Data and Information Inventory and Attributes

Data Quality Dimensions

[Multi-Value]

Description — The data quality dimensions that are formally governed for this type. Not every type requires governance across all six dimensions.

Benefit(s) — Focuses quality governance effort on the dimensions that matter for each specific type. Accuracy matters most for financial data; Completeness for master data; Timeliness for operational data.

Source — Manual.

Examples — Accuracy; Completeness; Consistency; Timeliness; Uniqueness; Validity

Notes — Valid dimensions: Accuracy (values reflect reality), Completeness (required values are present), Consistency (values agree across systems), Timeliness (values are current), Uniqueness (no duplicate records), Validity (values conform to defined format and range). Separate multiple values with semicolons.

Description — The minimum acceptable quality level for each governed dimension — the threshold below which the data quality is considered a governance finding requiring remediation.

Benefit(s) — Creates measurable, auditable quality standards rather than qualitative aspirations. Enables automated quality monitoring against defined thresholds.

Source — Manual.

Examples — Completeness ≥ 98%; Accuracy ≥ 99.5%; Uniqueness = 100% (for primary identifiers)

Notes — Express as a percentage threshold per governed dimension. Thresholds should be set by the Owner in consultation with consuming stakeholders.

Description — The overall governance risk rating for this Data and Information type — the combined assessment of likelihood and impact of data quality failure, unauthorized access, or loss of this type.

Benefit(s) — Surfaces high-risk data types for priority governance attention and investment. The combination of Sensitivity Classification and Assessed Risk produces the data risk profile that drives security and governance investment decisions.

Source — Manual.

Examples — Very High, High, Medium, Low, Very Low

Notes — Valid values: Very High | High | Medium | Low | Very Low. Risk considerations: sensitivity classification, strategic importance, quality threshold compliance, authoritative source maturity, and regulatory exposure.