The Challenge: A Data Warehouse Without a Safety Net
One of Central Europe’s largest financial institutions was managing vast volumes of customer and transaction data across its Data Warehouse. But on its most critical data streams, there was no standardized QA methodology — no rulebook, no testing structure, no consistent way to verify that the data flowing through the system was accurate, consistent, or historically intact.
For a bank where data integrity isn’t a nice-to-have but a regulatory and operational imperative, this was a serious gap. Each release brought new requirements, and every change carried the risk of silently breaking something that had been working. The question wasn’t if something would slip through — it was when.
The Discovery: Bringing in IDS
IDS Consulting was brought in to build what the bank’s critical streams lacked: a complete QA foundation from scratch — not to improve an existing process, but to create one entirely.
The Solution: Building the QA Backbone
IDS’s QA team became an embedded, stable component of the Data Warehouse program, working side-by-side with the bank’s analysts and developers at every release cycle.
- A standardized testing strategy. We defined clear rules for how data quality would be verified across critical streams — establishing a methodology where none had existed before.
- Unified SQL test templates. Business requirements were translated into structured SQL scripts following a single, consistent format. Readable, repeatable, transferable.
- Automated test execution. We implemented a system that runs our SQL scripts automatically — eliminating manual execution bottlenecks and freeing the team to focus on new requirements rather than repetitive checks.
- Historization testing. We took full ownership of verifying that historical data — years of the bank’s transaction and customer records — remained intact and accurate over time. We designed the scenarios; we held the accountability.
- Defect management & UAT support. Working in close coordination with the bank’s internal team, defects were tracked and managed in Jira. IDS provided hands-on support through UAT and into production, including fast-turnaround retesting during high-pressure releases.
The Battle: Speed, Pressure, and Starting From Zero
Two challenges defined this engagement.
First, the starting point itself. Defining an entirely new testing methodology — agreeing on formats, rules, and responsibilities — required alignment across teams with no prior structure to build on.
Second, the pace. Banking projects don’t pause for QA. Urgent defects had to be identified, retested, and regression-validated under tight deadlines, often within a single release cycle.
The Results: Quality You Can Count On
- Faster, leaner testing cycles. Automated test execution replaced time-consuming manual checks, delivering meaningful capacity gains at every release.
- Early defect detection. Errors caught during testing, not in production — protecting the bank’s data integrity and reducing costly downstream fixes.
- Continuous regression safety. New implementations no longer risk breaking existing, validated functionality. The bank moves fast without breaking things.
- An ongoing, trusted partnership. IDS’s QA team remains a core, active component of the Data Warehouse program — covering every new release with the same rigor, year after year.