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The security review took longer than the migration itself. I was auditing a client’s Power BI environment: 47 static RLS roles, each with its own DAX filter expression, each maintained by a different team, none of them connected to the data layer. When an analyst queried the same tables directly from a notebook, the filters simply didn’t apply. Two security models, one dataset, zero consistency.

Data engineering teams spend enormous energy building reliable pipelines – clean medallion layers, solid transformation logic, well-tuned Spark jobs. Then something breaks in production that worked perfectly in development. A library version changed. An environment variable was missing. A Spark executor launched with a subtly different runtime than the one the job was built against.

Modern data teams are surrounded by success signals that no longer mean very much. Dashboards show pipelines running on schedule. Jobs complete within SLAs. Infrastructure metrics glow green. And yet, business stakeholders still don’t trust the numbers.

Going into 2026, where AI models that help shape critical business decisions based on the data they are fed, data quality is of more importance than ever. According to reports, 94% of respondents see incorrect data negatively impacting their organization. Databricks, which is one of the largest modern data processing platforms, offers many built-in options for data quality, but none are fully comprehensive.

Data governance has emerged as a critical component in the realm of data management. As organizations realize the potential value of data as a strategic asset, it’s becoming evident that governing this data efficiently is of paramount importance.