Data Quality on DataAgentX

Data Quality and Validation: Two Gates, Two Risks

Tue, 13 Oct 2026 00:00:00 +0000

If a table has no nulls and no duplicates, can you confidently publish the analysis?

Not necessarily. Even if the data is entirely correct, the calculation might be wrong; even if calculated correctly, the comparison might be flawed, or correlation might be mistaken for causation. These three types of failures occur at different stages and require distinct checks to catch them.

Data Analytics Skills divides these into two skills: Analyze Data Quality to check the data, and Validate Data to check the analysis. These two gates guard against different risks; missing either one leads to leakage.

If the SQL Runs, Can You Trust the Data Agent Answer?

Thu, 10 Sep 2026 00:00:00 +0000

A successful query, attractive chart, and complete report do not independently make an answer trustworthy. The agent may have selected the wrong source, bound the wrong metric, executed correct SQL over incomplete data, and then described correlation as causation.

One accuracy score compresses these failures into a number and hides high-cost errors. A better evaluation follows the analysis chain layer by layer and sends each failure back to the component that can repair it.

The SQL Ran. Why Is the Answer Still Wrong?

Fri, 04 Sep 2026 00:00:00 +0000

The most dangerous result in analytics is often not a failed query. It is a query that runs cleanly, returns a precise number, produces a polished chart—and answers the wrong question.

Suppose an operator asks, “How many new customers did we acquire this month?” The system finds a customer table, counts distinct IDs by created_at, and returns 12,486. The query is reproducible and syntactically correct. But Sales defines a new customer as a first-time payer, while Finance excludes refunded transactions and internal test accounts. The query counted new customer records, not the metric that will drive the budget decision.