The Data Quality Metrics You're Using Are from 2020. Here's Why Your Pipeline is Broken

If your primary data quality metric is the percentage of null values, you have a problem.

It's not because it's a bad metric. It's because it's insufficient, and by 2026, there will be no excuse for this.

Most data teams inherit pipelines built when the goal was to move data from A to B without it exploding. This worked. But today, these same pipelines feed language models, recommendation systems, and autonomous agents. The standard has changed. The metrics, not so much.

The problem is not about data quality but about the illusion of cleanliness.

A dataset with 0% null values and 0% duplicates could still be garbage. Why? Because classic metrics don't capture:

• Distribution drift: your today's data doesn't resemble training data.
• Inconsistencies between sources: the CRM says one thing, the data warehouse says another.
• Plausible but incorrect values: a $150,000 income raises no alarms, but it might be in the wrong currency.
• Broken relationships: a foreign key exists but points to an obsolete record.

None of these failures appear in a completeness or uniqueness report.

A concrete example.

An e-commerce team measured quality with traditional metrics: completeness, uniqueness, format. Everything was green. The trained recommendation model started to degrade silently three months after deployment.

The problem: a supplier changed product category coding without notice. The data remained complete and unique. But the semantics were different. The model never knew.

What metrics to incorporate today.

It's not about throwing away what you have. It's about adding layers that weren't necessary before:

1. Data freshness with SLA per entity: it's not the same if a user is outdated for 2 hours as compared to a financial transaction.
2. Production drift monitoring: tools like Evidently or Nannyml allow detecting distribution changes before they impact results.
3. Semantic validations: Great Expectations or Soda can define data contracts that go beyond type and format.
4. Lineage and traceability: knowing where each field comes from is no longer a luxury, it's a requirement for debugging when something goes wrong.
5. Test coverage based on criticality: not all fields deserve the same level of validation. Prioritize according to the impact on business decisions.

The bottom line.

Data quality stopped being an engineering problem and became a system reliability problem. When a model makes decisions in production, the quality of data feeding it has real consequences: incorrect credit approvals, biased diagnoses, recommendations that repel instead of retain customers.

You used the metrics you used because they were sufficient at the time. But the time has changed.

Where to start.

Review the most critical pipeline you have today. Identify what validations exist. Ask if these validations would detect the problem mentioned in the e-commerce example earlier.

If the answer is no, you now know where to start.