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<title>Rajesh Kumar, Author at Datagaps | Gen AI-Powered Automated Cloud Data Testing</title>
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<title>Rajesh Kumar, Author at Datagaps | Gen AI-Powered Automated Cloud Data Testing</title>
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<title>Top 3 ETL Testing Tools: How to Choose the Best Tool Clone</title>
<link>https://www.datagaps.com/blog/top-3-etl-testing-tools-clone/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Wed, 23 Apr 2025 08:41:00 +0000</pubDate>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[ETL Testing]]></category>
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<description><![CDATA[<p>ETL Testing refers to the testing, validation, and analysis of the Extraction, Transformation, and Loading Processes that are part of ETL and ELT Pipelines. As ETL testing refers to “Data-in-Motion” Testing, the unit test architecture and principles slightly differ from “Data-at-Rest” Testing (Warehouse/DB Validation).</p>
<p>The post <a href="https://www.datagaps.com/blog/top-3-etl-testing-tools-clone/">Top 3 ETL Testing Tools: How to Choose the Best Tool Clone</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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<h2 class="elementor-heading-title elementor-size-default">What are ETL Testing Tools?</h2> </div>
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<p><span style="text-decoration: underline;"><span style="color: #0000ff; text-decoration: underline;"><a style="color: #0000ff; text-decoration: underline;" href="https://www.datagaps.com/data-validation-etl-testing-tools/" target="_blank" rel="noopener"><span style="color: #1967d2; text-decoration: underline;">ETL testing tools</span></a></span></span> are purpose-built platforms that validate data as it moves through extract, transform, and load pipelines. As data pipelines become more complex, organizations rely on ETL testing tools to verify transformations, detect data issues, and maintain trust in analytics.</p><p>While many teams explore general ETL tools, it is important to distinguish between ETL tools used for data movement and ETL testing tools used for validation and quality assurance.</p><p>Looking for a structured starting point? Check out our <span style="text-decoration: underline;"><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/blog/how-to-validate-etl-testing-checklist/" target="_blank" rel="noopener">ETL Testing Checklist</a></span></span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">When are ETL Testing Tools Used?</h2> </div>
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<p>ETL testing tools are primarily used across two major categories of projects where data accuracy is critical:</p> </div>
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<h3 class="elementor-icon-box-title">
<span >
1. Data Migration Projects </span>
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These involve moving data across systems while ensuring consistency and completeness. Common scenarios include: </p>
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<ul><li>Application migrations</li><li>Cloud migrations such as moving to <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/snowflake-testing-automation/" target="_blank" rel="noopener">Snowflake</a></span></span> or <span style="text-decoration: underline;"><span style="color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/databricks-testing-automation/" target="_blank" rel="noopener">Databricks</a></span></span></li><li>Data warehouse migrations such as Teradata to Redshift or Teradata to Databricks</li></ul> </div>
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<p>In these cases, ETL testing tools and data testing tools are essential for validating large-scale data movement and ensuring no data loss or transformation errors.</p><p>Need help with data migration? Explore our <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><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/data-migration-testing-automation/" target="_blank" rel="noopener">Data Migration Solution page</a>.</span></span></p> </div>
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<span >
2. Data Pipeline Testing </span>
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<p class="elementor-icon-box-description">
These focus on ongoing validation of data pipelines in production environments. Key use cases include: </p>
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<ul><li>Verifying data transformations across pipelines</li><li>Ensuring consistency between source and target systems</li><li>Detecting data quality issues early</li><li>Supporting continuous validation as pipelines scale Here, ETL automation testing tools help teams scale validation, reduce manual effort, and maintain data quality across evolving pipelines.<br /><br />Read more on <span style="text-decoration: underline; 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/data-testing-concepts/etl-testing/" target="_blank" rel="noopener">ETL Testing</a></span> for data pipeline environments.</li></ul> </div>
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<h2 class="elementor-heading-title elementor-size-default">Evaluation Criteria: How We Selected and Assessed ETL Testing Tools?</h2> </div>
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<p class="font-claude-response-body">Modern ETL testing tools are expected to deliver multi-source validation, transformation testing, automation, AI-assisted test creation, and scalability across large data environments. These capabilities formed the basis of our evaluation.</p> </div>
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<p class="font-claude-response-body">Several tools come up frequently in this space. iceDQ, Tosca DI, and Informatica DVO were considered but excluded for specific reasons:</p> </div>
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<p><strong>iceDQ:</strong> The on-premise version of iceDQ lacks several core ETL testing capabilities that enterprise teams typically require. The SaaS version is more feature-complete but not suited for teams that need on-premise deployment.</p><p><strong>Informatica DVO:</strong> Informatica DVO is not a standalone ETL testing tool. It runs only within the Informatica platform, making it irrelevant for teams outside that ecosystem.</p><p><strong>Tosca DI:</strong> While Tosca is a popular choice for application and UI testing, Tosca DI is found to be limited in scope for ETL testing and end-to-end pipeline validation, making it a less suitable option for teams with comprehensive data pipeline testing requirements.</p> </div>
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<p class="font-claude-response-body">ETL testing tools broadly fall into three categories: purpose-built ETL testing platforms, query-based validation tools, and developer-first testing frameworks. This comparison selects one representative from each category to highlight how different approaches address the same validation challenges. In this comparison, Datagaps ETL Validator represents the purpose-built category, QuerySurge the query-based approach, and dbt Tests the developer-first framework.</p> </div>
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<p class="font-claude-response-body">Evaluation was based on nine criteria that reflect real production requirements: core ETL testing capabilities, automation and CI/CD integration, usability and test authoring, data quality and observability, data contracts and governance, testing scope and coverage, enterprise readiness, scalability and performance, and pricing and accessibility.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Top 3 ETL Testing Tools: Detailed Comparison</h2> </div>
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<p>Below is a detailed comparison of three widely considered options: <span style="text-decoration: underline;"><span style="color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-validation-etl-testing-tools/" target="_blank" rel="noopener">Datagaps ETL Validator</a></span></span>, QuerySurge, and dbt tests.</p> </div>
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.etl-table tbody tr.etl-data-row td {
padding: 10px 8px;
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width: 30px;
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</style>
<div class="etl-section">
<div class="etl-legend">
<div class="etl-legend__title">Legend</div>
<div class="etl-legend__item">
<span class="etl-legend__badge etl-legend__badge--star">★</span>
<span>Unique / standout feature</span>
</div>
<div class="etl-legend__item">
<span class="etl-legend__badge etl-legend__badge--check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span>
<span>Strong / full support</span>
</div>
<div class="etl-legend__item">
<span class="etl-legend__badge etl-legend__badge--half">◐</span>
<span>Partial / limited support</span>
</div>
<div class="etl-legend__item">
<span class="etl-legend__badge etl-legend__badge--cross">✘</span>
<span>Not supported / not available</span>
</div>
</div>
<p class="etl-scroll-hint">← Scroll to see full table →</p>
<div class="etl-table-wrapper">
<table class="etl-table">
<colgroup>
<col>
<col>
<col>
<col>
<col>
</colgroup>
<thead>
<tr>
<th>Feature / Capability</th>
<th class="tool-col"><span class="etl-head-nowrap">Datagaps<br>ETL Validator</span></th>
<th class="tool-col"><span class="etl-head-nowrap">QuerySurge</span></th>
<th class="tool-col"><span class="etl-head-nowrap">dbt Tests</span></th>
<th>Verdict</th>
</tr>
</thead>
<tbody>
<tr class="etl-cat-row"><td colspan="5">1. Core ETL Testing</td></tr>
<tr class="etl-data-row">
<td>ETL Test Authoring & Execution</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator and QuerySurge are purpose-built for end-to-end ETL test authoring and execution. dbt Tests define quality checks on dbt models only.</td>
</tr>
<tr class="etl-data-row">
<td>ELT / In-Database Testing</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td>ETL Validator and dbt Tests push validation to the warehouse natively. ETL Validator leads on orchestration across multiple platforms. QuerySurge is partial.</td>
</tr>
<tr class="etl-data-row">
<td>Flat File / CSV Testing</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator and QuerySurge handle flat file and CSV validation natively. dbt Tests are database-only.</td>
</tr>
<tr class="etl-data-row">
<td>Multiple Source / Target Support</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator supports multiple heterogeneous sources and targets in a single test run. QuerySurge supports only a single source-target pair. dbt Tests operate within a single warehouse.</td>
</tr>
<tr class="etl-data-row">
<td>Transformation Validation</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td>ETL Validator adds GenAI-assisted rule authoring across any ecosystem. dbt Tests are strong for validating dbt model outputs. QuerySurge uses SQL-based validation.</td>
</tr>
<tr class="etl-data-row">
<td>Source-to-Target Reconciliation</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator uniquely supports Data Profile reconciliation. QuerySurge covers row counts and aggregations. dbt has no cross-system reconciliation.</td>
</tr>
<tr class="etl-data-row">
<td>Source-to-Report Testing</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator validates the full chain from raw source through to the BI report layer. QuerySurge has limited support. dbt Tests do not reach the reporting layer.</td>
</tr>
<tr class="etl-data-row">
<td>Non-dbt Pipeline Testing</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator and QuerySurge test any pipeline regardless of transformation tool. dbt Tests are locked to dbt models.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">2. Automation & CI/CD</td></tr>
<tr class="etl-data-row">
<td>Automated Regression Testing</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator adds GenAI-assisted test maintenance. QuerySurge offers structured ETL regression automation. dbt Tests re-run on every invocation but have no dedicated regression management.</td>
</tr>
<tr class="etl-data-row">
<td>CI/CD Pipeline Integration</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-star">★</span></td>
<td>dbt Tests have first-class CI/CD integration. ETL Validator and QuerySurge both support CI/CD with broad pipeline trigger options.</td>
</tr>
<tr class="etl-data-row">
<td>Scheduled / Triggered Test Runs</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator and QuerySurge support native scheduling and REST API triggers. dbt Tests depend on dbt Cloud or an external orchestrator such as Airflow.</td>
</tr>
<tr class="etl-data-row">
<td>Test Case Reusability</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td>All three support reusable test definitions. ETL Validator and QuerySurge offer reusable templates via their UIs and test libraries.</td>
</tr>
<tr class="etl-data-row">
<td>Test Maintenance Overhead</td>
<td><span class="sym-text">Low</span></td>
<td><span class="sym-text">Medium</span></td>
<td><span class="sym-text">Medium-High</span></td>
<td>ETL Validator's GenAI-assisted maintenance significantly reduces upkeep as pipelines change. dbt Tests require engineers to update definitions manually for every model or schema change.</td>
</tr>
<tr class="etl-data-row">
<td>Cross-Pipeline Orchestration</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator and QuerySurge orchestrate tests across multiple pipelines in a single run. dbt Tests are scoped to the dbt DAG.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">3. Usability & Test Authoring</td></tr>
<tr class="etl-data-row">
<td>No-Code / Visual Test Builder</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator is the only tool with a drag-and-drop no-code interface for ETL testing. QuerySurge is partial. dbt Tests are written entirely in YAML and SQL.</td>
</tr>
<tr class="etl-data-row">
<td>Ease of Setup</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator and QuerySurge deploy in days. dbt Tests require an existing dbt project before writing a single test.</td>
</tr>
<tr class="etl-data-row">
<td>Business User Accessibility</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator is designed for QA analysts and business users without coding skills. QuerySurge requires SQL knowledge. dbt Tests require proficiency in dbt, YAML, SQL, and version control.</td>
</tr>
<tr class="etl-data-row">
<td>GenAI / AI-Assisted Test Creation</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator generates tests automatically from ETL mapping documents using agentic AI, cutting initial test creation time by over 60%. QuerySurge offers limited GenAI support.</td>
</tr>
<tr class="etl-data-row">
<td>Test Documentation & Visibility</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator provides customisable stakeholder dashboards. QuerySurge offers detailed reporting. dbt generates docs automatically but test visibility for non-engineers is limited.</td>
</tr>
<tr class="etl-data-row">
<td>Learning Curve</td>
<td><span class="sym-text">Low</span></td>
<td><span class="sym-text">Low-Medium</span></td>
<td><span class="sym-text">High</span></td>
<td>ETL Validator is the fastest to productive use for any team profile. dbt Tests require mastery of the full dbt framework.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">4. Data Quality & Observability</td></tr>
<tr class="etl-data-row">
<td>Data Quality Monitoring</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator provides continuous DQ monitoring with scoring and alerting. dbt Tests and QuerySurge run at job execution time only.</td>
</tr>
<tr class="etl-data-row">
<td>Anomaly Detection</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator automatically detects data anomalies across pipelines using AI. Neither QuerySurge nor dbt Tests offer automated anomaly detection.</td>
</tr>
<tr class="etl-data-row">
<td>Data Profiling</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator provides rich data profiling alongside test execution. QuerySurge offers basic profiling. dbt Tests require separate tools such as dbt-profiler or Elementary.</td>
</tr>
<tr class="etl-data-row">
<td>Data Lineage</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-star">★</span></td>
<td>dbt auto-generates column-level lineage across the entire DAG. ETL Validator provides pipeline-level lineage tied to DQ scoring. QuerySurge has no lineage support.</td>
</tr>
<tr class="etl-data-row">
<td>DQ Scoring & Health Dashboards</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator uniquely provides quantified DQ scores and health dashboards across pipelines. Neither QuerySurge nor dbt offer this natively.</td>
</tr>
<tr class="etl-data-row">
<td>Alerting & Notifications</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator and QuerySurge support native alerting on test failures. dbt alerting depends on the orchestration layer.</td>
</tr>
<tr class="etl-data-row">
<td>BI Regression Testing</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator's visual BI report regression testing across Power BI, Tableau, QuickSight, and Oracle Analytics has no equivalent in QuerySurge or dbt.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">5. Data Contracts & Governance</td></tr>
<tr class="etl-data-row">
<td>Data Contracts</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator supports formal data contracts for validating data and schema obligations across pipeline boundaries. dbt has partial support via dbt contracts. QuerySurge has none.</td>
</tr>
<tr class="etl-data-row">
<td>Schema Validation & Drift Detection</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td>ETL Validator and dbt Tests both detect schema drift. QuerySurge offers partial schema validation.</td>
</tr>
<tr class="etl-data-row">
<td>Data Observability Integration</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator provides built-in observability across the full pipeline. dbt integrates with third-party tools. QuerySurge is less observability-focused.</td>
</tr>
<tr class="etl-data-row">
<td>Audit Trails & Compliance Reporting</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator and QuerySurge provide compliance-grade audit trails out of the box. dbt requires significant custom engineering to produce audit-ready reports.</td>
</tr>
<tr class="etl-data-row">
<td>Role-Based Access Control</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator and QuerySurge support enterprise RBAC natively. dbt Cloud offers team-level permissions; dbt Core has no access control layer.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">6. Testing Scope & Coverage</td></tr>
<tr class="etl-data-row">
<td>Mixed-Source Pipelines</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator's Apache Spark engine supports heterogeneous sources including databases, files, and APIs. dbt is warehouse-only.</td>
</tr>
<tr class="etl-data-row">
<td>Legacy System Testing</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator and QuerySurge test pipelines built in any ETL tool including legacy platforms. dbt Tests are not suitable for non-dbt pipelines.</td>
</tr>
<tr class="etl-data-row">
<td>Streaming / Real-Time Data Validation</td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator and QuerySurge have partial streaming support. dbt is mainly a batch transformation tool.</td>
</tr>
<tr class="etl-data-row">
<td>Extensibility</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator provides the capability to add custom plugins using Python, making it highly extensible. QuerySurge and dbt have a fixed set of capabilities.</td>
</tr>
<tr class="etl-data-row">
<td>Test Data Generation</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-cross">✘</span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator uniquely generates synthetic test data for automating pipeline testing, reducing reliance on production data copies.</td>
</tr>
<tr class="etl-data-row">
<td>End-to-End Pipeline Coverage</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator covers ingestion, transformation, loading, and BI reporting. dbt Tests cover only the transformation layer within dbt models.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">7. Enterprise Readiness</td></tr>
<tr class="etl-data-row">
<td>Enterprise Support & SLAs</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator and QuerySurge offer dedicated commercial support with SLAs. dbt Core is open-source with community support only.</td>
</tr>
<tr class="etl-data-row">
<td>On-Premise Deployment</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator and QuerySurge support on-premise deployment. dbt Cloud is SaaS-based.</td>
</tr>
<tr class="etl-data-row">
<td>Multi-Project / Multi-Team Support</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator supports multiple projects in a single deployment with container isolation. QuerySurge supports multi-team setups.</td>
</tr>
<tr class="etl-data-row">
<td>Custom Dashboards for Stakeholders</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-cross">✘</span></td>
<td>ETL Validator uniquely provides customisable stakeholder-facing dashboards for sharing test results and data quality scores.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">8. Scalability & Performance</td></tr>
<tr class="etl-data-row">
<td>Handling Large Data Volumes</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td>ETL Validator's Spark-based execution engine is built for billions of records. QuerySurge is comparatively limited for enterprise-scale data volumes.</td>
</tr>
<tr class="etl-data-row">
<td>Auto-Scaling</td>
<td><span class="sym-star">★</span></td>
<td><span class="sym-partial">◐</span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator has native on-demand auto-scaling. dbt and QuerySurge rely on underlying infrastructure.</td>
</tr>
<tr class="etl-data-row">
<td>Parallel Test Execution</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-partial">◐</span></td>
