<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:wfw="http://wellformedweb.org/CommentAPI/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:sy="http://purl.org/rss/1.0/modules/syndication/" xmlns:slash="http://purl.org/rss/1.0/modules/slash/" > <channel> <title>Case Studies Archive - Datagaps | Gen AI-Powered Automated Cloud Data Testing</title> <atom:link href="https://www.datagaps.com/case-study/feed/" rel="self" type="application/rss+xml" /> <link>https://www.datagaps.com/blog/case-study/</link> <description></description> <lastBuildDate>Fri, 28 Aug 2026 06:35:24 +0000</lastBuildDate> <language>en-US</language> <sy:updatePeriod> hourly </sy:updatePeriod> <sy:updateFrequency> 1 </sy:updateFrequency> <generator>https://wordpress.org/?v=7.1</generator> <image> <url>https://www.datagaps.com/wp-content/uploads/favicon.svg</url> <title>Case Studies Archive - Datagaps | Gen AI-Powered Automated Cloud Data Testing</title> <link>https://www.datagaps.com/blog/case-study/</link> <width>32</width> <height>32</height> </image> <item> <title>Automating Financial and Production Data Reconciliation for a U.S. Property Insurer</title> <link>https://www.datagaps.com/case-study/automated-data-reconciliation-insurance/</link> <dc:creator><![CDATA[Rajesh Kumar]]></dc:creator> <pubDate>Fri, 28 Aug 2026 06:32:27 +0000</pubDate> <guid isPermaLink="false">https://www.datagaps.com/?post_type=case_study&p=61078</guid> <description><![CDATA[<p>Customer Profile A major U.S. property insurer managing 600 to 800 data interfaces and pipelines across Oracle, Oracle PeopleSoft, Guidewire, and AWS. Business Challenges Manual reconciliation across multiple teams was time-consuming, inconsistent, and error-prone. Data quality issues surfaced downstream with no proactive validation at system handoffs. Legacy tools and fragmented scripts could not scale with […]</p> <p>The post <a href="https://www.datagaps.com/case-study/automated-data-reconciliation-insurance/">Automating Financial and Production Data Reconciliation for a U.S. Property Insurer</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p> ]]></description> <content:encoded><![CDATA[<p><strong><b>Customer Profile</b></strong></p> <p>A major U.S. property insurer managing 600 to 800 data interfaces and pipelines across Oracle, Oracle PeopleSoft, Guidewire, and AWS.</p> <p><strong><b>Business Challenges</b></strong></p> <ul> <li>Manual reconciliation across multiple teams was time-consuming, inconsistent, and error-prone.</li> <li>Data quality issues surfaced downstream with no proactive validation at system handoffs.</li> <li>Legacy tools and fragmented scripts could not scale with growing data volumes and complexity.</li> </ul> <p><strong><b>How We Solved It</b></strong></p> <ul> <li>Automated source-to-target reconciliation across all financial systems and data pipelines.</li> <li>Deployed a unified validation framework replacing manual comparisons and custom scripts.</li> <li>Enabled exception-based production reconciliation with automated financial controls.</li> </ul> <p><strong><b>Outcomes Delivered</b></strong></p> <ul> <li>90% reduction in test execution time</li> <li>2X expansion in line of business coverage</li> <li>50% reduction in testing team size</li> </ul> <p>The post <a href="https://www.datagaps.com/case-study/automated-data-reconciliation-insurance/">Automating Financial and Production Data Reconciliation for a U.S. Property Insurer</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p> ]]></content:encoded> </item> <item> <title>Strengthening Data Quality and Governance Across Distributed Data Platforms</title> <link>https://www.datagaps.com/case-study/databricks-data-quality-and-governance-across-distributed-data-platforms/</link> <dc:creator><![CDATA[Rajesh Kumar]]></dc:creator> <pubDate>Mon, 27 Jul 2026 12:00:14 +0000</pubDate> <guid isPermaLink="false">https://www.datagaps.com/?post_type=case_study&p=56454</guid> <description><![CDATA[<p>Customer Profile A leading enterprise modernizing its data landscape by consolidating data from SQL Server, Oracle, Salesforce, and flat files into a centralized Databricks Delta Lake. Business Challenges Data was fragmented across multiple systems, creating inconsistent views and reducing trust in business data. Data quality issues were often identified only after reaching reports and downstream […]</p> <p>The post <a href="https://www.datagaps.com/case-study/databricks-data-quality-and-governance-across-distributed-data-platforms/">Strengthening Data Quality and Governance Across Distributed Data Platforms</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p> ]]></description> <content:encoded><![CDATA[<p><strong><b>Customer Profile</b></strong></p> <p>A leading enterprise modernizing its data landscape by consolidating data from SQL Server, Oracle, Salesforce, and flat files into a centralized Databricks Delta Lake.