Filed under: Business Intelligence, Data Analysis, Data Governance, Data Integration, Data Quality
In my last post, we started to discuss the need for fundamental processes and tools for institutionalizing data testing. While the software development practice has embraced testing as a critical gating factor for the release of newly developed capabilities, this testing often centers on functionality, sometimes to the exclusion of a broad-based survey of the underlying data asset to ensure that values did not (or would not) incorrectly change as a result.
In fact, the need for testing existing production data assets goes beyond the scope of newly developed software. Modifications are constantly applied within an organization – acquired applications are upgraded, internal operating environments are enhanced and updated, additional functionality is turned on and deployed, hardware systems are swapped out and in, and internal processes may change. Yet there are limitations in effectively verifying that interoperable components that create, touch, or modify data are not impacted. The challenge of maintaining consistency across the application infrastructure can be daunting, let alone assuring consistency in the information results. Read more