<td>ETL Validator's Spark engine enables high-parallelism across hundreds of tests simultaneously. dbt test parallelism is warehouse-dependent.</td>
</tr>
<tr class="etl-data-row">
<td>Cloud-Native Deployment</td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td><span class="sym-check"><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/2714.png" alt="✔" class="wp-smiley" style="height: 1em; max-height: 1em;" /></span></td>
<td>All three are cloud-native. ETL Validator supports AKS, EKS, GKE, and Databricks. dbt Cloud is fully managed.</td>
</tr>
<tr class="etl-cat-row"><td colspan="5">9. Pricing & Accessibility</td></tr>
<tr class="etl-data-row">
<td>Licensing Model</td>
<td><span class="sym-text">Commercial</span></td>
<td><span class="sym-text">Commercial</span></td>
<td><span class="sym-text">Open-Source / dbt Cloud</span></td>
<td>dbt Core is free and open-source; dbt Cloud adds a managed commercial tier. The true cost of dbt Tests includes engineering time to build, maintain, and extend.</td>
</tr>
<tr class="etl-data-row">
<td>Relative Cost</td>
<td><span class="sym-text">Best value</span></td>
<td><span class="sym-text">Mid-range</span></td>
<td><span class="sym-text">Free + engineering cost</span></td>
<td>dbt Tests appear free, but the hidden cost is engineering hours to configure and maintain them. ETL Validator delivers broad feature coverage across total cost of ownership.</td>
</tr>
<tr class="etl-data-row">
<td>ETL Vendor Lock-in Risk</td>
<td><span class="sym-text">Low</span></td>
<td><span class="sym-text">Low</span></td>
<td><span class="sym-text">Medium</span></td>
<td>dbt Tests are tightly coupled to the dbt ecosystem. ETL Validator and QuerySurge carry low lock-in risk.</td>
</tr>
<tr class="etl-data-row">
<td>Ideal Team Profile</td>
<td><span class="sym-text">Data Engineering & QA teams of all sizes</span></td>
<td><span class="sym-text">QA Teams</span></td>
<td><span class="sym-text">dbt-native analytics engineers</span></td>
<td>dbt Tests only make sense for teams already running dbt. ETL Validator serves QA, engineering, and business users.</td>
</tr>
</tbody>
</table>
</div>
</div> </div>
</div>
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<h3 class="elementor-heading-title elementor-size-default">Which ETL Testing Tool Should You Choose?</h3> </div>
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<p class="font-claude-response-body">Choosing the right <span style="color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/data-validation-etl-testing-tools/" target="_blank" rel="noopener"><span style="text-decoration: underline;">ETL testing tool</span></a></span> depends on how comprehensive your testing needs are across data pipelines. While multiple tools offer specific capabilities, they differ significantly in scope, flexibility, and coverage.</p> </div>
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<h4 class="elementor-icon-box-title">
<span >
Datagaps ETL Validator </span>
</h4>
<p class="elementor-icon-box-description">
Datagaps ETL Validator provides a more complete approach by supporting end-to-end ETL testing across heterogeneous data sources, including databases, files, APIs and BI layers. It also offers automation, AI-driven test generation, and scalability required for modern data environments. </p>
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<h4 class="elementor-icon-box-title">
<span >
QuerySurge </span>
</h4>
<p class="elementor-icon-box-description">
QuerySurge is effective for SQL-based validation but is largely limited to query-pair comparisons and does not support broader multi-system or end-to-end pipeline testing scenarios. </p>
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<span >
dbt tests </span>
</h4>
<p class="elementor-icon-box-description">
dbt Tests are limited to rule-based data checks within a single data warehouse. They are not built for complete ETL testing and do not address pipeline validation across systems. </p>
</div>
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</div>
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<h3 class="elementor-heading-title elementor-size-default">Our Recommendation for ETL Testing Tool</h3> </div>
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<p><span style="font-weight: 600;"><br />For teams that need comprehensive coverage across the full pipeline, <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; font-weight: 600;" href="https://www.datagaps.com/data-validation-etl-testing-tools/" target="_blank" rel="noopener">Datagaps ETL Validator </a></span>is the clear choice. </span>Where QuerySurge stops at query-pair validation and does not scale effectively for large data volumes, and dbt Tests stay within the warehouse running rule-based checks, Datagaps ETL Validator goes further: across sources, through transformations, and all the way to the BI reporting layer. Built on a Spark-based engine, Datagaps ETL Validator is designed to scale for enterprise data volumes without compromising on performance. It is purpose-built for ETL testing and Datagaps is recognized as a data pipelines test automation specialist in Gartner’s Market Guide for DataOps Tools. If reliable, end-to-end data validation matters to your team, Datagaps ETL Validator is the tool built for that job.</p> </div>
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<p>For teams looking beyond framework-specific validation toward complete pipeline testing and ETL automation, <span style="text-decoration: underline;"><span style="color: #1967d2;"><strong><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/data-validation-etl-testing-tools/" target="_blank" rel="noopener">Datagaps ETL Validator</a></strong></span></span> offers a more comprehensive approach.</p> </div>
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<p><span style="text-decoration: underline;">Disclaimer</span>: The above-mentioned list is purely an outcome of the conversations and feedback received from various industry users in the ETL/Data Warehouse testing space. Any concerns or views can be shared at <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="mailto:contact@datagaps.com">contact@datagaps.com</a></span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Watch ETL Validator in Action with Demo</h2> </div>
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Check out how ETL Validator simplifies ETL Testing, data validation through automation across pipelines from this playlist </div>
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Demo Playlist </span>
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Start your 14-day free trial in our sandbox. Explore and optimize your ETL processes. Start your trial today! </div>
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<h2 class="elementor-heading-title elementor-size-default">Get Started with ETL Validator – An ETL & Data Testing tool</h2> </div>
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<p>The post <a href="https://www.datagaps.com/blog/top-3-etl-testing-tools-clone/">Top 3 ETL Testing Tools: How to Choose the Best Tool Clone</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>Data Reconciliation Best Practices with DataOps Suite ETL Validator</title>
<link>https://www.datagaps.com/blog/data-reconciliation-best-practices/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Fri, 04 Oct 2024 07:24:51 +0000</pubDate>
<category><![CDATA[Data Validation]]></category>
<category><![CDATA[ETL Testing]]></category>
<category><![CDATA[Data Reconciliation]]></category>
<category><![CDATA[Data Reconciliation Best Practices]]></category>
<guid isPermaLink="false">https://www.datagaps.com/?p=33883</guid>
<description><![CDATA[<p>Learn data reconciliation best practices using ETL Validator to ensure data accuracy, integrity, and consistency through automated ETL validation.</p>
<p>The post <a href="https://www.datagaps.com/blog/data-reconciliation-best-practices/">Data Reconciliation Best Practices with DataOps Suite ETL Validator</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 SCXW140759129 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW140759129 BCX0">Data reconciliation is testing data accuracy and </span><span class="NormalTextRun SCXW140759129 BCX0">consist</span><span class="NormalTextRun SCXW140759129 BCX0">ency across two datasets. This process involves best practices such as comparing record counts, data comparison, </span><span class="NormalTextRun SpellingErrorV2Themed SCXW140759129 BCX0">matrics</span><span class="NormalTextRun SCXW140759129 BCX0"> comparison, data sampling and profile test cases, and meta/multiple data comparing queries through tools such as </span><a href="https://www.datagaps.com/etl-validator/" target="_blank" rel="noopener"><span style="text-decoration: underline; color: #1967d2;"><span class="NormalTextRun SpellingErrorV2Themed SCXW140759129 BCX0">DataOps</span></span><span class="NormalTextRun SCXW140759129 BCX0"><span style="text-decoration: underline; color: #1967d2;"> Sute ETL Validator.</span></span></a></span><span class="EOP SCXW140759129 BCX0" data-ccp-props="{"134245418":true,"134245529":true,"201341983":0,"335559739":160,"335559740":279}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">What is data reconciliation in ETL? </h2> </div>
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<p><span class="TextRun SCXW247631065 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW247631065 BCX0">Our <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 tool</a></span> and techniques are designed to detect errors such as data loss, corruption, or duplicate data that may occur during the ETL validation process. The ETL Validator tool ensures that the data warehouse </span><span class="NormalTextRun SCXW247631065 BCX0">remains</span> <span class="NormalTextRun SCXW247631065 BCX0">an accurate</span><span class="NormalTextRun SCXW247631065 BCX0"> and up-to-date reflection of the data from the source to the target. Discuss the best practices for data reconciliation using the ETL Validator and how it enhances the overall ETL (Extract, Transform, Load) process. </span></span><span class="EOP SCXW247631065 BCX0" data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335559738":0,"335559739":0,"335559740":279}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Top 3 Best Practices for Data Reconciliation Using Datagaps Suite ETL Validator Tools </h2> </div>
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<p><span class="TextRun SCXW138756087 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW138756087 BCX0">When working with large-scale data systems, ensuring the accuracy and consistency of data as it moves through different layers of the ETL (Extract, Transform, Load) process is essential. </span><span class="NormalTextRun SpellingErrorV2Themed SCXW138756087 BCX0">Datagaps</span><span class="NormalTextRun SCXW138756087 BCX0"> Suite ETL Validator Tools offer a comprehensive solution for <span style="color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/data-reconciliation/" target="_blank" rel="noopener"><span style="text-decoration: underline;">data reconciliation</span></a></span>, helping to streamline testing, reduce manual errors, and accelerate project timelines. Here are the top three best practices for making the most out of these tools.</span></span><span class="EOP SCXW138756087 BCX0" data-ccp-props="{"201341983":0,"335559739":160,"335559740":279}"> </span></p> </div>
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<h3 class="elementor-heading-title elementor-size-default">1. Use Wizards and Automation to Save Time</h3> </div>
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<p><span data-contrast="none">If you’re dealing with a “lift and shift” scenario—where data is transferred without transformation from one system to another—or a “like-to-like” reconciliation, the ETL Validator’s wizard functionality can drastically reduce the time spent on test case creation. The wizard allows you to generate test cases for hundreds of tables simultaneously, minimizing manual work and freeing up your time to focus on more complex reconciliation tasks.</span><span data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335551550":0,"335551620":0,"335557856":16777215,"335559738":220,"335559739":220,"335559740":279}"> </span></p><p><span data-contrast="none">For example, if your organization is migrating data from an on-premise data warehouse to a cloud-based system like Amazon Redshift or Snowflake, setting up individual test cases for each table could take weeks. By leveraging the wizard, you can automate this task, ensuring every table is checked for consistency while cutting down on configuration time by up to 70%. Not only does this save time, but it also reduces the chance for human error, which is especially crucial when dealing with large datasets.</span><span data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335551550":0,"335551620":0,"335557856":16777215,"335559738":220,"335559739":220,"335559740":279}"> </span></p><p><span data-contrast="none">The wizard or automation is designed for ease of use, so even team members without deep technical expertise can effectively use it. It’s as simple as selecting the source and target systems, specifying the data objects you want to reconcile, and letting the tool handle the rest.</span><span data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335551550":0,"335551620":0,"335557856":16777215,"335559738":220,"335559739":220,"335559740":279}"> </span></p> </div>
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<h3 class="elementor-heading-title elementor-size-default">2. Use Mapping Documents to Automate Test Case Generation and Creation </h3> </div>
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<p><span data-contrast="none">When working with transformed datasets—where data undergoes various transformations as it moves from source to target systems—manually generating test cases can be a tedious and error-prone process. Datagaps Suite offers the ability to utilize mapping documents to create these test cases in bulk. This is particularly useful when you’re reconciling data that has been aggregated, filtered, or otherwise modified, as the tool can automatically generate test cases based on the predefined mappings between source and target datasets.</span><span data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335551550":0,"335551620":0,"335557856":16777215,"335559738":220,"335559739":220,"335559740":279}"> </span></p><p><span data-contrast="none">Let’s say you are working on a complex ETL pipeline where data is transformed through various stages, such as calculating totals, applying business rules, or converting currencies. The mapping documents in Datagaps Suite can be used to track how data fields should align between the source and target systems. Instead of manually writing SQL queries to verify each transformation, you can rely on the tool to auto-generate the necessary test cases, ensuring that the transformed data is accurate without requiring exhaustive manual input.</span><span data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335551550":0,"335551620":0,"335557856":16777215,"335559738":220,"335559739":220,"335559740":279}"> </span></p><p><span data-contrast="none">However, it’s important to review any queries flagged by the application as complex or incorrect. For example, in cases where joins, nested queries, or aggregations are involved, the tool might highlight potential issues. You should review these flagged areas to ensure that your mapping logic is correct and any custom transformations are appropriately handled. By automating this process, you can significantly reduce both the time and complexity involved in data reconciliation.</span></p> </div>
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<h3 class="elementor-heading-title elementor-size-default">3. Define Post-Reconciliation Processes to Automate DataOps and DevOps Workflows</h3> </div>
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<p><span data-contrast="none">While catching inconsistencies is crucial, the real power of the ETL Validator comes into play once <span style="color: #1967d2;"><a style="color: #1967d2;" href="https://en.wikipedia.org/wiki/Data_validation_and_reconciliation" target="_blank" rel="noopener"><span style="text-decoration: underline;">data reconciliation</span></a></span> is complete. It’s not enough to simply flag issues; an efficient post-reconciliation process ensures that errors are addressed, tracked, and resolved systematically. By integrating the ETL Validator with your DataOps and DevOps pipelines, you can automate critical follow-up actions such as saving incorrect records, fixing mismatches, appending missing data, and more.</span><span data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335551550":0,"335551620":0,"335557856":16777215,"335559738":220,"335559739":220,"335559740":279}"> </span></p><p><span data-contrast="none">For example, when a discrepancy is found, you can configure the tool to trigger automatic notifications through email or a messaging platform like Slack. Furthermore, for larger organizations with formal incident management processes, integration with ticketing systems like Jira or ServiceNow can automatically create tickets for data discrepancies. This ensures that any issues are logged, assigned to the appropriate teams, and resolved as part of your regular workflow.</span><span data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335551550":0,"335551620":0,"335557856":16777215,"335559738":220,"335559739":220,"335559740":279}"> </span></p><p><span data-contrast="none">In addition, automating corrective actions based on the type of mismatch can save significant time. For example, if missing records are identified in the target system, a rule can be created to automatically append these records, eliminating the need for manual intervention. By defining clear post-reconciliation processes, you not only improve data quality but also streamline your operational workflows, minimizing downtime and ensuring that the data pipeline continues to function smoothly.</span><span data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335551550":0,"335551620":0,"335557856":16777215,"335559738":220,"335559739":220,"335559740":279}"> </span></p> </div>
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<h3 class="elementor-heading-title elementor-size-default">Top 6 Features of automated cloud data reconciliation tools </h3> </div>
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<p><span class="TextRun SCXW116869508 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW116869508 BCX0">Many aspects of data reconciliation solutions make it easier to </span><span class="NormalTextRun SCXW116869508 BCX0">validate</span><span class="NormalTextRun SCXW116869508 BCX0"> and reconcile data across complex systems.</span></span><span class="EOP SCXW116869508 BCX0" data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335559738":0,"335559739":0,"335559740":279}"> </span></p> </div>
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<p><span class="TextRun SCXW177834163 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW177834163 BCX0">These are some of the main attributes that you can use. </span></span></p> </div>
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Application Reconciliation: </span>
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Provides application-specific reconciliation functions to ensure proper master data flow from systems such as Master Data Management Systems to downstream systems. </p>
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Cross-System Comparison: </span>
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This feature makes it possible to compare operational data side by side across several platforms, guaranteeing that the data is shared between systems like SAP and salesforce. </p>
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Migration Data Validation: </span>
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Tools provide a drag-and-drop interface to assess the successful reconciliation of transferred data when moving data from old systems to new platforms. </p>
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Customized Dashboards and Reports: </span>
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BI Validator Tools integrate with Power BI, Tableau, and back-end systems like Oracle Fusion Applications (OFA) to provide customized dashboards and reports for reconciliation tests. These studies give senior management insights and meet organizational issues. </p>
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Testing Scheduling and Pipeline Automation: </span>
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Reconciliation technologies enable users to plan tests and automate the reconciliation process, guaranteeing that validation tests are executed following every ETL/ELT task. </p>
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Remediation and Re-run Capabilities: </span>
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When there are inconsistencies, these tools allow users to fix problems and revalidate data by providing remediation and re-run alternatives. </p>
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<h3 class="elementor-heading-title elementor-size-default">Data Migration Reconciliation Techniques </h3> </div>
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<p><span class="NormalTextRun SCXW89155484 BCX0">Effective data reconciliation techniques are essential for </span><span class="NormalTextRun SCXW89155484 BCX0">maintaining</span><span class="NormalTextRun SCXW89155484 BCX0"> the reliability and accuracy of data in ETL processes. Here are some essential things you can do to make sure that the migration between systems goes quickly: </span></p> </div>
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1. Field-Level Reconciliation: </span>
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This data reconciliation technique involves comparing individual fields within records to confirm the accuracy and consistency of the data.
