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The Silent Data Quality Problem

Bad data is rarely dramatic. It does not cause a system outage or a visible error. It appears in a dashboard as a number that is slightly wrong — 9.4 million in revenue instead of 10.2 million, because two days of records were dropped by a pipeline that retried without deduplication. It appears as a customer count that is 3% higher than the actual count because a migration introduced duplicates that no one caught. It appears as a report that the finance team stopped trusting — not because anyone proved it was wrong, but because the numbers don't match what they expected, and nobody can explain why.

Data quality problems erode trust. Once business stakeholders stop trusting a dashboard, they stop using it — and the data platform investment delivers no value regardless of how technically sound the underlying infrastructure is.

A data quality framework prevents this by making data problems visible and actionable before they reach business consumers.

The Four Dimensions We Address

Completeness

Is the data all there? Are records missing? Are required fields null when they should not be? Completeness checks identify gaps in extraction (source system records not captured), gaps in transformation (records dropped by a transformation error), and gaps in loading (partial loads that didn't complete).

Validity

Does the data conform to expected formats, ranges, and business rules? A date column that contains future dates for historical records. A price column that contains negative values. An order status that contains a value not in the valid status list. Validity checks surface data that has been technically ingested but is logically incorrect.

Freshness

Is the data as current as it should be? A dashboard that claims to show "yesterday's sales" but is actually showing data from three days ago is a data quality problem. Freshness monitoring tracks when each table was last successfully updated and alerts when tables fall behind their expected refresh schedule.

Consistency

Does the data agree with itself across tables and datasets? Total order count in the orders table matches the sum of line items in the order lines table. Customer count in the CRM export matches the customer dimension in the warehouse. Consistency checks detect integration failures and transformation bugs that cause the same entity to be counted differently in different places.

Tools We Use

dbt tests: schema tests (not null, unique, accepted values, referential integrity) and custom data tests configured in the dbt project alongside the transformation models. Cloud Dataplex data quality rules for warehouse-level validation. Google Cloud Monitoring for freshness SLA alerting. Great Expectations for organizations that need a standalone data quality framework outside of dbt.