Why This Matters
Most data quality problems in mid-market companies are not technical — they are definitional. Two departments calculate the same metric differently, a report uses a field that no one fully understands, or a system migration changes a field’s meaning without anyone updating downstream reports. A data dictionary prevents this by creating a single, maintained source of truth for what every data element means. It is foundational infrastructure for any company that wants trustworthy, consistent financial data.
Where This Fits
This term sits within the Data Governance & AI Readiness area of Performance & Control.