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2. Record-Level Reconciliation: </span>
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This technique comprehensively compares all records between the source and target databases. By identifying discrepancies, we can make necessary corrections to guarantee that every record is transferred accurately. </p>
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3. Aggregation Reconciliation: </span>
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This approach involves verifying aggregated values, such as sums and averages, to confirm that the total values are consistent between the source and target databases. </p>
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4. Automated Reconciliation Tools: </span>
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Leveraging automated solutions such as <a href="https://www.datagaps.com/dataops-suite/" style="color: #1967d2"><u>Datagaps DataOps Suite</u></a> can enhance the reconciliation workflow by seamlessly integrating multiple data sources and automating the processes of comparison and correction. </p>
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<p><span class="TextRun SCXW79156814 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW79156814 BCX0"><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/etl-validator/" target="_blank" rel="noopener">ETL Validator</a> </span>is a powerful tool that streamlines data reconciliation processes, reduces manual effort, and ensures compliance with industry regulations. It offers features like pipeline automation, cross-system comparison, and pipeline scheduling, making it more efficient and reliable. By leveraging ETL Validators, organizations can improve data quality and ensure smooth operations.</span></span><span class="EOP SCXW79156814 BCX0" data-ccp-props="{"134233117":false,"134233118":false,"134245418":true,"134245529":true,"201341983":0,"335559738":0,"335559739":0,"335559740":279}"> </span></p> </div>
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<p><span class="LineBreakBlob BlobObject DragDrop SCXW171160723 BCX0">Enhance data accuracy with DataOps Suite ETL Validator</span></p><p>Automate your Data Reconciliation & Low-code, No-code ETL/ELT testing.</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/data-reconciliation-best-practices/">Data Reconciliation Best Practices with DataOps Suite ETL Validator</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>Why FinOps is Essential for Fintech Companies</title>
<link>https://www.datagaps.com/blog/why-finops-is-important-for-fintech-companies/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Fri, 20 Sep 2024 13:32:19 +0000</pubDate>
<category><![CDATA[Data Quality]]></category>
<category><![CDATA[DataOps]]></category>
<category><![CDATA[Analyitcs FinOps]]></category>
<category><![CDATA[Analytics Ops]]></category>
<category><![CDATA[FinOps]]></category>
<category><![CDATA[FinOps Best Practices]]></category>
<category><![CDATA[FinOps Cloud]]></category>
<guid isPermaLink="false">https://www.datagaps.com/?p=33674</guid>
<description><![CDATA[<p>FinOps (financial operations) helps fintech companies manage cloud spending through better collaboration between finance, technology, and business teams. This post covers FinOps’ core benefits — cost optimization, financial accountability, real-time financial management, and enhanced cross-team collaboration — and how it bridges traditionally siloed financial and technical practices. It also explains how Datagaps’ DataOps Suite supports […]</p>
<p>The post <a href="https://www.datagaps.com/blog/why-finops-is-important-for-fintech-companies/">Why FinOps is Essential for Fintech Companies</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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<p>FinOps (financial operations) helps fintech companies manage cloud spending through better collaboration between finance, technology, and business teams. This post covers FinOps’ core benefits — cost optimization, financial accountability, real-time financial management, and enhanced cross-team collaboration — and how it bridges traditionally siloed financial and technical practices. It also explains how Datagaps’ DataOps Suite supports FinOps by automating data reconciliation, validation, and testing, strengthening data governance and ensuring reliable financial data for decision-making.</p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li>FinOps bridges finance and technology — it replaces siloed traditional financial management with a collaborative approach involving finance, operations, and engineering teams working toward shared cost and performance goals.</li><li>Four core benefits define its value for fintech — cost optimization, financial accountability, real-time financial management, and enhanced cross-team collaboration all support better decision-making in a fast-moving industry.</li><li>DataOps Suite automates FinOps-related workflows — by automating data reconciliation, validation, and testing, it reduces manual effort and helps ensure financial data used in FinOps decisions is accurate and reliable.</li><li>Data governance is a key enabler — Datagaps strengthens FinOps practices by providing oversight of data quality and integrity, which is essential for maintaining financial accountability and compliance.</li></ul> </div>
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<h2 class="elementor-heading-title elementor-size-default">What is FinOps in Cloud?</h2> </div>
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<p><span class="TextRun SCXW103207625 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW103207625 BCX0">Fintech comp</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">nies must m</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">n</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">ge costs, </span><span class="NormalTextRun SCXW103207625 BCX0">optimize</span><span class="NormalTextRun SCXW103207625 BCX0"> resources, </span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">nd </span><span class="NormalTextRun SCXW103207625 BCX0">m</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">int</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">in</span><span class="NormalTextRun SCXW103207625 BCX0"> fin</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">nci</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">l </span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">ccount</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">bility while delivering innov</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">tive services. This is where FinOps, or financial operations</span><span class="NormalTextRun SCXW103207625 BCX0">, comes into pl</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">y. <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.finops.org/introduction/what-is-finops/" target="_blank" rel="noopener">FinOps is </a></span></span><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.finops.org/introduction/what-is-finops/"><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0"> fr</span><span class="NormalTextRun SCXW103207625 BCX0">a</span></a></span><span class="NormalTextRun SCXW103207625 BCX0"><a href="https://www.finops.org/introduction/what-is-finops/"><span style="text-decoration: underline; color: #1967d2;">mework</span></a> th</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">t combines </span><span class="NormalTextRun SCXW103207625 BCX0">fin</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">nci</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">l m</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">n</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">gement</span><span class="NormalTextRun SCXW103207625 BCX0">, oper</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">tion</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">l pr</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">ctices, </span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">nd technology to help org</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">niz</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">tions m</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">n</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">ge their </span><span class="NormalTextRun SCXW103207625 BCX0">clo</span><span class="NormalTextRun SCXW103207625 BCX0">u</span><span class="NormalTextRun SCXW103207625 BCX0">d</span><span class="NormalTextRun SCXW103207625 BCX0"> spending efficiently </span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">nd effectively. It brings together fin</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">nce, technology, </span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">nd business te</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">ms to </span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">chieve fin</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">nci</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">l </span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">ccount</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">bility </span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">nd m</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">ximize the v</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">lue of every doll</span><span class="NormalTextRun SCXW103207625 BCX0">a</span><span class="NormalTextRun SCXW103207625 BCX0">r spent on </span><span class="NormalTextRun SCXW103207625 BCX0">clo</span><span class="NormalTextRun SCXW103207625 BCX0">u</span><span class="NormalTextRun SCXW103207625 BCX0">d</span><span class="NormalTextRun SCXW103207625 BCX0"> services. </span></span><span class="EOP SCXW103207625 BCX0" data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335559738":0,"335559739":0,"335559740":279}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Why FinOps is Important in Fintech Companies</h2> </div>
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Aligning Finance with Technology </span>
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<a href="https://www.datagaps.com/dataops-suite/" style="color:#1967d2"><u>FinOps</u></a> is a transformative approach that bridges the gap between financial operations and technological advancements. FinOps ensures that financial practices keep pace with rapid technological changes in the fintech sector, where agility and innovation are paramount. It enables organizations to optimize cloud spending, allocating resources efficiently without compromising innovation. </p>
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The Shift from Traditional Financial Management </span>
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Unlike traditional financial management, which often operates in silos, FinOps promotes a collaborative culture. It brings together finance, operations, and engineering teams to work towards common goals, such as cost optimization, performance improvement, and business value creation. This collaboration is essential in fintech, where financial efficiency and technological excellence go hand in hand. </p>
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<h2 class="elementor-heading-title elementor-size-default">Benefits of FinOps: Why It's Vital for Fintech Companies</h2> </div>
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<img decoding="async" width="1200" height="628" src="https://www.datagaps.com/wp-content/uploads/Benefits-of-FinOps.jpg" class="attachment-full size-full wp-image-33680" alt="benefits of finops in cloud" srcset="https://www.datagaps.com/wp-content/uploads/Benefits-of-FinOps.jpg 1200w, https://www.datagaps.com/wp-content/uploads/Benefits-of-FinOps-300x157.jpg 300w, https://www.datagaps.com/wp-content/uploads/Benefits-of-FinOps-1024x536.jpg 1024w, https://www.datagaps.com/wp-content/uploads/Benefits-of-FinOps-768x402.jpg 768w" sizes="(max-width: 1200px) 100vw, 1200px" /> </div>
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1. Cost Optimization </span>
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One of FinOps's primary benefits is cost optimization. By providing real-time visibility into cloud spending, FinOps allows fintech companies to identify areas where costs can be reduced without affecting performance. This is crucial in a competitive industry where controlling costs can lead to significant competitive advantages. </p>
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2. Financial Accountability </span>
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FinOps ensures that every department within a fintech organization is accountable for its cloud spending. This accountability fosters a culture of financial responsibility, where teams are motivated to optimize their resource usage and make data-driven decisions that align with the company's financial goals. </p>
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3. Real-Time Financial Management </span>
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FinOps enables real-time financial management, allowing fintech companies to monitor their financial performance continuously. This real-time insight is essential for making informed decisions quickly, which is critical in an industry that thrives on agility and responsiveness. </p>
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4. Enhanced Collaboration </span>
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FinOps fosters collaboration between finance, operations, and engineering teams, breaking down silos and ensuring that everyone is working towards the same financial goals. This collaboration leads to better decision-making and a more unified approach to financial management. </p>
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<h2 class="elementor-heading-title elementor-size-default">How does Datagaps DataOps Suite Empower FinOps for Fintech Companies?</h2> </div>
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The Role of DataOps in FinOps </span>
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<a href="https://www.datagaps.com/dataops-suite/" style="color:#1967d2"><u>Datagaps' DataOps Suite</u></a> is a powerful tool that enhances FinOps practices within fintech companies. DataOps Suite allows fintech companies to gain deeper insights into their cloud spending by automating data workflows and ensuring data accuracy. It streamlines data validation and testing, ensuring that financial data is accurate and reliable for real-time decision-making. </p>
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Automation and Efficiency </span>
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The DataOps Suite integrates seamlessly with FinOps strategies, automating repetitive tasks such as data reconciliation, validation, and testing. This automation reduces the manual effort required for financial management, freeing up resources to focus on strategic initiatives. With Datagaps, fintech companies can ensure that their FinOps processes are efficient, accurate, and aligned with their financial goals. </p>
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Enhanced Data Governance </span>
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Incorporating DataOps Suite into FinOps practices also strengthens data governance. By providing comprehensive oversight of data quality and integrity, Datagaps ensures that all financial data used in FinOps is trustworthy. This enhanced governance is crucial for maintaining financial accountability and compliance within fintech companies. </p>
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<h3 class="elementor-heading-title elementor-size-default">Analytics FinOps for Financial Efficiency and Success</h3> </div>
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<p><span class="TextRun SCXW120797502 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW120797502 BCX0"><a href="https://www.datagaps.com/dataops-suite/" target="_blank" rel="noopener"><span style="text-decoration: underline; color: #1967d2;">Analytics FinOps</span></a> offers fintech comp</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">nies </span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0"> powerful fr</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">mework for m</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">n</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">ging their fin</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">nces in </span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0"> world where fin</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">nci</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">l efficiency is critic</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">l to success. By </span><span class="NormalTextRun SCXW120797502 BCX0">optimizing</span><span class="NormalTextRun SCXW120797502 BCX0"> costs, ensuring fin</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">nci</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">l </span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">ccount</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">bility, </span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">nd en</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">bling re</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">l-time </span><span class="NormalTextRun SCXW120797502 BCX0">fin</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">nci</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">l m</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">n</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">gement</span><span class="NormalTextRun SCXW120797502 BCX0">, <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://hu.wikipedia.org/wiki/FinOps" target="_blank" rel="noopener">FinOps</a></span> helps fintech comp</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">nies st</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">y competitive </span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">nd </span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">chieve their business go</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">ls. Embr</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">cing Analytics FinOps is not just </span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0"> str</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">tegic choice; </span><span class="NormalTextRun SCXW120797502 BCX0">It’s</span><span class="NormalTextRun SCXW120797502 BCX0"> necess</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">ry for </span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">ny fintech comp</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">ny th</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">t w</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">nts to thrive in tod</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">y’s f</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">st-p</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">ced fin</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">nci</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">l l</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">ndsc</span><span class="NormalTextRun SCXW120797502 BCX0">a</span><span class="NormalTextRun SCXW120797502 BCX0">pe. </span></span><span class="EOP SCXW120797502 BCX0" data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335559738":0,"335559739":0,"335559740":279}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Ready to transform your financial operations?</h2> </div>
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<p><span class="TextRun SCXW188263925 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW188263925 BCX0">Explore how </span><span class="NormalTextRun SpellingErrorV2Themed SCXW188263925 BCX0">D</span><span class="NormalTextRun SpellingErrorV2Themed SCXW188263925 BCX0">a</span><span class="NormalTextRun SpellingErrorV2Themed SCXW188263925 BCX0">t</span><span class="NormalTextRun SpellingErrorV2Themed SCXW188263925 BCX0">a</span><span class="NormalTextRun SpellingErrorV2Themed SCXW188263925 BCX0">g</span><span class="NormalTextRun SpellingErrorV2Themed SCXW188263925 BCX0">a</span><span class="NormalTextRun SpellingErrorV2Themed SCXW188263925 BCX0">ps</span><span class="NormalTextRun SCXW188263925 BCX0">‘ </span><span class="NormalTextRun SpellingErrorV2Themed SCXW188263925 BCX0">D</span><span class="NormalTextRun SpellingErrorV2Themed SCXW188263925 BCX0">a</span><span class="NormalTextRun SpellingErrorV2Themed SCXW188263925 BCX0">t</span><span class="NormalTextRun SpellingErrorV2Themed SCXW188263925 BCX0">a</span><span class="NormalTextRun SpellingErrorV2Themed SCXW188263925 BCX0">Ops</span><span class="NormalTextRun SCXW188263925 BCX0"> Suite c</span><span class="NormalTextRun SCXW188263925 BCX0">a</span><span class="NormalTextRun SCXW188263925 BCX0">n help you implement FinOps pr</span><span class="NormalTextRun SCXW188263925 BCX0">a</span><span class="NormalTextRun SCXW188263925 BCX0">ctices </span><span class="NormalTextRun SCXW188263925 BCX0">a</span><span class="NormalTextRun SCXW188263925 BCX0">nd </span><span class="NormalTextRun SCXW188263925 BCX0">a</span><span class="NormalTextRun SCXW188263925 BCX0">chieve fin</span><span class="NormalTextRun SCXW188263925 BCX0">a</span><span class="NormalTextRun SCXW188263925 BCX0">nci</span><span class="NormalTextRun SCXW188263925 BCX0">a</span><span class="NormalTextRun SCXW188263925 BCX0">l excellence. Schedule </span><span class="NormalTextRun SCXW188263925 BCX0">a</span><span class="NormalTextRun SCXW188263925 BCX0"> demo tod</span><span class="NormalTextRun SCXW188263925 BCX0">a</span><span class="NormalTextRun SCXW188263925 BCX0">y!</span></span></p> </div>
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<h3 id="faq-heading">FAQs: FinOps and DataOps Suite</h3>
<section class="faq-section" aria-labelledby="faq-heading">
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<details>
<summary>1) What is FinOps and why does it matter for fintech companies?</summary>
<p>
FinOps (Financial Operations) is a collaborative framework that brings together
finance, engineering, and business teams to manage cloud spending effectively.
It helps fintech organizations optimize costs, improve financial accountability,
and make faster, data-driven decisions while scaling cloud operations.
</p>
</details>
<details>
<summary>2) What are the core benefits of adopting FinOps?</summary>
<p>
FinOps enables organizations to optimize cloud costs, improve financial
accountability, gain real-time visibility into spending, and strengthen
collaboration between finance, operations, and engineering teams, replacing
traditional siloed approaches to cloud cost management.
</p>
</details>
<details>
<summary>3) How does DataOps Suite support FinOps practices?</summary>
<p>
DataOps Suite automates data reconciliation, validation, and testing to help
ensure the financial data used for FinOps reporting and decision-making is
accurate, complete, and reliable while reducing manual effort.
</p>
</details>
<details>
<summary>4) Why is data governance important for FinOps success?</summary>
<p>
Effective data governance ensures financial information remains accurate,
consistent, and trustworthy. By maintaining high-quality data, organizations
can make better FinOps decisions, improve accountability, and support
regulatory compliance.
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</details>
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<p>The post <a href="https://www.datagaps.com/blog/why-finops-is-important-for-fintech-companies/">Why FinOps is Essential for Fintech Companies</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>Guide to Power BI Conditional Formatting</title>
<link>https://www.datagaps.com/blog/guide-to-power-bi-conditional-formatting/</link>
<comments>https://www.datagaps.com/blog/guide-to-power-bi-conditional-formatting/#respond</comments>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Wed, 17 Jul 2024 12:04:12 +0000</pubDate>
<category><![CDATA[BI Testing]]></category>
<category><![CDATA[Power BI Testing]]></category>
<category><![CDATA[Power BI]]></category>
<category><![CDATA[Power BI Conditional Formatting]]></category>
<guid isPermaLink="false">https://www.datagaps.com/?p=32336</guid>
<description><![CDATA[<p>Power BI conditional formatting dynamically applies background color, font color, data bars, icons, or web URLs to table and matrix cells based on defined conditions, making reports easier to read and interpret at a glance. This guide covers Power BI’s core features, the four main formatting types, and step-by-step instructions for applying background/font color formatting. […]</p>
<p>The post <a href="https://www.datagaps.com/blog/guide-to-power-bi-conditional-formatting/">Guide to Power BI Conditional Formatting</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>Power BI conditional formatting dynamically applies background color, font color, data bars, icons, or web URLs to table and matrix cells based on defined conditions, making reports easier to read and interpret at a glance. This guide covers Power BI’s core features, the four main formatting types, and step-by-step instructions for applying background/font color formatting. It also includes practical tips—like using DAX measures and high-contrast colors—plus notes that entire-row formatting isn’t supported natively in Power BI.</p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li>Four types of conditional formatting are available — background color, font color, data bars, icons, and web URLs, each helping highlight specific values or trends within tables and matrices.</li><li>Formatting complexity ranges from simple to advanced — conditions can be as basic as a value threshold or require complex DAX expressions for highly specific formatting rules.</li><li>Entire rows cannot be conditionally formatted — Power BI’s conditional formatting applies at the cell/column level, not across a full row, which is an important limitation to plan around.</li><li>Format Painter enables reuse across visuals — conditional formatting can be copied from one table or visual to another, saving setup time when applying consistent formatting across a report.</li></ul> </div>
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<p><span data-contrast="auto">Let’s say you are a marketing sales manager, and you are entrusted with the task of analyzing product sales reports. You must be able to derive meaningful insights from the data to understand how product sales are performing in different regions. This, however, can be challenging if you are employing just Table or Matrix visualizations. Looking up the data in each row to understand what it conveys can be pretty tiresome and difficult. Imagine if you are presented with the same report using different colors, such as orange to black for high to falling revenue figures. You will agree that this way of presenting reports is less straining for both the eyes and the brain. This is where Power BI conditional formatting makes its presence strongly felt. </span></p> </div>
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<p><a href="https://learn.microsoft.com/en-us/power-bi/create-reports/desktop-conditional-table-formatting" target="_blank" rel="noopener"><span class="TextRun SCXW211145480 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="auto"><span class="NormalTextRun SCXW211145480 BCX0">Power BI Conditional Formatting</span></span></a><span class="TextRun SCXW211145480 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="auto"><span class="NormalTextRun SCXW211145480 BCX0"> allows users to dynamically set formatting concerning background color, text color, field values, </span><span class="NormalTextRun SpellingErrorV2Themed SCXW211145480 BCX0">etc</span><span class="NormalTextRun SCXW211145480 BCX0">, to table cells based on certain conditions. This in turn helps highlight specific values or data, in the process, allowing an individual to better </span><span class="NormalTextRun SCXW211145480 BCX0">comprehend</span><span class="NormalTextRun SCXW211145480 BCX0"> the information and significance of a set of cells. All these aid in better reading of reports, which leads to better decision-making in quick time. Moreover, Power BI also </span><span class="NormalTextRun SCXW211145480 BCX0">facilitates</span><span class="NormalTextRun SCXW211145480 BCX0"> the representation of data as data bars, active weblinks and KPI icons. </span></span><span class="EOP SCXW211145480 BCX0" data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335551550":6,"335551620":6,"335559738":0,"335559739":0,"335559740":300}"> </span></p> </div>