</p> <p><strong><b>Business Challenges</b></strong></p> <ul> <li>Data was fragmented across multiple systems, creating inconsistent views and reducing trust in business data.</li> <li>Data quality issues were often identified only after reaching reports and downstream applications.</li> <li>Scaling data quality enforcement across hundreds of tables and millions of records posed significant operational challenges.</li> </ul> <p><strong><b>How We Solved It</b></strong></p> <ul> <li>Implemented Databricks Delta Lake as a single source of truth for enterprise data.</li> <li>Embedded DataGaps data quality checks directly into bronze, silver, and gold data pipelines.</li> <li>Automated monitoring, incident management, and workflow orchestration to ensure continuous data quality enforcement.</li> </ul> <p><strong><b>Outcomes Delivered</b></strong></p> <ul> <li>60-70% reduction in manual validation effort</li> <li>7,000 data quality rules deployed across 600-700 tables</li> <li>Centralized data quality governance across 30+ projects</li> <li>Zero manual triage for data quality failures</li> </ul> <p>The post <a href="https://www.datagaps.com/case-study/databricks-data-quality-and-governance-across-distributed-data-platforms/">Strengthening Data Quality and Governance Across Distributed Data Platforms</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p> ]]></content:encoded> </item> <item> <title>Automated Data Validation and Cross System Comparison for a Global Hospitality Company</title> <link>https://www.datagaps.com/case-study/automated-power-bi-data-validation-hospitality/</link> <comments>https://www.datagaps.com/case-study/automated-power-bi-data-validation-hospitality/#respond</comments> <dc:creator><![CDATA[Rajesh Kumar]]></dc:creator> <pubDate>Wed, 29 Oct 2025 07:36:38 +0000</pubDate> <guid isPermaLink="false">https://www.datagaps.com/?post_type=case_study&p=40936</guid> <description><![CDATA[<p>Customer Profile A global hospitality organization managing multiple Power BI dashboards and analytics systems. The company sought to automate data validation between Power BI and Dremio while improving reporting accuracy and governance. Business Challenges Manual QA processes across dozens of Power BI dashboards. Ensuring data consistency between Dremio and Power BI reports. Lack of scalable […]</p> <p>The post <a href="https://www.datagaps.com/case-study/automated-power-bi-data-validation-hospitality/">Automated Data Validation and Cross System Comparison for a Global Hospitality Company</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p> ]]></description> <content:encoded><![CDATA[<p><strong><b>Customer Profile<br /> </b></strong><br /> A global hospitality organization managing multiple Power BI dashboards and analytics systems. The company sought to automate data validation between Power BI and Dremio while improving reporting accuracy and governance.</p> <p><strong><b>Business Challenges</b></strong></p> <ul> <li>Manual QA processes across dozens of Power BI dashboards.</li> <li>Ensuring data consistency between Dremio and Power BI reports.</li> <li>Lack of scalable monitoring and proactive issue detection.</li> </ul> <p><strong><b>How We Solved It</b></strong></p> <ul> <li>Automated cross-system validation between Dremio and Power BI.</li> <li>Implemented rule-based monitoring and alerting pipelines.</li> <li>Enabled continuous validation of reports, KPIs, and filters.</li> </ul> <p><strong><b>Outcomes Delivered</b></strong></p> <ul> <li>70% reduction in QA effort.</li> <li>96 automated validation pipelines deployed.</li> <li>100% consistency between reports and source systems.