Hello everyone,
I have been observing discussions here for some time and have finally encountered a situation in our own migration project that compels me to seek the community's insight. We recently completed a migration from a legacy reporting system to Power BI, focusing on replicating our core sales and inventory dashboards. The technical migration of the data itself, which involved moving from a proprietary data warehouse to Azure SQL, was deemed successful by our engineering team. The ETL processes were validated for row counts and basic schema alignment.
However, now that we are attempting to rebuild the reports in Power BI, we are discovering persistent discrepancies in the aggregated figures. The summarized totals—particularly for monthly revenue and regional inventory turnover—are consistently between 5% and 15% lower than the historical reports generated by the old system. This suggests a data integrity issue that was not caught by simple row-count verification.
My primary question revolves around the methodology for validating data integrity beyond basic structural checks. Given that our legacy system’s business logic was somewhat opaque, embedded in the reporting tool itself, I suspect the issue may lie in one of the following areas:
* **Transformation Logic:** Differences in how measures like "revenue" are calculated (e.g., handling of returns, discounts, or fiscal periods) between the old ETL and the new one.
* **Data Granularity:** The possibility of duplicate records being handled differently (e.g., deduplication at the source vs. in the report).
* **Temporal Alignment:** Misalignment in effective dates or snapshot logic for inventory data.
We followed a documented cutover plan with a two-week parallel run, but the depth of our comparison was insufficient. The transition, from final data extraction to having the new data pipeline declared operational, took approximately three weeks. The report reconstruction is now in its fourth week and stalled by this issue.
Could anyone share a structured approach or specific validation steps they employed in a similar migration—perhaps from tools like Cognos or Business Objects to a modern BI platform—to ensure semantic equivalence and not just syntactic data transfer? What are the critical points in the data flow where one should audit the logic, not just the data?
Ah, the classic "row counts match but the numbers don't" migration gut-punch. Been there.
You've nailed the core issue: row counts and schema checks are just the first layer. The real devil is in the semantics - the hidden business logic in that old system. It's not just about moving data, it's about moving meaning.
For a start, I'd go back to the most granular data you can and run a differential analysis. Don't just compare aggregated totals. Pick a specific month and region where you see a 15% variance and trace it all the way down. Compare record-by-record at the transaction or daily snapshot level between the old system's source export (if you have it) and your new Azure SQL tables. You'll often find differences in how things like refunds, cancellations, or status changes were logically handled (or excluded) in the old reports.
Also, check your date/time handling and your "active record" logic. Time zones, closed periods, and how you handle updates to historical records can silently chop out chunks of data. Good luck - this is the tedious but crucial part
K8s enthusiast