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<img loading="lazy" decoding="async" width="955" height="604" src="https://www.datagaps.com/wp-content/uploads/power-bi-conditional-formatting-to-table.png" class="attachment-full size-full wp-image-32340" alt="power-bi-conditional-formatting-to-table matrix" srcset="https://www.datagaps.com/wp-content/uploads/power-bi-conditional-formatting-to-table.png 955w, https://www.datagaps.com/wp-content/uploads/power-bi-conditional-formatting-to-table-300x190.png 300w, https://www.datagaps.com/wp-content/uploads/power-bi-conditional-formatting-to-table-768x486.png 768w" sizes="(max-width: 955px) 100vw, 955px" /> </div>
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<p><span class="TextRun SCXW74070007 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="auto"><span class="NormalTextRun SCXW74070007 BCX0">In this blog, we will learn all about Power </span><span class="NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW74070007 BCX0">BI, conditional</span><span class="NormalTextRun SCXW74070007 BCX0"> formatting, and how we can apply </span></span><span class="TextRun SCXW74070007 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="auto"><span class="NormalTextRun SCXW74070007 BCX0">Power BI conditional formatting </span></span><span class="TextRun SCXW74070007 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="auto"><span class="NormalTextRun SCXW74070007 BCX0">to a range of cells based on certain or different conditions for useful information and insights. We will also share some important tips at the end for a more coherent understanding of the subject. </span></span><span class="EOP SCXW74070007 BCX0" data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335551550":6,"335551620":6,"335559738":0,"335559739":0,"335559740":300}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">What is Power BI?</h2> </div>
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<p><span data-contrast="auto">Power BI is a set of software services, apps, and connectors working in tandem to analyze and visualize raw data from multiple data sources, and turn it into logical, coherent, visually appealing, and actionable insights to help organizations make strategic business decisions in quick time. The data used by this powerful data analytic reporting tool, which brings disparate data sets into <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/blog/slicers-testing-in-power-bi-report/" target="_blank" rel="noopener">reporting dashboards</a></span>, can be stored both in the cloud and on-premises.</span></p> </div>
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<p class="elementor-heading-title elementor-size-default">A few Important Features of Power BI are enumerated as follows: </p> </div>
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Extensive range of captivating visualizations: </span>
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In Power BI, visualizations of raw data are important. The tool consists of a myriad number of detailed and appealing visualizations to help you convert raw data into actionable information. </p>
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Extensive data integration options: </span>
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Power BI comes with the Get Data function which offers users the option to lift both structured and unstructured data from the cloud as well as data present within the organization’s premises to transform, and visualize your data. </p>
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<h2 class="elementor-heading-title elementor-size-default">What is Conditional Formatting? </h2> </div>
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<p><span class="TextRun SCXW252730413 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="auto"><span class="NormalTextRun SCXW252730413 BCX0">There are quite a few popular spreadsheet software programs that allow you to apply custom formatting to cells based on certain conditions. This process, known as conditional formatting, uses color-based formatting to draw attention, emphasize, or put the spotlight on critical metrics, facts, or red flags, making it easier to analyze facts and information. End users can quickly interpret and understand the data with a glance with conditional formatting, as it aids in highlighting crucial information such as sales, budgetary allocations, resource </span><span class="NormalTextRun SCXW252730413 BCX0">utilization</span><span class="NormalTextRun SCXW252730413 BCX0">, and deadlines, among others</span><span class="NormalTextRun SCXW252730413 BCX0">. </span><span class="NormalTextRun SCXW252730413 BCX0">Conditional formatting breaks down a vast array of incomprehensible statistics into easily understandable visuals. Additionally, conditional formatting works as a trustworthy ‘alert’ system that keeps you abreast of </span><span class="NormalTextRun SCXW252730413 BCX0">important facts</span><span class="NormalTextRun SCXW252730413 BCX0">, to make the job of decision-making </span><span class="NormalTextRun SCXW252730413 BCX0">rather simple</span><span class="NormalTextRun SCXW252730413 BCX0">. </span></span><span class="EOP SCXW252730413 BCX0" data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335551550":6,"335551620":6,"335559738":0,"335559739":0,"335559740":300}"> </span></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Power BI conditional matrix formatting </h2> </div>
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<p><span data-contrast="auto">In Microsoft Power BI, conditional formatting is generally employed for tables and matrices. Users can easily apply the following types of formatting to table columns or matrices to determine how reports are displayed:</span><span data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335551550":6,"335551620":6,"335559738":0,"335559739":0,"335559740":300}"> </span></p><ul><li><b><span data-contrast="auto">Background color</span></b><span data-contrast="auto">: users can change the background color of the cell</span></li><li><b><span data-contrast="auto"> Font colo</span></b><span data-contrast="auto">r: users can change the text/ color or font size of the values placed in the cells</span></li><li><b><span data-contrast="auto"> Data bars</span></b><span data-contrast="auto">: represent data as bars of the values contained in the cells </span></li><li><b><span data-contrast="auto"> Icons</span></b><span data-contrast="auto">: represent data values present in cells as icons </span></li><li><b><span data-contrast="auto">Web URL</span></b><span data-contrast="auto">: Convert field value to web URL i.e., clickable links </span></li></ul> </div>
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<h2 class="elementor-heading-title elementor-size-default">Understanding Conditional Formatting in Power BI </h2> </div>
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<p><span class="TextRun SCXW226647045 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="auto"><span class="NormalTextRun SCXW226647045 BCX0">Now, </span><span class="NormalTextRun SCXW226647045 BCX0">let’s</span><span class="NormalTextRun SCXW226647045 BCX0"> see how to apply basic </span></span><span class="TextRun SCXW226647045 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="auto"><span class="NormalTextRun SCXW226647045 BCX0">conditional formatting to a table or matrix</span></span><span class="TextRun SCXW226647045 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="auto"><span class="NormalTextRun SCXW226647045 BCX0"> in Power BI to change the background and font/text color of cells. Here, it is important to understand that the underlying conditions for formatting can vary in complicity, ranging from </span><span class="NormalTextRun AdvancedProofingIssueV2Themed SCXW226647045 BCX0">pretty straightforward</span><span class="NormalTextRun SCXW226647045 BCX0"> ones, i.e., values higher or lower than a specified threshold, to extremely complex ones, requiring DAX expressions with highly specific formatting rules. As mentioned above, you can also add data bars and icons using conditional formatting power BI, which then formats the data based on underlying data changes. Once you select the table/matrix where conditional formatting is to be applied, go to the Visualizations pane, click on the drop-down menu icon, and select conditional formatting. </span></span><span class="EOP SCXW226647045 BCX0" data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335551550":6,"335551620":6,"335559738":0,"335559739":0,"335559740":300}"> </span></p> </div>
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<h3 class="elementor-heading-title elementor-size-default">1. Background color and font color </h3> </div>
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<img loading="lazy" decoding="async" width="1390" height="832" src="https://www.datagaps.com/wp-content/uploads/Power_BI_Conditional_Formatting-concerning_background_color.png" class="attachment-full size-full wp-image-32343" alt="ower_BI_Conditional_Formatting-concerning_background_colors" srcset="https://www.datagaps.com/wp-content/uploads/Power_BI_Conditional_Formatting-concerning_background_color.png 1390w, https://www.datagaps.com/wp-content/uploads/Power_BI_Conditional_Formatting-concerning_background_color-300x180.png 300w, https://www.datagaps.com/wp-content/uploads/Power_BI_Conditional_Formatting-concerning_background_color-1024x613.png 1024w, https://www.datagaps.com/wp-content/uploads/Power_BI_Conditional_Formatting-concerning_background_color-768x460.png 768w" sizes="(max-width: 1390px) 100vw, 1390px" /> </div>
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<p><span class="TextRun SCXW73601557 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="auto"><span class="NormalTextRun SCXW73601557 BCX0">Here, you can change the background color or font color of cells or text based on specified conditions. Background color formatting is </span><span class="NormalTextRun SCXW73601557 BCX0">generally employed</span><span class="NormalTextRun SCXW73601557 BCX0"> to build heat maps, which makes the identification of high and low values easier. </span><span class="NormalTextRun SCXW73601557 BCX0">Note that applying <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">Power BI conditional formatting</a></span> to visuals will reverse any custom background or font color already applied to the cell</span><span class="NormalTextRun SCXW73601557 BCX0">.</span><span class="NormalTextRun SCXW73601557 BCX0"> </span></span><span class="TextRun SCXW73601557 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="auto"><span class="NormalTextRun SCXW73601557 BCX0"> </span></span><span class="EOP SCXW73601557 BCX0" data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335551550":6,"335551620":6,"335559738":0,"335559739":0,"335559740":300}"> </span></p> </div>
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<img loading="lazy" decoding="async" width="351" height="259" src="https://www.datagaps.com/wp-content/uploads/background-color.png" class="attachment-large size-large wp-image-32341" alt="background color fromatting" srcset="https://www.datagaps.com/wp-content/uploads/background-color.png 351w, https://www.datagaps.com/wp-content/uploads/background-color-300x221.png 300w" sizes="(max-width: 351px) 100vw, 351px" /> </div>
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<h3 class="elementor-heading-title elementor-size-default">2. Data Bars</h3> </div>
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<img loading="lazy" decoding="async" width="291" height="448" src="https://www.datagaps.com/wp-content/uploads/data-bar-margin-profit.png" class="attachment-large size-large wp-image-32342" alt="profit margin" srcset="https://www.datagaps.com/wp-content/uploads/data-bar-margin-profit.png 291w, https://www.datagaps.com/wp-content/uploads/data-bar-margin-profit-195x300.png 195w" sizes="(max-width: 291px) 100vw, 291px" /> </div>
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<div class="elementor-element elementor-element-7f4bd6f elementor-widget elementor-widget-text-editor" data-id="7f4bd6f" data-element_type="widget" data-e-type="widget" data-widget_type="text-editor.default">
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<p><span class="TextRun SCXW199288560 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="auto"><span class="NormalTextRun SCXW199288560 BCX0">A Data bar is a horizontal bar within the cell used to </span><span class="NormalTextRun SCXW199288560 BCX0">represent</span><span class="NormalTextRun SCXW199288560 BCX0"> the value of a cell as compared to other cell values. </span></span></p> </div>
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<div class="elementor-element elementor-element-43ac386 elementor-widget elementor-widget-heading" data-id="43ac386" data-element_type="widget" data-e-type="widget" data-widget_type="heading.default">
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<h3 class="elementor-heading-title elementor-size-default">3. Icons
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<p>Icons allow you to add symbols or icons in proximity to your data points.</p> </div>
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<div class="elementor-element elementor-element-87a6fd5 elementor-widget elementor-widget-heading" data-id="87a6fd5" data-element_type="widget" data-e-type="widget" data-widget_type="heading.default">
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<h3 class="elementor-heading-title elementor-size-default">4. Web URLs </h3> </div>
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<div class="elementor-element elementor-element-e9a93b1 elementor-widget elementor-widget-text-editor" data-id="e9a93b1" data-element_type="widget" data-e-type="widget" data-widget_type="text-editor.default">
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<p><span class="TextRun SCXW14308119 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="auto"><span class="NormalTextRun SCXW14308119 BCX0">This type of </span></span><span class="TextRun SCXW14308119 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="auto"><span class="NormalTextRun SCXW14308119 BCX0">conditional formatting in Power BI</span></span><span class="TextRun SCXW14308119 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="auto"><span class="NormalTextRun SCXW14308119 BCX0"> allows you to create hyperlinks for </span><span class="NormalTextRun SCXW14308119 BCX0">additional</span><span class="NormalTextRun SCXW14308119 BCX0"> information from external sources.</span></span><span class="EOP SCXW14308119 BCX0" data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335551550":6,"335551620":6,"335559738":0,"335559739":0,"335559740":300}"> </span></p> </div>
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<div class="elementor-element elementor-element-69ec6ba elementor-widget elementor-widget-text-editor" data-id="69ec6ba" data-element_type="widget" data-e-type="widget" data-widget_type="text-editor.default">
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<p><span class="TextRun SCXW173960935 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="auto"><span class="NormalTextRun SCXW173960935 BCX0">Conditional formatting in Power BI </span></span><span class="TextRun SCXW173960935 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="auto"><span class="NormalTextRun SCXW173960935 BCX0">helps apply custom formatting to cells which makes the report visually appealing and easy to comprehend</span><span class="NormalTextRun SCXW173960935 BCX0">. </span><span class="NormalTextRun SCXW173960935 BCX0">Conditional formatting specifies how colors are displayed in a report, which makes it </span><span class="NormalTextRun SCXW173960935 BCX0">rather easy</span><span class="NormalTextRun SCXW173960935 BCX0"> to highlight important points, trends, resource </span><span class="NormalTextRun SCXW173960935 BCX0">utilization</span><span class="NormalTextRun SCXW173960935 BCX0">, ROI, and other critical metrics, thus making it easier to analyze and </span><span class="NormalTextRun SCXW173960935 BCX0">utilize</span><span class="NormalTextRun SCXW173960935 BCX0"> data for strategic decision-making. At </span><span class="NormalTextRun SpellingErrorV2Themed SCXW173960935 BCX0">Datagaps</span><span class="NormalTextRun SCXW173960935 BCX0">, we </span><span class="NormalTextRun SCXW173960935 BCX0">pro</span><span class="NormalTextRun SCXW173960935 BCX0">vide a consistent & reliable solution for all your Power BI conditional formatting needs. With </span><span class="NormalTextRun SCXW173960935 BCX0">a high level</span><span class="NormalTextRun SCXW173960935 BCX0"> of scalability and fault-tolerant architecture, we help you unlock the full potential of </span></span><span style="color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/bi-testing-tools/" target="_blank" rel="noopener"><span style="text-decoration: underline;"><span class="TextRun SCXW173960935 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="auto"><span class="NormalTextRun SCXW173960935 BCX0">Power BI conditional formatting</span></span></span></a></span><span class="TextRun SCXW173960935 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="auto"><span class="NormalTextRun SCXW173960935 BCX0"> to draw insightful analysis for quick business decision-making</span><span class="NormalTextRun CommentStart CommentHighlightPipeRestV2 CommentHighlightRest SCXW173960935 BCX0">.</span></span></p> </div>
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<h3 class="elementor-heading-title elementor-size-default">FAQ's</h3> </div>
</div>
<div class="elementor-element elementor-element-6ce99fc3 elementor-widget elementor-widget-eael-adv-accordion" data-id="6ce99fc3" data-element_type="widget" data-e-type="widget" id="faq-14" data-widget_type="eael-adv-accordion.default">
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<div class="eael-adv-accordion" id="eael-adv-accordion-6ce99fc3" data-scroll-on-click="no" data-scroll-speed="300" data-accordion-id="6ce99fc3" data-accordion-type="accordion" data-toogle-speed="300">
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<div id="faq-1" class="elementor-tab-title eael-accordion-header active-default" tabindex="0" data-tab="1" aria-controls="elementor-tab-content-1821"><h3 class="eael-accordion-tab-title">How can Power BI conditional formatting be copied? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1821" class="eael-accordion-content clearfix active-default" data-tab="1" aria-labelledby="faq-1"><p><span class="TextRun SCXW247888229 BCX0"><span class="NormalTextRun SCXW247888229 BCX0">Users can copy conditional formatting in Power BI from one table or visual to another with the help of Format Painter.</span></span><span class="EOP SCXW247888229 BCX0"> </span></p></div>
</div><div class="eael-accordion-list">
<div id="faq-3" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="2" aria-controls="elementor-tab-content-1822"><h3 class="eael-accordion-tab-title">How can conditional formatting in Power BI be removed? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1822" class="eael-accordion-content clearfix" data-tab="2" aria-labelledby="faq-3"><p><span class="TextRun SCXW223318688 BCX0"><span class="NormalTextRun SCXW223318688 BCX0">You can remove conditional formatting in Power BI by clicking on the drop-down icon </span><span class="NormalTextRun SCXW223318688 BCX0">and choosing</span><span class="NormalTextRun SCXW223318688 BCX0"> “Remove conditional formatting.”</span></span><span class="EOP SCXW223318688 BCX0"> </span></p></div>
</div><div class="eael-accordion-list">
<div id="faq-9" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="3" aria-controls="elementor-tab-content-1823"><h3 class="eael-accordion-tab-title">Can I format an entire row in Power BI conditional formatting? </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1823" class="eael-accordion-content clearfix" data-tab="3" aria-labelledby="faq-9"><p><span class="TextRun SCXW6783992 BCX0"><span class="NormalTextRun SCXW6783992 BCX0">No. Power BI does not allow you to conditionally format an entire row in Power BI.</span></span><span class="EOP SCXW6783992 BCX0"> </span></p></div>
</div><div class="eael-accordion-list">
<div id="faq-10" class="elementor-tab-title eael-accordion-header" tabindex="0" data-tab="4" aria-controls="elementor-tab-content-1824"><h3 class="eael-accordion-tab-title">Power BI conditional formatting tips </h3><i aria-hidden="true" class="fa-toggle fas fa-angle-right"></i></div><div id="elementor-tab-content-1824" class="eael-accordion-content clearfix" data-tab="4" aria-labelledby="faq-10"><p><span>Create custom DAX measures to build logic for tedious conditional formatting in Power BI. </span><span> </span></p><p><span>Take care to choose high-contrast colors so that everyone, including people with disabilities, can read the reports easily. </span><span> </span></p><p><span>Lay emphasis on important trends, data, and patterns to make your reports visually appealing and easy to read. </span><span> </span></p></div>
</div></div> </div>
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Rajesh Kumar A </a>
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<p>Digital Marketing Manager at Datagaps. Drives data-driven growth through content, performance campaigns, and marketing technology.</p> </div>
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<a href="https://www.linkedin.com/in/s-p-s-murthy-akella-84469922/" >
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Director, Technology Strategy, Datagaps </p>
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<p>Director of Technology Strategy at Datagaps. Business solutions architect and Certified Scrum Master in data engineering, responsible AI, and ML across BFSI, telecom, aviation, and energy.</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/guide-to-power-bi-conditional-formatting/">Guide to Power BI Conditional Formatting</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 Feature Updates – Version 2023.4.0.0</title>
<link>https://www.datagaps.com/blog/dataops-suite-feature-updates-version-2023-4-0-0/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Sat, 06 Jan 2024 11:32:19 +0000</pubDate>
<category><![CDATA[BI Testing]]></category>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[Data Quality]]></category>
<category><![CDATA[DataOps]]></category>
<category><![CDATA[DevOps]]></category>
<category><![CDATA[ETL Testing]]></category>
<category><![CDATA[Tableau Testing]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=25392</guid>
<description><![CDATA[<p>Datagaps Inc. announced its latest product feature updates for the DataOps Suite Version 2023.4.0.0 – The theme of this update is Integration and Easy to apply Analysis. If you are interested in getting a demo of DataOps Suite, you can fill your details here.</p>
<p>The post <a href="https://www.datagaps.com/blog/dataops-suite-feature-updates-version-2023-4-0-0/">DataOps Suite Feature Updates – Version 2023.4.0.0</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>DataOps Suite 4.0.0 centers on GIT Integration, letting teams manage ETL/ELT code, BI reports, and test cases in a single Git repository across providers like Azure Repos and Bitbucket for version control, rollback, and CI/CD-enabled collaboration. The release also expands BI testing with Tableau Upgrade, Power BI Regression, and Power BI Upgrade comparisons, introduces Fixed and Delta deviation techniques for anomaly detection, and adds Microsoft Teams integration for real-time notifications and status updates across the data team.</p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li><strong>GIT Integration enables CI/CD for DataOps testing</strong> — teams can now store and version dataflows, ETL/ELT code, BI reports, and test cases in a single Git repository (Azure Repos, Bitbucket), with rollback and multi-user collaboration built in.</li><li><strong>BI regression and upgrade testing expands beyond Tableau</strong> — the suite now supports Tableau Upgrade, Power BI Regression, and Power BI Upgrade, comparing baseline vs. live dashboards for both text changes and pixel-level appearance shifts.</li><li><strong>Two new anomaly detection techniques improve data quality monitoring</strong> — Fixed deviation flags data points that cross a set threshold, while Delta deviation flags data points that shift too much from the prior value.</li><li><strong>Microsoft Teams integration strengthens collaboration</strong> — real-time notifications and status updates now surface directly in Teams, keeping the data team informed without switching tools.</li></ul> </div>
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<p>If you are interested in getting a <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/request-demo/" target="_blank" rel="noopener">demo</a></span> of DataOps Suite, <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/request-demo/" target="_blank" rel="noopener">you can fill your details here.</a></span></p> </div>
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<h3 class="elementor-heading-title elementor-size-default">DataOps Suite Feature Updates – Version 2023.4.0.0</h3> </div>
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<h2 class="elementor-heading-title elementor-size-default">1. Enable CI/CD for your DataOps testing with GIT integration</h2> </div>
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<img loading="lazy" decoding="async" width="980" height="549" src="https://www.datagaps.com/wp-content/uploads/DataOps-Suite-with-GIT-providers-Azure-DevOps-and-Bitbucket.webp" class="attachment-full size-full wp-image-25396" alt="" srcset="https://www.datagaps.com/wp-content/uploads/DataOps-Suite-with-GIT-providers-Azure-DevOps-and-Bitbucket.webp 980w, https://www.datagaps.com/wp-content/uploads/DataOps-Suite-with-GIT-providers-Azure-DevOps-and-Bitbucket-300x168.webp 300w, https://www.datagaps.com/wp-content/uploads/DataOps-Suite-with-GIT-providers-Azure-DevOps-and-Bitbucket-768x430.webp 768w" sizes="(max-width: 980px) 100vw, 980px" /> </div>
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<center>DataOps Suite with GIT providers Azure DevOps and Bitbucket</center> </div>
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<p>DataOps Suite supports various GIT providers like <strong>Azure Repos</strong> and <strong>Bitbucket</strong> to store, organize, manage, and track the dataflows of different versions, making it easier to roll back to previous versions if necessary. They provide a centralized repository to manage the dataflows in one place and make it easy for multiple team members to collaborate on maintaining the dataflows.</p><p><span class="fontSizeLarge">GIT Integration empowers the Data team to manage their ETL/ELT code, BI reports/workbooks and test cases in a single GIT repository.</span></p><h5><a href="https://help.datagaps.com/articles/#!dataops-suite/git-integration" target="_blank" rel="noopener"><span style="text-decoration: underline;">Learn More – GIT Integration</span></a></h5> </div>
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<h2 class="elementor-heading-title elementor-size-default">2. Improved Testing Capabilities for Tableau & Power BI Reports</h2> </div>
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DataOps Suite 4.0.0 elevates BI Analytics with enhanced features, catering to both Tableau and Power BI users.