</li> </ul> <p>The post <a href="https://www.datagaps.com/case-study/automated-power-bi-data-validation-hospitality/">Automated Data Validation and Cross System Comparison for a Global Hospitality Company</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p> ]]></content:encoded> <wfw:commentRss>https://www.datagaps.com/case-study/automated-power-bi-data-validation-hospitality/feed/</wfw:commentRss> <slash:comments>0</slash:comments> </item> <item> <title>Driving Retail Success Through Analytics Consolidation, Clarity, and Confidence</title> <link>https://www.datagaps.com/case-study/retail-analytics-consolidation-success/</link> <comments>https://www.datagaps.com/case-study/retail-analytics-consolidation-success/#respond</comments> <dc:creator><![CDATA[Rajesh Kumar]]></dc:creator> <pubDate>Wed, 29 Oct 2025 07:24:53 +0000</pubDate> <guid isPermaLink="false">https://www.datagaps.com/?post_type=case_study&p=40934</guid> <description><![CDATA[<p>Customer Profile A large retail organization operating multiple BI and analytics platforms following several acquisitions. The company sought to consolidate reporting systems, establish a single source of truth, and improve trust in business insights. Business Challenges Fragmented reporting across multiple BI tools and acquired business units. Inconsistent metrics and duplicate reports creating conflicting insights. Lack […]</p> <p>The post <a href="https://www.datagaps.com/case-study/retail-analytics-consolidation-success/">Driving Retail Success Through Analytics Consolidation, Clarity, and Confidence</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p> ]]></description> <content:encoded><![CDATA[<p><strong><b>Customer Profile<br /> </b></strong><br /> A large retail organization operating multiple BI and analytics platforms following several acquisitions. The company sought to consolidate reporting systems, establish a single source of truth, and improve trust in business insights.</p> <p><strong><b>Business Challenges</b></strong></p> <ul> <li>Fragmented reporting across multiple BI tools and acquired business units.</li> <li>Inconsistent metrics and duplicate reports creating conflicting insights.</li> <li>Lack of report ownership and governance impacting decision-making.</li> </ul> <p><strong><b>How We Solved It</b></strong></p> <ul> <li>Consolidated analytics platforms and standardized reporting processes.</li> <li>Created a unified data catalogue with validated business metrics.</li> <li>Established centralized data management, governance, and QA practices.</li> </ul> <p><strong><b>Outcomes Delivered</b></strong></p> <ul> <li>50% increase in development productivity</li> <li>50%+ reduction in analytics costs</li> <li>Eliminated duplicate and uncovered hidden data errors</li> </ul> <p>The post <a href="https://www.datagaps.com/case-study/retail-analytics-consolidation-success/">Driving Retail Success Through Analytics Consolidation, Clarity, and Confidence</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p> ]]></content:encoded> <wfw:commentRss>https://www.datagaps.com/case-study/retail-analytics-consolidation-success/feed/</wfw:commentRss> <slash:comments>0</slash:comments> </item> <item> <title>Scaling Power BI Testing Automation for Pharma Consulting Analytics</title> <link>https://www.datagaps.com/case-study/power-bi-testing-automation-pharma-analytics/</link> <comments>https://www.datagaps.com/case-study/power-bi-testing-automation-pharma-analytics/#respond</comments> <dc:creator><![CDATA[Rajesh Kumar]]></dc:creator> <pubDate>Wed, 29 Oct 2025 06:10:16 +0000</pubDate> <guid isPermaLink="false">https://www.datagaps.com/?post_type=case_study&p=40932</guid> <description><![CDATA[<p>Customer Profile A pharmaceutical consulting organization managing Power BI reporting across multiple clients, seeking to automate dashboard validation and ensure KPI consistency at scale. Business Challenges Manual Power BI validation was time-consuming and difficult to scale. Maintaining KPI consistency across multiple dashboards and client environments. Managing complex validation scenarios involving filters, slicers, and large data […]</p> <p>The post <a href="https://www.datagaps.com/case-study/power-bi-testing-automation-pharma-analytics/">Scaling Power BI Testing Automation for Pharma Consulting Analytics</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p> ]]></description> <content:encoded><![CDATA[<p><strong><b>Customer Profile<br /> </b></strong><br /> A pharmaceutical consulting organization managing Power BI reporting across multiple clients, seeking to automate dashboard validation and ensure KPI consistency at scale.</p> <p><strong><b>Business Challenges</b></strong></p> <ul> <li>Manual Power BI validation was time-consuming and difficult to scale.</li> <li>Maintaining KPI consistency across multiple dashboards and client environments.