Data quality is not an engineering problem
Most data quality problems are not engineering failures. They are quality decisions nobody made — thresholds inherited by default, definitions that were never agreed, policies that exist only as institutional habit.
Your data platform runs on decisions nobody made
The most consequential data infrastructure gaps are not the decisions that were made badly. They are the decisions nobody made — defaults the business inherited without signing off on.
The data infrastructure gaps that surface when AI moves in
When AI workloads arrive, they expose data infrastructure gaps that years of BI never surfaced. The problem is rarely the model — it is the data layer the model inherited.
The business metrics problem that data quality tools cannot fix
Business metric inconsistency is not a data quality problem. It is what happens when an organisation has never formally agreed on what its key metrics mean — and that is a governance gap, not a technical one.
Data contracts are not the tests you run. They are the agreement you make.
A schema test catches a broken assumption after the fact. A data contract prevents the assumption from being broken in the first place.
Your dashboards are green. That does not mean your data is correct.
Data observability tells you when something broke, but it does not tell you whether the data was ever correct. Confusing the two is one of the most common sources of false confidence in a data platform.
Schema drift is a business risk. Most teams treat it as a technical detail.
A schema change is not just a technical event. When it goes unmanaged, it silently degrades data quality, breaks downstream systems, and makes AI features unpredictable in ways that look like model problems — not data problems.