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<img loading="lazy" decoding="async" width="980" height="549" src="https://www.datagaps.com/wp-content/uploads/Improved-features-of-BI-Analytics.webp" class="attachment-full size-full wp-image-25400" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Improved-features-of-BI-Analytics.webp 980w, https://www.datagaps.com/wp-content/uploads/Improved-features-of-BI-Analytics-300x168.webp 300w, https://www.datagaps.com/wp-content/uploads/Improved-features-of-BI-Analytics-768x430.webp 768w" sizes="(max-width: 980px) 100vw, 980px" /> </div>
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<center>Improved features of BI Analytics</center>
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<div class="et_pb_module et_pb_text et_pb_text_9 et_pb_text_align_left et_pb_bg_layout_light"><div class="et_pb_text_inner"><p>In addition to Tableau Regression, DataOps Suite now supports <strong>Tableau Upgrade</strong>, <strong>Power BI Regression</strong>, and <strong>Power BI upgrade</strong>.</p><p><a href="https://help.datagaps.com/articles/#!dataops-suite/tableau-upgrade" target="_blank" rel="noopener"><span style="text-decoration: underline;">Table</span><u>au Upgrade</u></a> <span class="fontSizeLarge">compares the views and worksheets in the newer version with the same views and the worksheets in the older version in the same or different Tableau environments to ensure that no data or formatting has been lost.</span></p><p><span class="fontSizeLarge"><a href="https://help.datagaps.com/articles/#!dataops-suite/power-bi-regression" target="_blank" rel="noopener"><span style="text-decoration: underline;">Power BI Regression</span></a> performs a </span>regression testing of the Power BI dashboards by comparing the baseline version of the dashboard (PDF) with the live version. This comparison can be done for ‘text’ which highlights the changes in text even if the text is shifted by a little and ‘appearance’ which performs the pixel-to-pixel comparison and highlights text, chart, etc.,</p><p><a href="https://help.datagaps.com/articles/#!dataops-suite/power-bi-regression" target="_blank" rel="noopener"><span style="text-decoration: underline;">Power BI Upgrade</span></a>c<span class="fontSizeLarge">ompares the PDF report in the newer version with the same PDF report in the older version in the same or different Power BI environments to ensure that no data or formatting has been lost.</span></p><p><span class="fontSizeLarge">By automating the regression and migration testing, these components can save organizations time and effort, and help them to ensure that their dashboards and reports are trustworthy and working as expected.</span></p></div></div><div class="et_pb_module et_pb_text et_pb_text_10 et_pb_text_align_left et_pb_bg_layout_light"><div class="et_pb_text_inner"><h5><a href="https://help.datagaps.com/articles/#!dataops-suite/bi-analytics" target="_blank" rel="noopener"><u>Learn More</u></a></h5></div></div> </div>
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<h2 class="elementor-heading-title elementor-size-default">3. “Data Observability” Enhancements</h2> </div>
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<p>DataOps Suite introduces two innovative detection techniques—Fixed and Delta deviation—for identifying data anomalies.</p> </div>
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<img loading="lazy" decoding="async" width="980" height="549" src="https://www.datagaps.com/wp-content/uploads/Data-Observability-enhancements.webp" class="attachment-full size-full wp-image-25404" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Data-Observability-enhancements.webp 980w, https://www.datagaps.com/wp-content/uploads/Data-Observability-enhancements-300x168.webp 300w, https://www.datagaps.com/wp-content/uploads/Data-Observability-enhancements-768x430.webp 768w" sizes="(max-width: 980px) 100vw, 980px" /> </div>
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<center>Data Anomaly techniques</center>
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<div class="et_pb_module et_pb_text et_pb_text_14 et_pb_text_align_left et_pb_bg_layout_light"><div class="et_pb_text_inner"><p>DataOps Suite has introduced two new detection techniques, <strong>Fixed and Delta deviation,</strong> for reporting the data anomalies.</p><p><strong>Fixed deviation</strong> is a simple technique where we can set a fixed threshold, and if any data point deviates from the fixed threshold, it is flagged as an anomaly.</p><p><strong>Delta deviation</strong> is a sophisticated technique <span class="fontSizeLarge">that compares each data point to the previous one. If the change in the value of the data point is more than a certain amount, then it is flagged as an anomaly.</span></p><p><span class="fontSizeLarge">Both fixed and delta deviation are valuable tools that can be used to improve the <strong>quality</strong>, <strong>accuracy</strong>, and<strong> efficiency</strong> of data ingestion projects.</span></p></div></div><div class="et_pb_module et_pb_text et_pb_text_15 et_pb_text_align_left et_pb_bg_layout_light"><div class="et_pb_text_inner"><h5><a href="https://help.datagaps.com/articles/#!dataops-suite/data-observability" target="_blank" rel="noopener"><u>Learn More</u></a></h5></div></div> </div>
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<h2 class="elementor-heading-title elementor-size-default">4. Collaboration with Microsoft Teams
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DataOps Suite strengthens collaboration by seamlessly integrating with Microsoft Teams, providing real-time notifications and status updates. </div>
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<img loading="lazy" decoding="async" width="980" height="549" src="https://www.datagaps.com/wp-content/uploads/Collaboration-with-Microsoft-Teams.webp" class="attachment-full size-full wp-image-25408" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Collaboration-with-Microsoft-Teams.webp 980w, https://www.datagaps.com/wp-content/uploads/Collaboration-with-Microsoft-Teams-300x168.webp 300w, https://www.datagaps.com/wp-content/uploads/Collaboration-with-Microsoft-Teams-768x430.webp 768w" sizes="(max-width: 980px) 100vw, 980px" /> </div>
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<center>Collaboration with Microsoft Teams</center>
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<div class="et_pb_module et_pb_text et_pb_text_14 et_pb_text_align_left et_pb_bg_layout_light"><div class="et_pb_text_inner"><div class="et_pb_module et_pb_text et_pb_text_19 et_pb_text_align_left et_pb_bg_layout_light"><div class="et_pb_text_inner"><p>DataOps Suite collaborates with <a href="https://www.microsoft.com/en-us/microsoft-teams/collaboration" target="_blank" rel="noopener"><u>Microsoft Teams</u></a> by sending the status of Pipeline and <span class="fontSizeLarge">Data Quality rules of the Data Model execution </span>to the channels through “Webhook URL<strong>”</strong>.</p><p><span class="fontSizeLarge">By integrating DataOps Suite with Microsoft Teams, team members can receive notifications about pipeline or data quality rule execution status, errors, and other important notification events directly in Microsoft Teams </span>based on the configured Teams template<span class="fontSizeLarge">. This can help to improve communication and collaboration between team members, and ensure that everyone is on the same page.</span></p></div></div><div class="et_pb_module et_pb_text et_pb_text_20 et_pb_text_align_left et_pb_bg_layout_light"><div class="et_pb_text_inner"><h5><a href="https://help.datagaps.com/articles/#!dataops-suite/collaboration-tools" target="_blank" rel="noopener"><u>Learn More</u></a></h5></div></div></div></div> </div>
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<div class="et_pb_module et_pb_text et_pb_text_14 et_pb_text_align_left et_pb_bg_layout_light"><div class="et_pb_text_inner"><div class="et_pb_module et_pb_text et_pb_text_19 et_pb_text_align_left et_pb_bg_layout_light"><div class="et_pb_text_inner"><div class="et_pb_module et_pb_text et_pb_text_21 et_pb_text_align_left et_pb_bg_layout_light"><div class="et_pb_text_inner">For more information on other enhancements of DataOps Suite version 4.0.0, read our <a href="https://help.datagaps.com/articles/#!dataops-suite/dataops-suite-releases/a/h2_203948859" target="_blank" rel="noopener"><u>Release Notes.</u></a></div></div><div class="et_pb_module et_pb_text et_pb_text_22 et_pb_text_align_left et_pb_bg_layout_light"><div class="et_pb_text_inner"><a href="https://help.datagaps.com/articles/#!dataops-suite/dataops-suite-releases" target="_blank" rel="noopener"><u>Full Release Notes of DataOps Suite – Learn More</u></a></div></div></div></div></div></div> </div>
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<h3 class="elementor-heading-title elementor-size-default">DataOps Suite – Free Trial</h3> </div>
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The Datagaps’ DataOps Suite now comes with new components that add extensibility and connectivity with other applications as well as a focus on ease of creating tests by automatically creating SQL Queries and identifying anomalies based on data profile. </div>
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<h4 class="elementor-heading-title elementor-size-default">Try DataOps Suite Free for 14 days…</h4> </div>
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<h2 id="faq-heading">Frequently Asked Questions: DataOps Suite 4.0.0 Release</h2>
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<details>
<summary>1) What is GIT Integration in DataOps Suite 4.0.0?</summary>
<p>
GIT Integration lets teams store, organize, and version-control their ETL/ELT code, BI reports and
workbooks, and test cases in a single Git repository, using providers like Azure Repos and Bitbucket,
with easy rollback to previous versions.
</p>
</details>
<details>
<summary>2) What new BI testing capabilities did version 4.0.0 add?</summary>
<p>
The release adds Tableau Upgrade testing, Power BI Regression testing, and Power BI Upgrade testing —
comparing baseline and live dashboard versions for both text changes and pixel-level visual differences.
</p>
</details>
<details>
<summary>3) How does DataOps Suite detect data anomalies in version 4.0.0?</summary>
<p>
It introduces two techniques: Fixed deviation, which flags a data point when it crosses a set threshold,
and Delta deviation, which flags a data point when it changes too much compared to the previous value.
</p>
</details>
<details>
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<p>The post <a href="https://www.datagaps.com/blog/dataops-suite-feature-updates-version-2023-4-0-0/">DataOps Suite Feature Updates – Version 2023.4.0.0</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>A Glimpse into the Trusted Data Summit 2023 by Datagaps</title>
<link>https://www.datagaps.com/blog/a-glimpse-into-the-trusted-data-summit-2023-by-datagaps/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Fri, 05 Jan 2024 12:52:45 +0000</pubDate>
<category><![CDATA[Trusted Data Summit]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=25373</guid>
<description><![CDATA[<p>Trust has become the cornerstone of decision-making. The Trusted Data Summit 2023, a virtual event hosted by Datagaps</p>
<p>The post <a href="https://www.datagaps.com/blog/a-glimpse-into-the-trusted-data-summit-2023-by-datagaps/">A Glimpse into the Trusted Data Summit 2023 by Datagaps</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>The Trusted Data Summit 2023, a virtual event hosted by Datagaps on November 15, 2023, brought together 900+ professionals, including speakers like Rajarajan Sampath, Donald Farmer, and Evan Levy. Sessions covered data trust in the age of AI, governance, data quality, security, and automation strategies for ETL, BI, and data quality testing. The summit featured keynotes, interactive workshops, and real-world case studies from organizations including NYU, PwC, and UVA.</p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li>900+ professionals attended virtually, including notable industry speakers such as Rajarajan Sampath, Donald Farmer, and Evan Levy, covering topics from AI-era data trust to governance and security.</li><li>Sessions spanned data quality, governance, and automation, with specific focus on strategies for ETL Testing, Data Quality Monitoring, and BI Testing.</li><li>Interactive workshops turned theory into practice, giving attendees hands-on experience with data trust technologies and methodologies they could apply immediately.</li><li>Real-world case studies featured organizations like NYU, PwC, and UVA, demonstrating how trusted data drives innovation and business growth across sectors.</li></ul> </div>
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<p><span class="TextRun SCXW108474843 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW108474843 BCX0">In a world increasingly driven by data, trust has become the cornerstone of decision-making. The Trusted Data Summit 2023, a virtual event hosted by </span><span class="NormalTextRun SCXW108474843 BCX0">Datagaps</span><span class="NormalTextRun SCXW108474843 BCX0">, served as</span><span class="NormalTextRun SCXW108474843 BCX0"> </span><span class="NormalTextRun SCXW108474843 BCX0">a </span><span class="NormalTextRun SCXW108474843 BCX0">forum for like</span><span class="NormalTextRun SCXW108474843 BCX0">–</span><span class="NormalTextRun SCXW108474843 BCX0">minded data enthusiasts to exchange ideas about</span><span class="NormalTextRun SCXW108474843 BCX0"> </span><span class="NormalTextRun SCXW108474843 BCX0">improving the data journey through the enterprise</span><span class="NormalTextRun SCXW108474843 BCX0">. </span><span class="NormalTextRun SCXW108474843 BCX0"> </span><span class="NormalTextRun SCXW108474843 BCX0">Held on November 15th, 2023, industry experts, </span><span class="NormalTextRun SCXW108474843 BCX0">partners, and clients from </span><span class="NormalTextRun SCXW108474843 BCX0">across the globe converged </span><span class="NormalTextRun SCXW108474843 BCX0">virtually to</span><span class="NormalTextRun SCXW108474843 BCX0"> delve into the intricacies of building and </span><span class="NormalTextRun SCXW108474843 BCX0">maintaining</span><span class="NormalTextRun SCXW108474843 BCX0"> trust in data.</span></span><span class="EOP SCXW108474843 BCX0" data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":160,"335559740":259}"> </span></p> </div>
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<p>Over 900+ professionals, including renowned figures like Rajarajan Sampath, Donald Farmer, Evan Levy, and more, graced the summit with their expertise. Their insights provided a comprehensive overview of the data trust landscape, covering topics such as data in the age of AI, data governance, data quality management, data security, and automation strategies for <a href="https://www.datagaps.com/data-testing-concepts/etl-testing/" target="_blank" rel="noopener"><span style="text-decoration: underline;">ETL Testing</span></a>, <a href="https://www.datagaps.com/dataops-suite/data-quality/" target="_blank" rel="noopener"><u>Data Quality Monitoring</u></a><u>,</u> and <a href="https://www.datagaps.com/data-testing-concepts/bi-testing/" target="_blank" rel="noopener"><u>BI Testing</u></a>.</p> </div>
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<p>Beyond the captivating keynotes, the summit offered interactive workshops, allowing attendees to gain practical experience with cutting-edge data trust technologies and methodologies. These sessions transformed theoretical knowledge into actionable strategies that could be implemented immediately within organizations.</p> </div>
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<h4 class="elementor-heading-title elementor-size-default">Real-World Case Studies: Illuminating Data Trust in Action</h4> </div>
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<img loading="lazy" decoding="async" width="739" height="475" src="https://www.datagaps.com/wp-content/uploads/UVA-case-studies.webp" class="attachment-full size-full wp-image-25377" alt="" srcset="https://www.datagaps.com/wp-content/uploads/UVA-case-studies.webp 739w, https://www.datagaps.com/wp-content/uploads/UVA-case-studies-300x193.webp 300w" sizes="(max-width: 739px) 100vw, 739px" /> </div>
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<p>The summit showcased how organizations from various sectors are leveraging trusted data to fuel innovation and propel growth. Real-world case studies from companies like <a href="https://www.nyu.edu/?challenge=d06e90d7-4d8f-4b88-9d8c-10b73beb60f1" target="_blank" rel="noopener"><u>NYU</u></a>, <a href="https://www.pwc.in/" target="_blank" rel="noopener"><u>PwC</u></a>, <a href="https://www.virginia.edu/" target="_blank" rel="noopener"><u>UVA</u></a>, and others provided tangible examples of how data trust can transform businesses and industries.</p> </div>
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<h4 class="elementor-heading-title elementor-size-default">Direct Dialogue with Data Trust Experts
</h4> </div>
</div>
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<p>Attendees took the opportunity to engage with the summit’s experts in interactive Q&A sessions. These sessions addressed essential questions, providing valuable insights into the challenges and opportunities in the realm of data.</p> </div>
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<h4 class="elementor-heading-title elementor-size-default">Innovative Solutions: Paving the Way for Data Excellence</h4> </div>
</div>
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<p>The Trusted Data Summit was a launchpad for the latest data trust solutions and software advancements. Attendees gained firsthand access to modern technologies, witnessing their potential to improve the data management landscape.</p> </div>
</div>
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<h4 class="elementor-heading-title elementor-size-default">A Resounding Success: A Summit That Shaped the Discourse</h4> </div>
</div>
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The Trusted Data Summit 2023 concluded with a resounding success, leaving an indelible mark on the field of data trust. The summit’s focus on actionable insights, collaborative learning, and showcasing real-world applications of data trust resonated deeply with attendees. </div>
</div>
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<h4 class="elementor-heading-title elementor-size-default">Embark on Your Data Excellence Journey</h4> </div>
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<p><span class="TextRun SCXW222413346 BCX0" lang="EN-US" xml:lang="EN-US" data-contrast="none"><span class="NormalTextRun SCXW222413346 BCX0">As the curtains close on the Trusted Data Summit 2023, the journey </span><span class="NormalTextRun SCXW222413346 BCX0">doesn’t</span><span class="NormalTextRun SCXW222413346 BCX0"> end here. </span><span class="NormalTextRun SCXW222413346 BCX0">Datagaps</span><span class="NormalTextRun SCXW222413346 BCX0"> invites you to continue your quest for data excellence. Stay connected with us for future events, pre-register for upcoming summits, and explore our wealth of resources and tools. Together, </span><span class="NormalTextRun SCXW222413346 BCX0">let’s</span><span class="NormalTextRun SCXW222413346 BCX0"> embark on a data-driven future where trust and innovation are the cornerstones of success.</span></span><span class="EOP SCXW222413346 BCX0" data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":160,"335559740":259}"> </span></p> </div>
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<h4 class="elementor-heading-title elementor-size-default">Thank You Note from Our CEO Mr Narendar Yalamanchilli</h4> </div>
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<center>Result in benchmark</center> </div>
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<h4 class="elementor-heading-title elementor-size-default">Join The Data Excellence Journey</h4> </div>
</div>
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<p data-sourcepos="23:1-23:98"><a href="https://www.datagaps.com/trusted-data-summit/" target="_blank" rel="noopener"><u>Visit our summit page</u></a> to delve into the details of our Data Leader and Professionals Tracks.</p><p data-sourcepos="25:1-25:143">Together, let’s transform the way you manage, govern, and analyze your data. Together, we can build a future where data trust is the foundation of success.</p> </div>
</div>
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<h4 class="elementor-heading-title elementor-size-default">Join The Data Excellence Journey</h4> </div>
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<p>Datagaps is committed to empowering organizations to achieve data excellence through our comprehensive suite of data management solutions and expert services. We are passionate about helping businesses harness the power of data to make informed decisions, optimize operations, and drive innovation.</p> </div>
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<h2 id="faq-heading">FAQs: Trusted Data Summit 2023</h2>
<section class="faq-section" aria-labelledby="faq-heading">
<div class="faq-list">
<details>
<summary>1) What was the Trusted Data Summit 2023?</summary>
<p>
Trusted Data Summit 2023 was a virtual event hosted by Datagaps on November 15,
2023, bringing together more than 900 data professionals to discuss data trust,
governance, data quality, automation, and AI-driven data management.
</p>
</details>
<details>
<summary>2) Who were some of the speakers at the Trusted Data Summit 2023?</summary>
<p>
Featured speakers included Rajarajan Sampath, Donald Farmer, and Evan Levy, who
shared insights on building trusted data, strengthening governance, improving
security, and preparing organizations for the AI era.
</p>
</details>
<details>
<summary>3) What topics did the summit’s sessions cover?</summary>
<p>
The sessions explored best practices for data quality, data governance, and
automation, with dedicated discussions on ETL Testing, Data Quality Monitoring,
BI Testing, and establishing trusted data pipelines.
</p>
</details>
<details>
<summary>4) Did the summit include hands-on or practical content, not just presentations?</summary>
<p>
Yes. In addition to keynote presentations, the summit included interactive
workshops, practical product demonstrations, and real-world customer case studies
from organizations such as NYU, PwC, and the University of Virginia (UVA).
</p>
</details>
</div>
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Rajesh Kumar A </a>
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Digital Marketing Manager, Datagaps </p>
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Director, Technology Strategy, Datagaps </p>
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<p>Director of Technology Strategy at Datagaps. Business solutions architect and Certified Scrum Master in data engineering, responsible AI, and ML across BFSI, telecom, aviation, and energy.</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/a-glimpse-into-the-trusted-data-summit-2023-by-datagaps/">A Glimpse into the Trusted Data Summit 2023 by Datagaps</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>Enforcing Best Practices for Power BI Development</title>
<link>https://www.datagaps.com/blog/enforcing-best-practices-for-power-bi-development/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Fri, 05 Jan 2024 07:08:19 +0000</pubDate>
<category><![CDATA[BI Testing]]></category>
<category><![CDATA[Data Validation]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=25287</guid>
<description><![CDATA[<p>Best practices for Power BI development is important. Here we provide an overview of some key best practices for Power BI development</p>
<p>The post <a href="https://www.datagaps.com/blog/enforcing-best-practices-for-power-bi-development/">Enforcing Best Practices for Power BI Development</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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<h4 class="elementor-heading-title elementor-size-default">What is Power BI ?