</li> <li>Managing complex validation scenarios involving filters, slicers, and large data volumes.</li> </ul> <p><strong><b>How We Solved It</b></strong></p> <ul> <li>Automated Power BI dashboard, KPI, and visual validation using BI Validator.</li> <li>Enabled cross-report consistency checks and automated reconciliation testing.</li> <li>Implemented API-triggered validations and reusable test frameworks across multiple pharma clients.</li> </ul> <p><strong><b>Outcomes Delivered</b></strong></p> <ul> <li>70% reduction in manual validation effort</li> <li>80% faster testing turnaround time</li> <li>30–60 minutes saved per report validation cycle</li> </ul> <p>The post <a href="https://www.datagaps.com/case-study/power-bi-testing-automation-pharma-analytics/">Scaling Power BI Testing Automation for Pharma Consulting Analytics</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p> ]]></content:encoded> <wfw:commentRss>https://www.datagaps.com/case-study/power-bi-testing-automation-pharma-analytics/feed/</wfw:commentRss> <slash:comments>0</slash:comments> </item> <item> <title>Ensuring SOX Compliant Financial Reporting for a Global Ticketing Leader</title> <link>https://www.datagaps.com/case-study/sox-compliant-financial-reporting-global-ticketing-leader/</link> <dc:creator><![CDATA[Raj Mohan Achanta]]></dc:creator> <pubDate>Thu, 16 Oct 2025 05:25:58 +0000</pubDate> <guid isPermaLink="false">https://www.datagaps.com/?post_type=case_study&p=40729</guid> <description><![CDATA[<p>Customer Profile A global ticketing company seeking to strengthen SOX compliance by automating financial controls and validating critical financial and customer data across multiple enterprise systems. Business Challenges Data discrepancies across systems increased compliance and audit risks. Manual reconciliation processes were time-consuming and difficult to scale. Limited visibility and traceability across financial reporting workflows. How […]</p> <p>The post <a href="https://www.datagaps.com/case-study/sox-compliant-financial-reporting-global-ticketing-leader/">Ensuring SOX Compliant Financial Reporting for a Global Ticketing Leader</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p> ]]></description> <content:encoded><![CDATA[<p><strong><b>Customer Profile<br /> </b></strong><br /> A global ticketing company seeking to strengthen SOX compliance by automating financial controls and validating critical financial and customer data across multiple enterprise systems.</p> <p><strong><b>Business Challenges</b></strong></p> <ul> <li>Data discrepancies across systems increased compliance and audit risks.</li> <li>Manual reconciliation processes were time-consuming and difficult to scale.</li> <li>Limited visibility and traceability across financial reporting workflows.</li> </ul> <p><strong><b>How We Solved It</b></strong></p> <ul> <li>Automated SOX-focused financial validations using DataOps Suite.</li> <li>Implemented rule-based controls and audit trails for data governance and compliance.</li> <li>Developed anomaly detection dashboards to identify data quality issues and unusual patterns.</li> </ul> <p><strong><b>Outcomes Delivered</b></strong></p> <ul> <li>99%+ reporting accuracy achieved</li> <li>70% reduction in manual reconciliation effort</li> <li>40% faster reconciliation through anomaly detection</li> </ul> <p>The post <a href="https://www.datagaps.com/case-study/sox-compliant-financial-reporting-global-ticketing-leader/">Ensuring SOX Compliant Financial Reporting for a Global Ticketing Leader</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p> ]]></content:encoded> </item> <item> <title>Driving NAIC MAR compliance through automated financial reconciliation</title> <link>https://www.datagaps.com/case-study/naic-mar-compliance-automated-financial-reconciliation/</link> <comments>https://www.datagaps.com/case-study/naic-mar-compliance-automated-financial-reconciliation/#respond</comments> <dc:creator><![CDATA[Rajesh Kumar]]></dc:creator> <pubDate>Wed, 13 Aug 2025 07:40:55 +0000</pubDate> <guid isPermaLink="false">https://www.datagaps.com/?post_type=case_study&p=39190</guid> <description><![CDATA[<p>Customer Profile An insurance organization seeking to automate financial reconciliation and strengthen compliance with NAIC Model Audit Rule (MAR) requirements across legacy and modern data platforms. Business Challenges Manual reconciliation processes made variance analysis slow and resource intensive. Limited visibility