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.
We have it backwards.
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.
Stop cleaning the swamp. Stop building the bridge. Stop the garbage at the gate.
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 ?
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