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<p><a href="https://powerbi.microsoft.com/en-in/">Power BI</a> is a powerful tool for creating interactive and informative reports and visualizations. However, to create effective reports, following best practices for Power BI development is important. This article will provide an overview of some key best practices for Power BI development, including creating a clear and well-defined data model, keeping the data fresh, using appropriate visualizations, using filters and slicers, organizing and labelling report elements, documenting and commenting on your work, testing and validation, and security and access management. By following these best practices, you can create accurate and meaningful reports that are easy to understand and navigate and that effectively communicate the information they contain.</p><p>Throughout this blog post on certain pointers, we showcase Datagaps’ <a href="https://www.datagaps.com/bi-testing-tools/bi-validator/">BI Validator</a>.</p> </div>
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<h4 class="elementor-heading-title elementor-size-default">What is BI Validator and where do you use it?</h4> </div>
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<p>Datagaps’ BI Validator is a tool that can be used to validate and test the accuracy of data in Business Intelligence (BI) reports. It is designed to automate the validation process and can be integrated with other BI tools and platforms. It can be used to check for errors, inconsistencies, or outliers in the data and can also be used to <a href="https://www.datagaps.com/blog/what-are-data-quality-dimensions/"><span style="text-decoration: underline; color: #1967d2;">perform data quality checks</span>.</a> It can validate data from a wide range of BI platforms, including Power BI, <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.tableau.com/">Tableau</a></span>, and SAP BusinessObjects, and it can also be used to validate data from a variety of data sources, such as Excel spreadsheets, SQL Server databases, and cloud services like Azure SQL Database or Power Platform. The tool can be used to validate the data before it is loaded into a data warehouse or reporting system.</p><p><span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/bi-testing-tools/bi-validator-download/">Try BI Validator – Free for 14 days.</a></span></p> </div>
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<h4 class="elementor-heading-title elementor-size-default">Creating a Clear and Well Defined Data Model
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<p>A clear and well-defined data model is crucial to make it easy to build accurate and meaningful visualizations in Power BI.</p><p>There are several ways to achieve this:</p><ul><li><strong>Identify key pieces of information:</strong> Start by identifying the key pieces of information that you want to include in the report, such as sales, customers, products, etc.</li><li><strong>Organize the data into logical tables:</strong> Once you have identified the key pieces of information, organize that information into logical tables, such as a table for customers, a table for products, and a table for sales.</li><li><strong>Create relationships between tables:</strong> Once the tables are created, establish relationships between them, for example, a relationship between customers and sales or between products and sales.</li><li><strong>Define the data structure:</strong> Define each table column’s data structure and data types. This will help to ensure that the data is accurate and consistent.</li><li><strong>Use calculated columns and measures:</strong> Use calculated columns and measures to create new values based on the existing data, for example, total sales, average sales, etc.</li></ul> </div>
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<img loading="lazy" decoding="async" width="1250" height="457" src="https://www.datagaps.com/wp-content/uploads/Power-BI-Tables-and-relationships.webp" class="attachment-full size-full wp-image-25301" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Power-BI-Tables-and-relationships.webp 1250w, https://www.datagaps.com/wp-content/uploads/Power-BI-Tables-and-relationships-300x110.webp 300w, https://www.datagaps.com/wp-content/uploads/Power-BI-Tables-and-relationships-1024x374.webp 1024w, https://www.datagaps.com/wp-content/uploads/Power-BI-Tables-and-relationships-768x281.webp 768w" sizes="(max-width: 1250px) 100vw, 1250px" /> </div>
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<center>Power BI Tables and Relationships</center> </div>
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<p>By creating a clear and well-defined data model, you can make it easy to build accurate and meaningful visualizations in Power BI, which will make the report more useful and reliable. Additionally, a well-designed data model can help to improve the performance of the report, especially when working with large amounts of data.</p> </div>
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<h4 class="elementor-heading-title elementor-size-default">Keeping the Data Fresh
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<p>Keeping the data in a Power BI report up to date is crucial to ensure the report is useful and accurate. There are several ways to achieve this:</p><p>There are several ways to achieve this:</p><ul><li><strong>Data refresh:</strong> Schedule regular data refreshes. This can be done daily, weekly, or monthly, depending on the data source. This will ensure that the data in the report is up to date</li><li><strong>Data source connection:</strong> Connect the report to a data source such as an Excel spreadsheet, a SQL Server database, or a cloud service like Azure SQL Database or Power Platform. This will ensure that the report is always pulling the most recent data.</li><li><strong>Direct Query:</strong> Use Direct Query to connect to the data source and retrieve the data as needed, this can be useful for scenarios where the data changes frequently, and you want the report to reflect the most recent data.</li><li><strong>Live connection:</strong> Use a live connection to connect to the data source and retrieve the data in real time. This can be useful for scenarios where the data changes frequently, and you want the report to reflect the most recent data in real time.</li></ul><p>By keeping the data fresh, you can ensure that the report is always accurate and up to date, making it more useful and reliable. Additionally, you can set up data alerts that will notify you of any issues with the data or the data refresh process.</p> </div>
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<img loading="lazy" decoding="async" width="775" height="563" src="https://www.datagaps.com/wp-content/uploads/Schedular.webp" class="attachment-full size-full wp-image-25308" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Schedular.webp 775w, https://www.datagaps.com/wp-content/uploads/Schedular-300x218.webp 300w, https://www.datagaps.com/wp-content/uploads/Schedular-768x558.webp 768w" sizes="(max-width: 775px) 100vw, 775px" /> </div>
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<h4 class="elementor-heading-title elementor-size-default">Using Appropriate Visualizations
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<p>Choosing the right visualization for the presenting data is crucial to make the report easy to understand and communicate the information effectively. Several types of visualizations can be used in Power BI; some of them are:</p><ul><li><strong>Bar charts:</strong> Show the comparison of different values for a specific category. It’s a great option for showing the relative size of data points, such as sales by product or region.</li><li><strong>Line charts:</strong> Show the trend of data over time. It’s a great option for showing how data has changed over time, for example, sales by month or year.</li><li><strong>Pie charts:</strong> Show the proportion of different values for a specific category. It’s a great option for showing data distribution, for example, sales by product category.</li><li><strong>Scatter charts:</strong> Show the relationship between two or more variables. It’s a great option for showing the correlation between different data points, for example, sales by price and quantity.</li><li><strong>Maps:</strong> Show the location-based data. It’s a great option for showing data associated with a specific location, for example, sales by state or country.</li><li><strong>Card visual:</strong> Show a single value or a data summary; it’s a great option for showing key metrics such as total sales, average sales, etc.</li></ul><p>By choosing the appropriate visualization for the presented data, the report will be easier to understand, and the information will be effectively communicated. Additionally, combining different visualization types can enhance the understanding and analysis of the data.</p> </div>
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<img loading="lazy" decoding="async" width="415" height="265" src="https://www.datagaps.com/wp-content/uploads/Power-BI-Chart.webp" class="attachment-large size-large wp-image-25313" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Power-BI-Chart.webp 415w, https://www.datagaps.com/wp-content/uploads/Power-BI-Chart-300x192.webp 300w" sizes="(max-width: 415px) 100vw, 415px" /> </div>
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<p>Power BI Chart</p> </div>
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<img loading="lazy" decoding="async" width="640" height="397" src="https://www.datagaps.com/wp-content/uploads/Pie-chart-representation-for-too-complex-data.webp" class="attachment-large size-large wp-image-25314" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Pie-chart-representation-for-too-complex-data.webp 755w, https://www.datagaps.com/wp-content/uploads/Pie-chart-representation-for-too-complex-data-300x186.webp 300w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p>Pie chart representation for too complex data</p> </div>
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<img loading="lazy" decoding="async" width="669" height="294" src="https://www.datagaps.com/wp-content/uploads/Stacked-bar-chart.webp" class="attachment-full size-full wp-image-25318" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Stacked-bar-chart.webp 669w, https://www.datagaps.com/wp-content/uploads/Stacked-bar-chart-300x132.webp 300w" sizes="(max-width: 669px) 100vw, 669px" /> </div>
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<img loading="lazy" decoding="async" width="668" height="283" src="https://www.datagaps.com/wp-content/uploads/small-multiples.webp" class="attachment-full size-full wp-image-25319" alt="" srcset="https://www.datagaps.com/wp-content/uploads/small-multiples.webp 668w, https://www.datagaps.com/wp-content/uploads/small-multiples-300x127.webp 300w" sizes="(max-width: 668px) 100vw, 668px" /> </div>
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Box and Whisker Plot
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<p>Filters and slicers are powerful tools in Power BI that allow users to focus on specific parts of the data, making the report more interactive.</p><p>There are several ways to achieve this:</p><ul><li><strong>Filters:</strong> Allow users to narrow down the data that is displayed in the report by applying conditions to the data. For example, you could use a filter to only show data for a specific period or region.</li><li><strong>Slicers:</strong> Users can interact with the report by selecting specific values, such as a product or a category. Slicers can be used to filter multiple visuals at once, making it easy for users to see the data that is most relevant to them.</li><li><strong>Relative date slicers:</strong> allow users to filter the data by a relative range of dates, for example, last month, last quarter, etc.</li><li><strong>Top N slicers:</strong> allow users to filter the data by the top N values, for example, the top 10 products or the top 5 regions.</li><li><strong>Advanced filtering options:</strong> allow users to filter the data using advanced options such as “contains”, “starts with”, “ends with”, etc.</li><li style="list-style-type: none;"> </li></ul><p>By using filters and slicers, users can quickly and easily focus on the most relevant data, making the report more interactive and efficient.</p> </div>
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<img loading="lazy" decoding="async" width="1144" height="449" src="https://www.datagaps.com/wp-content/uploads/Data-without-applying-any-filter.webp" class="attachment-full size-full wp-image-25326" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Data-without-applying-any-filter.webp 1144w, https://www.datagaps.com/wp-content/uploads/Data-without-applying-any-filter-300x118.webp 300w, https://www.datagaps.com/wp-content/uploads/Data-without-applying-any-filter-1024x402.webp 1024w, https://www.datagaps.com/wp-content/uploads/Data-without-applying-any-filter-768x301.webp 768w" sizes="(max-width: 1144px) 100vw, 1144px" /> </div>
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<center>Data without applying any filter</center>
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<img loading="lazy" decoding="async" width="1142" height="459" src="https://www.datagaps.com/wp-content/uploads/Result-in-benchmark.webp" class="attachment-full size-full wp-image-25327" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Result-in-benchmark.webp 1142w, https://www.datagaps.com/wp-content/uploads/Result-in-benchmark-300x121.webp 300w, https://www.datagaps.com/wp-content/uploads/Result-in-benchmark-1024x412.webp 1024w, https://www.datagaps.com/wp-content/uploads/Result-in-benchmark-768x309.webp 768w" sizes="(max-width: 1142px) 100vw, 1142px" /> </div>
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<center>Application of filters</center> </div>
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<img loading="lazy" decoding="async" width="1126" height="453" src="https://www.datagaps.com/wp-content/uploads/Application-of-filters.webp" class="attachment-full size-full wp-image-25328" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Application-of-filters.webp 1126w, https://www.datagaps.com/wp-content/uploads/Application-of-filters-300x121.webp 300w, https://www.datagaps.com/wp-content/uploads/Application-of-filters-1024x412.webp 1024w, https://www.datagaps.com/wp-content/uploads/Application-of-filters-768x309.webp 768w" sizes="(max-width: 1126px) 100vw, 1126px" /> </div>
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<center>Result in benchmark</center> </div>
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<h4 class="elementor-heading-title elementor-size-default">Organizing and Labelling Report Elements
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<p>Organizing and labelling report elements is important in creating a user-friendly and easy-to-navigate Power BI report.</p><p>There are several ways to achieve this:</p><ul><li>Use a clear and logical layout: Group together related visualizations and information and use a consistent layout throughout the report. For example, you could group all of the visualizations related to sales on one page and all of the visualizations related to customers on another page.</li><li>Use clear and descriptive titles: Add clear and descriptive titles to all visualizations and charts, making it easy for users to understand what the data represents.</li><li>Use bookmarks: Create bookmarks that allow users to jump to specific parts of the report quickly. For example, you could create a bookmark for the sales data and another bookmark for the customer data.</li><li>Use drill-through: Create drill-through actions that allow users to explore the data in more detail. For example, you could create a drill-through action that allows users to see the details of a specific sale when they click on a data point in a visualization.</li><li>Use consistent formatting: Use consistent formatting throughout the report, such as the same font, colour scheme, and layout. It will help users to focus on the data and not be distracted by formatting variations.</li><li style="list-style-type: none;"> </li></ul><p>By organizing and labelling report elements, you can make the report more user-friendly and easy to navigate, which can help users to find the information they need more easily, thus making the report more efficient and effective.</p> </div>
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<img loading="lazy" decoding="async" width="952" height="423" src="https://www.datagaps.com/wp-content/uploads/Label-when-applied-in-test-plan-1.webp" class="attachment-full size-full wp-image-25336" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Label-when-applied-in-test-plan-1.webp 952w, https://www.datagaps.com/wp-content/uploads/Label-when-applied-in-test-plan-1-300x133.webp 300w, https://www.datagaps.com/wp-content/uploads/Label-when-applied-in-test-plan-1-768x341.webp 768w" sizes="(max-width: 952px) 100vw, 952px" /> </div>
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<center>Label when applied in test plan</center> </div>
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<img loading="lazy" decoding="async" width="1100" height="348" src="https://www.datagaps.com/wp-content/uploads/Result-in-test-plan-tree-when-group-by-label.webp" class="attachment-full size-full wp-image-25337" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Result-in-test-plan-tree-when-group-by-label.webp 1100w, https://www.datagaps.com/wp-content/uploads/Result-in-test-plan-tree-when-group-by-label-300x95.webp 300w, https://www.datagaps.com/wp-content/uploads/Result-in-test-plan-tree-when-group-by-label-1024x324.webp 1024w, https://www.datagaps.com/wp-content/uploads/Result-in-test-plan-tree-when-group-by-label-768x243.webp 768w" sizes="(max-width: 1100px) 100vw, 1100px" /> </div>
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<center>Result in test plan tree when group by label</center> </div>
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<h4 class="elementor-heading-title elementor-size-default">Documenting and Commenting Your Work
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<p>Documenting and commenting your work is an important step in the Power BI development process as it helps others to understand and maintain the report.</p><p>There are several ways to achieve this:</p><ul><li><strong>Annotating the report:</strong> Add annotations and comments to the report that provide context and information about the data, calculations, and visualizations being used.</li><li><strong>Documenting data sources:</strong> Include information about the data sources used in the report, such as the location of the data, the data structure, and any transformations that were applied.</li><li><strong>Documenting calculations and formulas:</strong> Include information about any calculations or formulas that were used in the report, such as how they were created and what they do.</li><li><strong>Creating a report glossary:</strong> Create a glossary that defines the terms and measures used in the report, which can be helpful for users who are not familiar with the data.</li><li><strong>Creating a report guide:</strong> Create a guide that explains how to use the report, including how to navigate, filter, and interact with the data.</li><li style="list-style-type: none;"> </li></ul><p>By documenting and commenting your work, you can make it easier for others to understand and maintain the report, and it will be easier for users to use the report, as they will have a better understanding of the data and the context it is presented in.</p> </div>
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<h4 class="elementor-heading-title elementor-size-default">Use Testing and Validation Techniques
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<p>To ensure the accuracy and reliability of a Power BI report, it is important to test and validate the data.</p><p>There are several ways to achieve this:</p><ul><li><strong>Data validation:</strong> Compare the data in the report to the original data source to ensure that it is accurate and complete. This can be done using data validation tools, such as Power Query, or manually reviewing the data.</li><li><strong>Data quality checks:</strong> Check for errors or outliers in the data that might impact the report’s accuracy. This can be done using data quality tools, such as Data Profiling, or manually reviewing the data.</li><li><strong>Automated tests:</strong> Create automated tests that check the report for specific conditions or results. These tests can be run regularly to ensure the report functions correctly.</li><li><strong>User acceptance testing:</strong> Involve end-users in the testing process by having them review the report and provide feedback. This can help identify any issues or confusion users might have with the report.</li><li><strong>Performance testing:</strong> Test the report’s performance to ensure that it loads and functions quickly, even with large amounts of data. This can be done using performance testing tools, such as Power BI performance analyzer, or manually testing the report.</li><li style="list-style-type: none;"> </li></ul><p>Using these testing and validation techniques, you can ensure the integrity of the data and the report, identify any errors or problems that might exist, and make necessary changes before sharing the report with others, ultimately ensuring that the report is accurate and reliable.</p> </div>
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<h4 class="elementor-heading-title elementor-size-default">Security and Access Management
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<p>One of the key aspects of Power BI development is ensuring that the report is secure and that sensitive data is properly protected.</p><p>There are several ways to achieve this:</p><ul><li><strong>Row-level security:</strong> Allows you to limit data access to specific rows in a table based on the user’s role or group membership. This can be useful for scenarios where different users have different data access levels.</li><li><strong>Custom roles:</strong> Allows you to create custom roles defining user actions on a report. For example, you could create a role allowing users to view the data but not edit it.</li><li><strong>Data encryption:</strong> Power BI supports data encryption at rest and in transit, which helps to protect sensitive data from unauthorized access.</li><li><strong>Azure Active Directory (AAD) authentication:</strong> Allows you to use AAD to authenticate users and control access to the report. This can be useful for scenarios where you want to integrate Power BI with other Microsoft services, such as SharePoint or Teams.</li><li><strong>Conditional access:</strong> Allows you to configure access policies that are based on the user’s location, device, or other factors. For example, you could configure a policy that only allows users to access the report from a specific location or while connected to a specific network.</li><li style="list-style-type: none;"> </li></ul><p>When used together, All of these options will provide a robust security and access management strategy, ensuring that the report is only accessible by authorized users and that sensitive data is properly protected, thus providing an extra layer of security to the report.</p> </div>
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<img loading="lazy" decoding="async" width="883" height="467" src="https://www.datagaps.com/wp-content/uploads/Security-management.webp" class="attachment-full size-full wp-image-25350" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Security-management.webp 883w, https://www.datagaps.com/wp-content/uploads/Security-management-300x159.webp 300w, https://www.datagaps.com/wp-content/uploads/Security-management-768x406.webp 768w" sizes="(max-width: 883px) 100vw, 883px" /> </div>
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<center>Security management in BI Validator</center> </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/bi-testing-tools/bi-validator-download/">Free Trail: Try BI Validator for 14 Days</a></span></p> </div>
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<h4 class="elementor-heading-title elementor-size-default">Conclusion</h4> </div>