into transaction-level discrepancies and audit trails. Difficulty maintaining consistent financial controls across multiple systems. How […]</p> <p>The post <a href="https://www.datagaps.com/case-study/naic-mar-compliance-automated-financial-reconciliation/">Driving NAIC MAR compliance through automated financial reconciliation</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p> ]]></description> <content:encoded><![CDATA[<p><strong><b>Customer Profile<br /> </b></strong><br /> An insurance organization seeking to automate financial reconciliation and strengthen compliance with NAIC Model Audit Rule (MAR) requirements across legacy and modern data platforms.</p> <p><strong><b>Business Challenges</b></strong></p> <ul> <li>Manual reconciliation processes made variance analysis slow and resource intensive.</li> <li>Limited visibility into transaction-level discrepancies and audit trails.</li> <li>Difficulty maintaining consistent financial controls across multiple systems.</li> </ul> <p><strong><b>How We Solved It</b></strong></p> <ul> <li>Automated financial reconciliation using DataOps Suite and embedded reconciliation logic.</li> <li>Enabled drill-down analysis from aggregated variances to transaction-level details.</li> <li>Established a centralized audit repository with real-time compliance monitoring and reporting.</li> </ul> <p><strong><b>Outcomes Delivered</b></strong></p> <ul> <li>~3% premium variance traced from aggregate to transaction-level records</li> <li>Reduced reliance on manual dashboards</li> <li>Audit-ready reconciliation across financial layers</li> </ul> <p>The post <a href="https://www.datagaps.com/case-study/naic-mar-compliance-automated-financial-reconciliation/">Driving NAIC MAR compliance through automated financial reconciliation</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p> ]]></content:encoded> <wfw:commentRss>https://www.datagaps.com/case-study/naic-mar-compliance-automated-financial-reconciliation/feed/</wfw:commentRss> <slash:comments>0</slash:comments> </item> <item> <title>Global Research Firm Enhances Data Validation with DataOps Suite</title> <link>https://www.datagaps.com/case-study/global-research-data-validation-dataops-suite/</link> <comments>https://www.datagaps.com/case-study/global-research-data-validation-dataops-suite/#respond</comments> <dc:creator><![CDATA[Rajesh Kumar]]></dc:creator> <pubDate>Tue, 12 Aug 2025 12:51:54 +0000</pubDate> <guid isPermaLink="false">https://www.datagaps.com/?post_type=case_study&p=39188</guid> <description><![CDATA[<p>Customer Profile A global research and advisory firm seeking to automate data validation and regression testing across large-scale data warehouse environments. Business Challenges Validating data consistency across source and target systems. Managing daily regression testing for over 1,000 tables. Handling complex data formats and migration validation requirements. How We Solved It Automated data comparison, regression […]</p> <p>The post <a href="https://www.datagaps.com/case-study/global-research-data-validation-dataops-suite/">Global Research Firm Enhances Data Validation with DataOps Suite</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p> ]]></description> <content:encoded><![CDATA[<p><strong><b>Customer Profile<br /> </b></strong><br /> A global research and advisory firm seeking to automate data validation and regression testing across large-scale data warehouse environments.</p> <p><strong><b>Business Challenges</b></strong></p> <ul> <li>Validating data consistency across source and target systems.</li> <li>Managing daily regression testing for over 1,000 tables.</li> <li>Handling complex data formats and migration validation requirements.</li> </ul> <p><strong><b>How We Solved It</b></strong></p> <ul> <li>Automated data comparison, regression testing, and duplicate detection using DataOps Suite.</li> <li>Compared pre- and post-migration of Power BI dashboards.</li> <li>Integrated API and Power BI validation to ensure end-to-end data accuracy.