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Automated ETL Testing Tool Used In Pharmaceutical Firm

Automated ETL Testing Tool Used In Pharmaceutical Firm

by datamatrix_mm | May 12, 2025 | Case study

A pharmaceutical firm organizes its safety data by pulling information from multiple source systems into a data warehouse. At the time of this writing, the data warehouse holds in the range of 1,500,000 cases. An average case size is roughly about 67 KB of data,...
Health Insurance Provider Utilizes Our Solution to Dramatically Improve Data Quality

Health Insurance Provider Utilizes Our Solution to Dramatically Improve Data Quality

by datamatrix_mm | May 12, 2025 | Case study

A major health insurer was faced with validating large data sets being transformed from flat files, Oracle and SQL Server. Validating these large data sets could not be accomplished with traditional strategies such as minus queries, stare & compare, or Excel...
Insurance Company Utilizes Our Solution to Improve its Data Testing Practices

Insurance Company Utilizes Our Solution to Improve its Data Testing Practices

by datamatrix_mm | May 12, 2025 | Case study

Challenges The Insurance firm’s manual data testing process was using Microsoft Excel and Microsoft Access to perform data comparisons between source flat files and xml files to the target IBM DB2 and Microsoft SQL Server data warehouses. The process took extensive...
Optimizing Financial Data Management for a Banking Institution

Optimizing Financial Data Management for a Banking Institution

by datamatrix_mm | Mar 25, 2025 | Case study

A leading banking institution faced challenges in managing its vast financial data across multiple departments. The lack of a unified data management system resulted in inconsistent financial reporting, regulatory compliance issues, and inefficiencies in data...
Transforming Data Management for a Leading Retail Enterprise

Transforming Data Management for a Leading Retail Enterprise

by datamatrix_mm | Mar 25, 2025 | Case study

A well-established retail enterprise faced challenges in managing vast amounts of customer and operational data. With multiple data sources, inconsistent data quality, and compliance risks, the company struggled to derive meaningful insights for decision-making....

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