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<p>In conclusion, following best practices for Power BI development is crucial to create accurate and meaningful reports that are easy to understand and navigate. By following these best practices, you can ensure that the report is accurate and reliable and that it effectively communicates the information it contains. BI Validator is a powerful tool that can be used to validate and test the accuracy of data in Business Intelligence (BI) reports. By using BI Validator, you can ensure that the data is accurate and up-to-date and that it is properly structured and organized. BI Validator can also be used to check for errors, inconsistencies, or outliers in the data, and can also be used to perform data quality checks. By using BI Validator in conjunction with best practices for Power BI development, you can create accurate and meaningful reports that are easy to understand and navigate and that effectively communicate the information they contain.</p> </div>
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<p>The post <a href="https://www.datagaps.com/blog/enforcing-best-practices-for-power-bi-development/">Enforcing Best Practices for Power BI Development</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 Integration of Data Build Tool (dbt) and Datagaps DataOps Suite</title>
<link>https://www.datagaps.com/blog/the-integration-of-data-build-tool-dbt-and-datagaps-dataops-suite/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Thu, 04 Jan 2024 05:28:56 +0000</pubDate>
<category><![CDATA[Data Quality]]></category>
<category><![CDATA[Data Validation]]></category>
<category><![CDATA[Dataflow]]></category>
<category><![CDATA[DataOps]]></category>
<category><![CDATA[ETL Testing]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=25000</guid>
<description><![CDATA[<p>Data Warehouses provide a comprehensive view of data across many different sources, and proper analysis can encourage better business decisions and problem-solving. However, building and maintaining an effective data warehouse requires careful thought and consideration</p>
<p>The post <a href="https://www.datagaps.com/blog/the-integration-of-data-build-tool-dbt-and-datagaps-dataops-suite/">The Integration of Data Build Tool (dbt) and Datagaps DataOps Suite</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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<h4 class="elementor-heading-title elementor-size-default">What is dbt Data Build Tool?</h4> </div>
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<p>Introduction – In today’s data-driven world, organizations are continually seeking ways to optimize data operations, enhance data quality, and ensure robust governance practices. The integration of powerful tools like DBT (Data Build Tool) and Datagaps DataOps Suite has emerged as a game-changing solution. Individually, these tools offer impressive capabilities, and when integrated, they create a symbiotic relationship that addresses challenges related to data transformation, quality, profiling, and observability. In this article, we delve into the integration of DBT and Datagaps DataOps Suite, exploring how this combination fosters more efficient data operations and empowers data-driven decision-making.</p> </div>
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<h4 class="elementor-heading-title elementor-size-default">Data Build Tool(dbt): A Brief Overview
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<p>DBT, or <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://en.wikipedia.org/wiki/Data_build_tool" target="_blank" rel="noopener">Data Build Tool</a></span>, is a popular open-source command-line tool designed primarily for transforming data analytics. It allows data analysts and engineers to transform data within their warehouse in a structured and version-controlled manner. With its focus on SQL-based transformations, DBT promotes collaboration, transparency, and maintainability in data pipelines.</p> </div>
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<h4 class="elementor-heading-title elementor-size-default">Datagaps DataOps Suite: Enhancing Data Quality and Governance</h4> </div>
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<img loading="lazy" decoding="async" width="640" height="212" src="https://www.datagaps.com/wp-content/uploads/DataOps-Suite-Logo-1.webp" class="attachment-large size-large wp-image-25004" alt="DataOps-Suite-Logo-clr" srcset="https://www.datagaps.com/wp-content/uploads/DataOps-Suite-Logo-1.webp 885w, https://www.datagaps.com/wp-content/uploads/DataOps-Suite-Logo-1-300x99.webp 300w, https://www.datagaps.com/wp-content/uploads/DataOps-Suite-Logo-1-768x254.webp 768w" sizes="(max-width: 640px) 100vw, 640px" /> </div>
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<p>DataOps suite that encompasses a range of functionalities, including data quality assurance, profiling, observability, and governance. The suite’s core objective is to improve data operations across the board by ensuring that data is of high quality, well understood, and observable throughout its lifecycle. With plug-and-play integration systems, a plethora of supported data sources, cloud and on-prem system support, and much more the Suite is most comprehensive Data Quality, Profiling, and Governance system in the data sphere.</p> </div>
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<h4 class="elementor-heading-title elementor-size-default">Integration Mechanics: Leveraging REST APIs and Plugin Support </h4> </div>
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<img loading="lazy" decoding="async" width="1402" height="860" src="https://www.datagaps.com/wp-content/uploads/REST-APIs-and-plugin-support.jpeg" class="attachment-full size-full wp-image-25037" alt="" srcset="https://www.datagaps.com/wp-content/uploads/REST-APIs-and-plugin-support.jpeg 1402w, https://www.datagaps.com/wp-content/uploads/REST-APIs-and-plugin-support-300x184.jpeg 300w, https://www.datagaps.com/wp-content/uploads/REST-APIs-and-plugin-support-1024x628.jpeg 1024w, https://www.datagaps.com/wp-content/uploads/REST-APIs-and-plugin-support-768x471.jpeg 768w" sizes="(max-width: 1402px) 100vw, 1402px" /> </div>
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<p>The integration of DBT and Datagaps DataOps Suite is made possible through a combination of REST APIs and plugin support within the Datagaps DataOps Suite. This allows seamless communication between the two platforms, enabling data engineers and analysts to utilize the strengths of both tools without friction. Data transformations orchestrated through DBT can be automatically monitored, profiled, and governed within the Datagaps DataOps Suite, forming a cohesive data management ecosystem.</p> </div>
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<h4 class="elementor-heading-title elementor-size-default"><a href="https://www.datagaps.com/etl-testing-tools/etl-validator-download/">Try DataOps Suite – Free Trial</a></h4> </div>
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<h4 class="elementor-heading-title elementor-size-default">Plugins for Integration</h4> </div>
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<p>The suite provides end-users with complete customizability options and developer-friendly tools to create any set of customizations, integrations, processing nodes, test case validation, and other toolsets.</p> </div>
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<img loading="lazy" decoding="async" width="1408" height="832" src="https://www.datagaps.com/wp-content/uploads/Plugins-for-Integration.jpeg" class="attachment-full size-full wp-image-25048" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Plugins-for-Integration.jpeg 1408w, https://www.datagaps.com/wp-content/uploads/Plugins-for-Integration-300x177.jpeg 300w, https://www.datagaps.com/wp-content/uploads/Plugins-for-Integration-1024x605.jpeg 1024w, https://www.datagaps.com/wp-content/uploads/Plugins-for-Integration-768x454.jpeg 768w" sizes="(max-width: 1408px) 100vw, 1408px" /> </div>
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<p>As seen in the images, the application will help developers define parameters, variables, and datasets which are subsequently used in the code component to create toolsets capable of a variety of tasks.</p> </div>
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<p>The end users only have to plug in the required variables or parameters and the application subsequently takes care of the translations. The example showcases the various types of plugins that themselves can be exported or imported with the different use-cases seen in the enterprise data space. These plugins can be build in Scala as well.</p> </div>
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<h4 class="elementor-heading-title elementor-size-default">Trigger a DBT Job and Compare the Results </h4> </div>
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<p>In this showcase, we use the plugin showcased earlier to first trigger a DBT job after which we compare two datasets for incorrectness.</p> </div>
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<img loading="lazy" decoding="async" width="1793" height="958" src="https://www.datagaps.com/wp-content/uploads/Trigger-a-DBT-Job-and-Compare-the-Results-Dataflow.jpeg" class="attachment-full size-full wp-image-25053" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Trigger-a-DBT-Job-and-Compare-the-Results-Dataflow.jpeg 1793w, https://www.datagaps.com/wp-content/uploads/Trigger-a-DBT-Job-and-Compare-the-Results-Dataflow-300x160.jpeg 300w, https://www.datagaps.com/wp-content/uploads/Trigger-a-DBT-Job-and-Compare-the-Results-Dataflow-1024x547.jpeg 1024w, https://www.datagaps.com/wp-content/uploads/Trigger-a-DBT-Job-and-Compare-the-Results-Dataflow-768x410.jpeg 768w, https://www.datagaps.com/wp-content/uploads/Trigger-a-DBT-Job-and-Compare-the-Results-Dataflow-1536x821.jpeg 1536w" sizes="(max-width: 1793px) 100vw, 1793px" /> </div>
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<p>The dataflow displays the plugin node, loading of the 2 datasets, and data comparison node.</p> </div>
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<img loading="lazy" decoding="async" width="1377" height="522" src="https://www.datagaps.com/wp-content/uploads/Options-of-Chosen-Plugin.jpeg" class="attachment-full size-full wp-image-25058" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Options-of-Chosen-Plugin.jpeg 1377w, https://www.datagaps.com/wp-content/uploads/Options-of-Chosen-Plugin-300x114.jpeg 300w, https://www.datagaps.com/wp-content/uploads/Options-of-Chosen-Plugin-1024x388.jpeg 1024w, https://www.datagaps.com/wp-content/uploads/Options-of-Chosen-Plugin-768x291.jpeg 768w" sizes="(max-width: 1377px) 100vw, 1377px" /> </div>
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<p>Options of Chosen Plugin</p> </div>
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<img loading="lazy" decoding="async" width="1363" height="844" src="https://www.datagaps.com/wp-content/uploads/Output-Plugin.jpeg" class="attachment-full size-full wp-image-25064" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Output-Plugin.jpeg 1363w, https://www.datagaps.com/wp-content/uploads/Output-Plugin-300x186.jpeg 300w, https://www.datagaps.com/wp-content/uploads/Output-Plugin-1024x634.jpeg 1024w, https://www.datagaps.com/wp-content/uploads/Output-Plugin-768x476.jpeg 768w" sizes="(max-width: 1363px) 100vw, 1363px" /> </div>
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<p>Output Plugin</p> </div>
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<img loading="lazy" decoding="async" width="1404" height="874" src="https://www.datagaps.com/wp-content/uploads/Output-of-Data-Compare.jpeg" class="attachment-full size-full wp-image-25065" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Output-of-Data-Compare.jpeg 1404w, https://www.datagaps.com/wp-content/uploads/Output-of-Data-Compare-300x187.jpeg 300w, https://www.datagaps.com/wp-content/uploads/Output-of-Data-Compare-1024x637.jpeg 1024w, https://www.datagaps.com/wp-content/uploads/Output-of-Data-Compare-768x478.jpeg 768w" sizes="(max-width: 1404px) 100vw, 1404px" /> </div>
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<p>Output of Data Compare</p> </div>
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<p>As seen in the screenshots, the application first triggers a DBT job based on the user’s inputs, then loads up the Source and Target Datasets which were created post the job’s completion, and finally runs a data comparison check.</p><p>The failure of this test case triggers a notification to the users showcasing the various mismatched, isolated and duplicate records seen in the datasets as a compiled report.</p> </div>
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<h4 class="elementor-heading-title elementor-size-default">Benefits and Use-Cases</h4> </div>
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<img loading="lazy" decoding="async" width="640" height="1" src="https://www.datagaps.com/wp-content/uploads/Enhanced-Data-Quality-and-Profiling-Benefits.svg" class="attachment-medium_large size-medium_large wp-image-25179" alt="" /> </div>
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<p><strong>Enhanced Data Quality and Profiling:</strong> The integration ensures that data transformed using DBT undergoes rigorous data quality checks and profiling within the Datagaps DataOps Suite. This leads to cleaner, more reliable data, reducing errors and enhancing the trustworthiness of analyses.</p> </div>
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<img loading="lazy" decoding="async" width="640" height="1" src="https://www.datagaps.com/wp-content/uploads/Observability-and-Monitoring-Benefits.svg" class="attachment-medium_large size-medium_large wp-image-25180" alt="" /> </div>
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<p><strong>Observability and Monitoring:</strong> Datagaps DataOps Suite’s <a href="https://www.datagaps.com/dataops-suite/dataflow/" target="_blank" rel="noopener"><span style="text-decoration: underline; color: #1967d2;">observability features</span></a> allow teams to monitor data transformations executed through DBT in real-time. This enables swift identification of issues, performance bottlenecks, and anomalies, leading to quicker resolution times.</p> </div>
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<img loading="lazy" decoding="async" width="640" height="1" src="https://www.datagaps.com/wp-content/uploads/Efficient-Collaboration-Benefits.svg" class="attachment-medium_large size-medium_large wp-image-25181" alt="" /> </div>
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<p><strong>Efficient Collaboration:</strong> Data engineers and analysts can collaborate more efficiently using the integrated solution. DBT’s transformation logic and Datagaps DataOps Suite’s observability tools provide a shared context for better communication and decision-making.</p> </div>
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<img loading="lazy" decoding="async" width="640" height="1" src="https://www.datagaps.com/wp-content/uploads/Unified-Data-Governance-Benefits.svg" class="attachment-medium_large size-medium_large wp-image-25182" alt="" /> </div>
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<p><strong>Unified Data Governance:</strong> While Datagaps DataOps Suite doesn’t focus primarily on governance, its capabilities contribute to effective data governance practices. Organizations can ensure that data transformations adhere to compliance requirements and maintain a clear understanding of data lineage.</p> </div>
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<p><strong>Automated Documentation:</strong> The integration automates the process of documenting data transformations, lineage, and quality checks. This documentation is vital for maintaining a historical record of changes and ensuring transparency.</p> </div>
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<img loading="lazy" decoding="async" width="640" height="1" src="https://www.datagaps.com/wp-content/uploads/Holistic-Data-Strategy-Benefits.svg" class="attachment-medium_large size-medium_large wp-image-25199" alt="" /> </div>
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<p><strong>Holistic Data Strategy:</strong> Integrating DBT and Datagaps DataOps Suite supports organizations in developing a holistic data strategy. It bridges the gap between transformation and operational aspects, empowering organizations to make data-driven decisions confidently.</p> </div>
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<p>The integration of Data Build Tool (DBT) and Datagaps DataOps Suite is a remarkable example of how combining specialized tools can create a more robust and comprehensive data management solution. By harnessing the strengths of both tools, organizations can streamline data transformation, enhance data quality, improve observability, and ensure better data governance practices.</p> </div>
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<p>As the data landscape continues to evolve, this integration paves the way for more effective and efficient data operations, enabling organizations to unlock the full potential of their data-driven initiatives.</p> </div>
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<h4 class="elementor-heading-title elementor-size-default"><a href="https://www.datagaps.com/etl-testing-tools/etl-validator-download/">Try DataOps Suite – Free Trial</a></h4> </div>
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</section>
</div>
<p>The post <a href="https://www.datagaps.com/blog/the-integration-of-data-build-tool-dbt-and-datagaps-dataops-suite/">The Integration of Data Build Tool (dbt) and Datagaps DataOps Suite</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>Data Observability in Tableau Reports</title>
<link>https://www.datagaps.com/blog/data-observability-in-tableau-reports/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Wed, 03 Jan 2024 13:59:22 +0000</pubDate>
<category><![CDATA[BI Testing]]></category>
<category><![CDATA[Tableau Testing]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=24872</guid>
<description><![CDATA[<p>Today, Data has become the core of any business, and ETL processes are important for every data-related project. Be it Data Warehouse or Data Lake every business builds and runs thousands of ETL processes for its data movement. Data validation and ETL Testing will be of prime importance</p>
<p>The post <a href="https://www.datagaps.com/blog/data-observability-in-tableau-reports/">Data Observability in Tableau Reports</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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<p>Even after passing validation rules, migration checks, and metadata reviews, <span style="color: #17253d;">Tableau</span> reports need historical context to answer “are my reports correct?” This post covers three observability layers: pulling live datasets directly out of <span style="text-decoration: underline;"><span style="color: #1967d2; text-decoration: underline;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/automate-tableau-testing/" target="_blank" rel="noopener">Tableau</a></span></span> (or Power BI) via JavaScript, REST APIs, and Selenium; profiling those datasets for nulls, volumes, aggregates, and distributions to catch statistical anomalies using IQR methods; and ML-based anomaly detection that learns seasonality and sends real-time alerts through Slack, Teams, or email when metrics fall outside expected bounds.</p> </div>
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<p><strong>Key Takeaways</strong></p><ul><li><strong>Datasets can be pulled directly from live Tableau reports</strong> — using JavaScript, REST APIs, and Selenium as an add-on, with support for parameterizing reports, filters, and workbooks (and equivalent support for <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.datagaps.com/power-bi-automation-testing-tools/" target="_blank" rel="noopener">Power BI</a></span>).</li><li><strong>Profiling establishes statistical checkpoints</strong> — tracking nulls, volume, distinct percentages, aggregates, patterns, lengths, extremes, and distributions across multiple runs, with IQR-based methods flagging anomalies automatically.</li><li><strong>ML-based detection goes beyond static profiling</strong> — the system trains on seasonality and dependent variables to build a time-series-aware prediction of expected metric ranges.</li><li><strong>Anomalies trigger real-time, categorized alerts</strong> — when a metric falls outside its expected bounds, the test case fails and notifies users via Slack, Teams, or email with the specific metric and category flagged.</li></ul> </div>
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<h2 class="elementor-heading-title elementor-size-default">Data Observability in your Tableau Reports</h2> </div>
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<p>Before observabilities and anomaly detection, the reports have to be validated on many<br />dimensions which can be achieved using the various tools and test plans provided by <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/" target="_blank" rel="noopener">Datagaps</a></span></p><ul><li>Validation of the reports against datasets and other reports</li><li>Validation of reports against business rules and logical rules</li><li>Validation of reports through upgrades and regression tests</li><li>Metadata and Aesthetics Standardization</li><li>Performance and Security Optimization</li></ul> </div>
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<img loading="lazy" decoding="async" width="1280" height="918" src="https://www.datagaps.com/wp-content/uploads/Validation-of-your-Tableau-Reports-1280x918-1.webp" class="attachment-full size-full wp-image-24876" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Validation-of-your-Tableau-Reports-1280x918-1.webp 1280w, https://www.datagaps.com/wp-content/uploads/Validation-of-your-Tableau-Reports-1280x918-1-300x215.webp 300w, https://www.datagaps.com/wp-content/uploads/Validation-of-your-Tableau-Reports-1280x918-1-1024x734.webp 1024w, https://www.datagaps.com/wp-content/uploads/Validation-of-your-Tableau-Reports-1280x918-1-768x551.webp 768w" sizes="(max-width: 1280px) 100vw, 1280px" /> </div>
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<p>The full set of details are explained in a webinar conducted by Datagaps. In this blog, we focus on data observability in the tableau reports.</p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Pulling Datasets from Tableau</h2> </div>
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<p>The seamless integration offered by Datagaps allows users to pull datasets directly from reports into datasets.</p><p>This is done via JavaScript, REST APIs, and Selenium as an add-on.</p><p>The user can parameterize the reports, filters, and workbooks to be picked up. The application allows users to navigate through their workspaces to pick up reports.</p> </div>
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<img loading="lazy" decoding="async" width="1225" height="750" src="https://www.datagaps.com/wp-content/uploads/Add-view.webp" class="attachment-full size-full wp-image-24881" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Add-view.webp 1225w, https://www.datagaps.com/wp-content/uploads/Add-view-300x184.webp 300w, https://www.datagaps.com/wp-content/uploads/Add-view-1024x627.webp 1024w, https://www.datagaps.com/wp-content/uploads/Add-view-768x470.webp 768w" sizes="(max-width: 1225px) 100vw, 1225px" /> </div>
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<img loading="lazy" decoding="async" width="874" height="267" src="https://www.datagaps.com/wp-content/uploads/Shipmode-Department-Sales.webp" class="attachment-full size-full wp-image-24882" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Shipmode-Department-Sales.webp 874w, https://www.datagaps.com/wp-content/uploads/Shipmode-Department-Sales-300x92.webp 300w, https://www.datagaps.com/wp-content/uploads/Shipmode-Department-Sales-768x235.webp 768w" sizes="(max-width: 874px) 100vw, 874px" /> </div>
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<img loading="lazy" decoding="async" width="849" height="246" src="https://www.datagaps.com/wp-content/uploads/Region-Shipmode-Order.webp" class="attachment-full size-full wp-image-24883" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Region-Shipmode-Order.webp 849w, https://www.datagaps.com/wp-content/uploads/Region-Shipmode-Order-300x87.webp 300w, https://www.datagaps.com/wp-content/uploads/Region-Shipmode-Order-768x223.webp 768w" sizes="(max-width: 849px) 100vw, 849px" /> </div>
