BI Testing: Poor Data Quality Now Means Big Data Problems Later


BI Testing: Poor Data Quality Now Means Big Data Problems Later

On-Demand Webinar

Now that businesses rely on BI to drive strategy and optimizations, data integrity problems rapidly ripple throughout the company. Reliance on inaccurate data quality not only leads to less effective decision making; it also opens the door to regulatory risks. Data integrity could be compromised from the moment it’s created, as its integrated and moved, or as its transformed. However, data quality testing is usually deferred until the end of the process—as business users manually inspect reports. In the best case scenario, the problem is detected, but lengthy late-process debugging may be required to identify and resolve its source. In the worst case scenario, the problem goes unnoticed. In that case, the compromised data is appropriated into business operations, and any underlying process problems may continue to impact data quality.

If preventing such “big data problems” is critical for your business, join Tricentis and Reeeliance to discover the latest BI testing strategies. We’ll explore best practices and technologies for systematically eliminating data integrity issues at each stage of the process—across ETL layers, the core data warehouse, and BI layers. You will learn:

  • About the various ways that data integrity can be compromised
  • How investing a little time on test automation now will yield tremendous time savings over the long term
  • How business analysts can define and interpret automated data tests on their own—without relying on technical specialists
  • Why and how to incorporate BI/data warehouse testing into your end-to-end business process testing

Alain Traechslin, Product Manager, Tricentis

Christian Ossenberg, Senior Consultant, reeeliance IM GmbH

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