</li> </ul> <p><strong><b>Outcomes Delivered</b></strong></p> <ul> <li>50% reduction in manual data validation effort</li> <li>Improved data accuracy through automated inconsistency detection</li> <li>Enhanced data observability and monitoring capabilities</li> </ul> <p>The post <a href="https://www.datagaps.com/case-study/global-research-data-validation-dataops-suite/">Global Research Firm Enhances Data Validation with DataOps Suite</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p> ]]></content:encoded> <wfw:commentRss>https://www.datagaps.com/case-study/global-research-data-validation-dataops-suite/feed/</wfw:commentRss> <slash:comments>0</slash:comments> </item> <item> <title>Fintech Industry Leader</title> <link>https://www.datagaps.com/case-study/fintech-industry-leader/</link> <dc:creator><![CDATA[Rajesh Kumar]]></dc:creator> <pubDate>Tue, 03 Sep 2024 13:08:51 +0000</pubDate> <guid isPermaLink="false">https://www.datagaps.com/?post_type=case_study&p=33387</guid> <description><![CDATA[<p>Customer Profile A leading FinTech innovator managing AI-driven investment strategies and institutional portfolios. The company relied on large volumes of data to power AI/ML models and deliver accurate financial insights. Business Challenges Poor data quality impacting AI/ML model accuracy. Manual data validation and correction processes. Maintaining data integrity across multiple data sources. How We Solved […]</p> <p>The post <a href="https://www.datagaps.com/case-study/fintech-industry-leader/">Fintech Industry Leader</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p> ]]></description> <content:encoded><![CDATA[<p><strong><b>Customer Profile<br /> </b></strong><br /> A leading FinTech innovator managing AI-driven investment strategies and institutional portfolios. The company relied on large volumes of data to power AI/ML models and deliver accurate financial insights.</p> <p><strong><b>Business Challenges</b></strong></p> <ul> <li>Poor data quality impacting AI/ML model accuracy.</li> <li>Manual data validation and correction processes.</li> <li>Maintaining data integrity across multiple data sources.</li> </ul> <p><strong><b>How We Solved It</b></strong></p> <ul> <li>Automated end-to-end data quality validation across the pipeline.</li> <li>Implemented rule-based checks to detect and prevent data issues early.</li> <li>Enabled pre-production validation while reducing manual testing effort.</li> </ul> <p><strong><b>Outcomes Delivered</b></strong></p> <ul> <li>Achieved 100% automated testing coverage for all migrated data</li> <li>50-65% reduction in migration testing time</li> <li>35% reduction in Total Cost of Ownership</li> </ul> <p>The post <a href="https://www.datagaps.com/case-study/fintech-industry-leader/">Fintech Industry Leader</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p> ]]></content:encoded> </item> <item> <title>Fortune 100 Financial Services Company Automates Snowflake Data Pipeline Validation</title> <link>https://www.datagaps.com/case-study/fortune-100-financial-services-company/</link> <dc:creator><![CDATA[Rajesh Kumar]]></dc:creator> <pubDate>Sat, 22 Jun 2024 09:06:46 +0000</pubDate> <guid isPermaLink="false">https://www.datagaps.com/?post_type=case_study&p=31269</guid> <description><![CDATA[<p>A Fortune 100 Financial Services company embarked on a large-scale modernization project to migrate their data warehouse to Snowflake.</p> <p>The post <a href="https://www.datagaps.com/case-study/fortune-100-financial-services-company/">Fortune 100 Financial Services Company Automates Snowflake Data Pipeline Validation</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p> ]]></description> <content:encoded><![CDATA[<p><strong><b>Customer Profile<br /> </b></strong><br /> A Fortune 100 financial services company modernizing its data warehouse by migrating complex Mainframe and Oracle data to Snowflake.</p> <p><strong><b>Business Challenges</b></strong></p> <ul> <li>Validating large-scale data migrations.</li> <li>Managing complex Mainframe data structures.</li> <li>Ensuring data quality across Snowflake layers.</li> </ul> <p><strong><b>How We Solved It</b></strong></p> <ul> <li>Automated migration validation and reconciliation testing.</li> <li>Generated test cases at scale across thousands of tables.</li> <li>Enabled end-to-end testing across Snowflake’s Medallion Architecture.</li> </ul> <p><strong><b>Outcomes Delivered</b></strong></p> <ul> <li>100% automated test coverage</li> <li>Up to 35% faster migration delivery</li> <li>45–60% less testing effort</li> </ul> <p>The post <a href="https://www.datagaps.com/case-study/fortune-100-financial-services-company/">Fortune 100 Financial Services Company Automates Snowflake Data Pipeline Validation</a> appeared first on <a href="https://www.datagaps.com">Datagaps | Gen AI-Powered Automated Cloud Data Testing</a>.</p> ]]></content:encoded> </item> </channel> </rss>