Rethinking Data Quality for Modern Engineering

There’s a growing gap in modern quality engineering. Release cycles are accelerating, architectures are more distributed, and teams are under pressure to support AI-driven systems. Yet many test data strategies remain anchored in masked or copied production data—a model that is no longer keeping pace.

This approach introduces risk, limits coverage, and slows delivery. More importantly, it reflects an outdated assumption that data is something to manage and reuse. In reality, data has become an active part of how systems are built, tested, and validated.

The cost of not evolving is significant. Poor data quality costs organizations an average of $12.9 million annually, but the larger impact is seen in missed defects, delayed releases, and constrained innovation.

A new model is emerging—one that treats data as something that must be engineered. Data quality is no longer just about accuracy, but about enabling continuous testing, scaling across environments, representing edge conditions, and eliminating privacy risk by design.

This shift is what defines Data Quality Evolution.

This week’s article outlines the 10 dimensions that define this model—and what it takes to move from legacy data strategies to engineered data.