Stop polishing bad data. Start building it right from the first principle.
You enforce quality at the point of creation or ingestion. If a record doesn’t meet the first principles of your domain (e.g., timestamp cannot be in the future; customer_id must match a regex), it is rejected immediately. The rule: Do not allow a known violation to enter your persistent storage. Ever. 2. The "Nullable Integer" Paradox Let’s look at a classic first-principles failure: Nulls in numeric fields.
Ab initio (Latin for "from the beginning") means starting from first principles. In a quantum simulation, you don't patch errors later—you define the laws of physics upfront. If your initial conditions are wrong, the simulation is worthless. ab initio data quality
Use tools like pydantic (Python), Great Expectations (with expect_column_values_to_not_be_null set to fatal ), or dbt 's constraints (enforced, not just documented). If the contract fails, the pipe breaks. Loudly.
Ab Initio Data Quality: Why You Can’t Fix Rubbish Later Stop polishing bad data
If you work in data long enough, you’ve heard the mantra: “Garbage In, Garbage Out.” We all nod in agreement. Then, we build complex pipelines with 47 validation steps, six months of cleaning scripts, and a "trust but verify" dashboard that nobody actually reads.
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 ? If a record doesn’t meet the first principles
We have it backwards.