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<p>Note, this application works with a Power BI system as well.</p> </div>
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<h3 class="elementor-heading-title elementor-size-default">Profiling Reports
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<p>Profiling a dataset in the DataOps Suite creates checkpoints on nulls, volume, distinct percentages, aggregates, patterns, lengths, extremes, and distributions of the datasets.</p><p>The application allows a user to define which metrics and aggregates to track over the course of multiple runs. The application also catches anomalies using IQR and statistical methods using a set of moving sample records.</p><p>Once the application samples the dataset, upper and lower bounds for all the variable metric aggregates are set, and any anomalies trigger the test case to fail. Users are notified immediately, and the auto-generated reports are presented with the notifications.</p> </div>
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<img loading="lazy" decoding="async" width="960" height="476" src="https://www.datagaps.com/wp-content/uploads/Data-Profile-Options.webp" class="attachment-full size-full wp-image-24935" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Data-Profile-Options.webp 960w, https://www.datagaps.com/wp-content/uploads/Data-Profile-Options-300x149.webp 300w, https://www.datagaps.com/wp-content/uploads/Data-Profile-Options-768x381.webp 768w" sizes="(max-width: 960px) 100vw, 960px" /> </div>
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<h4 class="elementor-heading-title elementor-size-default">Overall DataFlow</h4> </div>
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<img loading="lazy" decoding="async" width="960" height="526" src="https://www.datagaps.com/wp-content/uploads/Profiling-Results.webp" class="attachment-full size-full wp-image-24940" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Profiling-Results.webp 960w, https://www.datagaps.com/wp-content/uploads/Profiling-Results-300x164.webp 300w, https://www.datagaps.com/wp-content/uploads/Profiling-Results-768x421.webp 768w" sizes="(max-width: 960px) 100vw, 960px" /> </div>
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<h4 class="elementor-heading-title elementor-size-default">Profiling Results</h4> </div>
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<img loading="lazy" decoding="async" width="767" height="380" src="https://www.datagaps.com/wp-content/uploads/Metric-Trends.webp" class="attachment-full size-full wp-image-24956" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Metric-Trends.webp 767w, https://www.datagaps.com/wp-content/uploads/Metric-Trends-300x149.webp 300w" sizes="(max-width: 767px) 100vw, 767px" /> </div>
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<h3 class="elementor-heading-title elementor-size-default">ML-based Anomaly Detection </h3> </div>
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<p>While profiling helps keep track of datasets in terms of aggregates and patterns using statistical methods, for proper anomaly detection a machine learning system that works with understanding the seasonality of the metric in question and the dependent variables.</p> </div>
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<p>The application can train itself on a dataset and automatically find our dependent variables or define a time-series type prediction system out of the box.</p><p>An easy-to-define system helps the user allocate the measures, categorization, statistics as of dates, and filter conditions. Based on the categorization, the training weights work differently.</p><p>The application trains itself on the datasets over the course of multiple runs and updates the upper and lower expected bounds of the metrics accordingly. Seasonality and the dependence on other variables also affect the training system over various categorizations and segregations.</p><p>If a metric is found outside the bounds, the test case fails, and a notification is sent to the user stating exactly which metrics in which category are found as anomalies. Reports are automatically generated and can be sent through the expected channel such as Slack, Teams, or email through plugins.</p> </div>
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<img loading="lazy" decoding="async" width="960" height="609" src="https://www.datagaps.com/wp-content/uploads/Detection-Options.webp" class="attachment-full size-full wp-image-24967" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Detection-Options.webp 960w, https://www.datagaps.com/wp-content/uploads/Detection-Options-300x190.webp 300w, https://www.datagaps.com/wp-content/uploads/Detection-Options-768x487.webp 768w" sizes="(max-width: 960px) 100vw, 960px" /> </div>
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<img loading="lazy" decoding="async" width="1357" height="380" src="https://www.datagaps.com/wp-content/uploads/Anomaly-Found.webp" class="attachment-full size-full wp-image-24978" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Anomaly-Found.webp 1357w, https://www.datagaps.com/wp-content/uploads/Anomaly-Found-300x84.webp 300w, https://www.datagaps.com/wp-content/uploads/Anomaly-Found-1024x287.webp 1024w, https://www.datagaps.com/wp-content/uploads/Anomaly-Found-768x215.webp 768w" sizes="(max-width: 1357px) 100vw, 1357px" /> </div>
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<h4 class="elementor-heading-title elementor-size-default">Anomaly Found
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<img loading="lazy" decoding="async" width="1331" height="580" src="https://www.datagaps.com/wp-content/uploads/Anomaly-over-the-course-of-multiple-cadences.webp" class="attachment-full size-full wp-image-24981" alt="" srcset="https://www.datagaps.com/wp-content/uploads/Anomaly-over-the-course-of-multiple-cadences.webp 1331w, https://www.datagaps.com/wp-content/uploads/Anomaly-over-the-course-of-multiple-cadences-300x131.webp 300w, https://www.datagaps.com/wp-content/uploads/Anomaly-over-the-course-of-multiple-cadences-1024x446.webp 1024w, https://www.datagaps.com/wp-content/uploads/Anomaly-over-the-course-of-multiple-cadences-768x335.webp 768w" sizes="(max-width: 1331px) 100vw, 1331px" /> </div>
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<p>Even with validation rules, migration validations, comparisons check, metadata checks, security checks, and performance checks answering the question “Are my reports correct?” requires a look at historical records and patterns. Observability plays a huge role in understanding changing datasets and constantly evolving definitions of data. This should not be limited to raw records or finalized views, but also the reports created by them.</p><p>Using the above methodology, you can achieve an even greater sense of confidence in your reports and trust the dataset and trends based not just on rules and transfer checks, but also on past trends, patterns, and machine learning-based anomaly detection.</p><p>For more info, refer to the webinar – > <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.youtube.com/watch?v=OjWCHoch9Sw&list=PLq-Q4hhL4wuBCDSrxPEzaKEDv11qD5Swr&index=7">Automate The Testing of Tableau using Datagaps BI Validator – Part 2 – YouTube </a></span></p><p>For more info on the various ways you can validate your BI reports in the aforementioned dimensions, be it Tableau or Power BI refer to the following – <span style="text-decoration: underline; color: #1967d2;"><a style="color: #1967d2; text-decoration: underline;" href="https://www.youtube.com/playlist?list=PLq-Q4hhL4wuBCDSrxPEzaKEDv11qD5Swr"> DataGaps Webinars – YouTube</a></span></p> </div>
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<h2 id="faq-heading">Frequently Asked Questions: Tableau Report Observability and Anomaly Detection</h2>
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<details>
<summary>1) How do you pull data directly from Tableau reports for observability?</summary>
<p>
Datasets can be pulled from live Tableau reports using JavaScript, REST APIs, and Selenium as an add-on,
with the ability to parameterize which reports, filters, and workbooks get picked up — the same approach
also works with Power BI.
</p>
</details>
<details>
<summary>2) What does profiling a Tableau dataset check for?</summary>
<p>
Profiling tracks nulls, data volume, distinct value percentages, aggregates, patterns, field lengths,
extremes, and distributions across multiple runs, using IQR and statistical methods to flag anomalies
against a moving sample.
</p>
</details>
<details>
<summary>3) How does ML-based anomaly detection differ from standard profiling?</summary>
<p>
While profiling uses static statistical checkpoints, ML-based detection trains on the dataset’s
seasonality and dependent variables to build a time-series prediction system that adapts its expected
bounds over time.
</p>
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<details>
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<p>The post <a href="https://www.datagaps.com/blog/data-observability-in-tableau-reports/">Data Observability in Tableau Reports</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>Data Profiling In Pharma Datasets Using DataOps Suite</title>
<link>https://www.datagaps.com/blog/data-profiling-in-pharma-datasets-using-dataops-suite/</link>
<dc:creator><![CDATA[Rajesh Kumar]]></dc:creator>
<pubDate>Tue, 14 Feb 2023 13:26:37 +0000</pubDate>
<category><![CDATA[Cloud Data Migration]]></category>
<category><![CDATA[Data Quality]]></category>
<category><![CDATA[Data Validation]]></category>
<category><![CDATA[Dataflow]]></category>
<category><![CDATA[DataOps]]></category>
<category><![CDATA[ETL Testing]]></category>
<guid isPermaLink="false">https://staging9.datagaps.com/?p=11758</guid>
<description><![CDATA[<p>Data profiling is a crucial step in the data management process, especially in the pharmaceutical industry where accurate and reliable data is essential for making informed decisions.</p>
<p>The post <a href="https://www.datagaps.com/blog/data-profiling-in-pharma-datasets-using-dataops-suite/">Data Profiling In Pharma Datasets Using DataOps Suite</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 profiling is a foundational step in pharmaceutical data management that helps identify anomalies, inconsistencies, and quality issues before they affect analytics or regulatory reporting. This guide explains how the <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> automates profiling of pharma datasets by analyzing key patterns, detecting outliers, monitoring data distributions, and tracking list-of-values (LOV) changes. These capabilities help organizations maintain data integrity, improve governance, and ensure reliable clinical, patient claims, and drug sales data for informed decision-making.</p> </div>
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<p><strong>Key Takeaways</strong></p><p> </p><ul><li data-section-id="pmx4mn" data-start="668" data-end="896"><strong data-start="670" data-end="725">Data profiling improves pharmaceutical data quality</strong> by identifying missing values, anomalies, pattern changes, and inconsistencies before they impact downstream analytics or reporting.</li><li data-section-id="1spkyqz" data-start="897" data-end="1120"><strong data-start="899" data-end="934">Monitoring primary key patterns</strong> helps detect unexpected format changes, such as shifts from numeric to alphanumeric identifiers, preventing data integration and governance issues.</li><li data-section-id="1bgj22p" data-start="1121" data-end="1348"><strong data-start="1123" data-end="1170">Outlier detection and distribution analysis</strong> enable teams to identify unusual trends in patient claims, drug pricing, and sales data that may indicate ETL errors or business anomalies.</li><li data-section-id="1he44uz" data-start="1349" data-end="1574"><strong data-start="1351" data-end="1393">Automated profiling with DataOps Suite</strong> provides statistics, distribution analysis, and list-of-values (LOV) tracking to continuously validate pharma datasets and improve data trust.</li></ul> </div>
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<p>In the pharmaceutical industry, it is common for different vendors to provide datasets that contain information on the same subjects or entities. For example, a vendor may provide a dataset containing information on clinical trial participants, while another vendor may provide a dataset containing information on patient outcomes.</p><p>In order to accurately merge or join these datasets, it is important that the primary keys used to identify the subjects or entities are consistent. For example, if one dataset uses a 9-digit numerical key to identify participants, it is important that any other datasets that contain information on the same participants also use a 9-digit numerical key.</p><p>If the pattern of the primary keys is not consistent, it can make it difficult or impossible to accurately link records from different datasets. This can lead to errors or incorrect analyses and can compromise the overall integrity of the data.</p> </div>
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To ensure the consistency of primary keys in pharma datasets, it is important to regularly monitor the patterns of primary keys and identify any potential issues. The DataOps Suite’s profile tracking node?? can be used to monitor the patterns of primary keys and alert you to any inconsistencies. This can help you ensure the quality and integrity of your pharma datasets and avoid any potential issues that could arise from inconsistent primary keys. </div>
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<p>As seen in the example below, originally the only pattern seen in the datasets was a 9-digit key. However, in the latest run post, an update from the client we see a new alphanumeric pattern is also seen in the system. This might indicate a data-type change and a definite notification in data governance.</p> </div>
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<img loading="lazy" decoding="async" width="1374" height="373" src="https://www.datagaps.com/wp-content/uploads/data-profile-node-result-showcasing-a-change-in-the-patterns-of-a-primary-key.png" class="attachment-1536x1536 size-1536x1536 wp-image-11759" alt="data-profile-node-result-showcasing-a-change-in-the-patterns-of-a-primary-key" srcset="https://www.datagaps.com/wp-content/uploads/data-profile-node-result-showcasing-a-change-in-the-patterns-of-a-primary-key.png 1374w, https://www.datagaps.com/wp-content/uploads/data-profile-node-result-showcasing-a-change-in-the-patterns-of-a-primary-key-300x81.png 300w, https://www.datagaps.com/wp-content/uploads/data-profile-node-result-showcasing-a-change-in-the-patterns-of-a-primary-key-1024x278.png 1024w, https://www.datagaps.com/wp-content/uploads/data-profile-node-result-showcasing-a-change-in-the-patterns-of-a-primary-key-768x208.png 768w" sizes="(max-width: 1374px) 100vw, 1374px" /> <figcaption class="widget-image-caption wp-caption-text">DataOps Suite: Data profile node result showcasing a change in the patterns of a primary key</figcaption>
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<h2 class="elementor-heading-title elementor-size-default">Outliers in Patient Claims and Drug Sales Datasets</h2> </div>
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Outliers are values in a dataset that are significantly different from the majority of the other values. In inpatient claims and drug sales datasets, outliers can occur in various aggregates, such as averages, standard deviations, minimum values, and maximum values. </div>
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<p>Outliers can have a significant impact on the results of any analyses or modeling efforts, as they can distort the overall patterns or trends in the data. For example, if a dataset contains an outlier value that is significantly higher or lower than the majority of the other values, it could skew the average or standard deviation, leading to incorrect or misleading results.</p><p><span style="color: #1967d2;"><a style="color: #1967d2;" href="https://www.datagaps.com/etl-testing-tools/etl-validator-download/" target="_blank" rel="noopener"><span style="text-decoration: underline;">Try DataOps Suite – Free Trial</span></a></span></p> </div>
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<p>A few examples of how variations in min-max values and standard deviations can help identify anomalies in patient claims and drug sales datasets:</p><ul><li>If the minimum value for a dataset decreases significantly over time, it could indicate an anomaly or error in the data. For instance, if the minimum value for a column containing drug prices decreases significantly from one month to the next, it could indicate that the price was entered incorrectly or that the drug is being sold at a significantly discounted rate.</li><li>If the maximum value for a dataset increases significantly over time, it could also indicate an anomaly or error in the data. Such as, if the maximum value for a column containing drug prices increases significantly from one month to the next, it could indicate that the price was entered incorrectly or that the drug is being sold at a significantly inflated rate.</li><li>If the standard deviation for a dataset increases significantly over time, it could also indicate an anomaly or error in the data. For example, if the standard deviation for a column containing drug prices increases significantly from one month to the next, it could indicate that the prices are becoming more variable than expected, which could be a sign of an anomaly or error.</li></ul> </div>
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<img loading="lazy" decoding="async" width="877" height="454" src="https://www.datagaps.com/wp-content/uploads/data-profile-node-results.png" class="attachment-1536x1536 size-1536x1536 wp-image-11761" alt="data-profile-node-results" srcset="https://www.datagaps.com/wp-content/uploads/data-profile-node-results.png 877w, https://www.datagaps.com/wp-content/uploads/data-profile-node-results-300x155.png 300w, https://www.datagaps.com/wp-content/uploads/data-profile-node-results-768x398.png 768w" sizes="(max-width: 877px) 100vw, 877px" /> <figcaption class="widget-image-caption wp-caption-text">DataOps Suite: Data Profile Node Results</figcaption>
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<p>Also Read: <a href="https://www.datagaps.com/blog/data-drift-using-dataops-data-profiling/" target="_blank" rel="noopener"><u>Data Drift Using DataOps Data Profiling</u></a></p> </div>
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<h2 class="elementor-heading-title elementor-size-default">Distributions and List of Values Deltas</h2> </div>
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For inpatient claims and drug sales datasets, it is important to monitor the distribution of values across different columns and variables. The DataOps Suite’s profile node can provide various plots and statistics that can help you understand the distribution of values in your data. </div>
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For example, if you are analyzing a dataset containing information on patient claims, you might be interested in the distribution of diagnoses across different diagnosis codes. The profile node can provide a histogram or other plot showing the distribution of diagnosis codes, which can help you identify any patterns or trends in the data. </div>
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<p>In addition to monitoring the distribution of values, it can also be useful to monitor a list of values (LOV) deltas. LOV deltas refer to the difference between the list of values used in one dataset and the list of values used in another dataset. For example, if you are comparing a dataset of patient claims from one year to a dataset of patient claims from the previous year, you might be interested in the LOV deltas between the two datasets.</p> </div>
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The DataOps Suite’s profile node can provide statistics on LOV deltas, which can help you identify any changes in the list of values used in your datasets. This can be useful for ensuring the quality and consistency of your data, and for identifying any potential issues or discrepancies. </p>
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<p><strong>As seen below 2 examples:</strong></p><p> </p><p><strong>Example A</strong> deals with showcasing a change in the number of distinct values seen in a geography key of a patient claims dataset.</p><p> </p><p><strong>Example B</strong> showcases how the distribution of sales among different “Lines of Therapy” has been drastically changed indicating either an issue in the calculation of LOT, a change in behavior of the LOT in the drug in question, or worse a bug in the ETL.</p> </div>
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<h3 class="elementor-heading-title elementor-size-default">Conclusion</h3> </div>
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<p>In conclusion, data profiling is an important step in the data preparation process, and it is especially important in the pharmaceutical industry where data quality and integrity are critical. The DataOps Suite’s profile node is a powerful tool that can help you perform data profiling on your pharma datasets, and it can provide valuable insights and help you identify any potential issues or inconsistencies.</p> </div>
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<p>Some of the key features of the profile <span class="fontColorRed">node</span> include overview statistics, column statistics, and column distribution plots, which can all be useful in understanding the contents, structure, and quality of your data. In addition, the profile node can help you identify anomalies and outliers in your data, and it can provide statistics on LOV deltas, which can be useful for ensuring the consistency of your data.</p> </div>
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<p>Overall, the DataOps Suite’s profile node is a valuable tool that can help you ensure the quality and integrity of your pharma datasets and support more accurate and reliable analyses and modeling efforts.</p> </div>
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<summary>1) What is data profiling in the pharmaceutical industry?</summary>
<p>
Data profiling is the process of analyzing pharmaceutical datasets to understand their structure,
quality, patterns, and distributions. It helps identify anomalies, missing values, inconsistencies,
and data quality issues before the data is used for reporting, analytics, or regulatory compliance.
</p>
</details>
<details>
<summary>2) Why is data profiling important for pharma datasets?</summary>
<p>
Pharma organizations rely on accurate clinical, patient, and drug data to support research,
compliance, and business decisions. Data profiling helps detect inconsistencies, outliers, and
unexpected data changes early, reducing the risk of inaccurate analyses and reporting.
</p>
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<summary>3) What types of data quality issues can data profiling detect?</summary>
<p>
Data profiling can detect changes in primary key patterns, null values, duplicate records,
outliers, unexpected value distributions, and list-of-values (LOV) changes. These insights help
identify ETL issues, data integration problems, and governance risks before they impact downstream
systems.
</p>
</details>
<details>
<summary>4) How does the DataOps Suite support automated data profiling?</summary>
<p>
The Datagaps DataOps Suite automates data profiling by generating column statistics, identifying
outliers, analyzing value distributions, tracking key patterns, and monitoring list-of-values
changes across datasets. This enables continuous monitoring of data quality and faster detection
of anomalies in pharmaceutical data pipelines.
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<p>The post <a href="https://www.datagaps.com/blog/data-profiling-in-pharma-datasets-using-dataops-suite/">Data Profiling In Pharma Datasets Using DataOps Suite</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p>
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