataOps</span><span class="NormalTextRun SCXW142531102 BCX8"> Suite marks a significant leap towards ensuring data reliability and integrity. Such innovations fortify the foundation for data-driven decisions and herald a new era of strategic agility and analytical precision. By adopting these sophisticated solutions, businesses can confidently navigate the complexities of modern data landscapes, unlocking new horizons of insight and opportunity. </span><span class="NormalTextRun SCXW142531102 BCX8">In essence, the</span><span class="NormalTextRun SCXW142531102 BCX8"> future of informed decision-making rests on the pillars of advanced data reconciliation tools.</span></span><span class="EOP SCXW142531102 BCX8" data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335557856":16777215,"335559738":0,"335559739":0,"335559740":279}"> </span></p> </div>
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<p><span class="TextRun SCXW23329035 BCX8" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW23329035 BCX8">Elevate </span><span class="NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW23329035 BCX8">your</span> </span><span style="text-decoration: underline; color: #1967d2;"><a class="Hyperlink SCXW23329035 BCX8" style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/request-a-demo/" target="_blank" rel="noreferrer noopener"><span class="TextRun Underlined SCXW23329035 BCX8" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW23329035 BCX8" data-ccp-charstyle="Hyperlink">data reconciliation strategy with </span><span class="NormalTextRun SCXW23329035 BCX8" data-ccp-charstyle="Hyperlink">Datagaps’</span><span class="NormalTextRun SCXW23329035 BCX8" data-ccp-charstyle="Hyperlink"> DataOps Suite</span></span></a></span><span class="TextRun SCXW23329035 BCX8" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW23329035 BCX8"><span style="color: #0000ff;"><strong>.</strong></span> Discover how it can transform your data integrity and consistency approach, propelling your business in a data-driven era.</span></span><span class="EOP SCXW23329035 BCX8" data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335557856":16777215,"335559738":0,"335559739":0,"335559740":279}"> </span></p> </div>
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Avinash Keshri </a>
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<p>Head of Product Marketing at Datagaps and IIM Bangalore alumnus. 13+ years of experience in commercializing AI and data platforms across global markets.</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/the-era-of-data-driven-decision-making-with-data-reconciliation/">The Era of Data-Driven Decision Making with Data Reconciliation </a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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