Ab Initio Data Quality: Why You Can’t Fix Rubbish Later
Most data teams focus on reactive data quality (DQ). They let data in, then scramble to fix it. But what if we borrowed a concept from theoretical chemistry and quantum physics? What if we focused on ? ab initio data quality
Here is why your data pipeline needs an ab initio mindset shift. Reactive DQ is expensive. You pay the cost of ingesting the data, storing it, processing it, and then again for the engineer who backfills it, and again for the analyst who mistrusts the result. Ab Initio Data Quality: Why You Can’t Fix
Audit your warehouse. Pick one critical table. Enforce NOT NULL on every single column. If you truly need a missing value, use a sentinel row (e.g., id = 0 , name = "UNKNOWN" ). You will be shocked how many bugs disappear. What if we focused on
Stop polishing bad data. Start building it right from the first principle.
We have it backwards.
