Assessment and Health attributes for the Data and Information Inventory - Data and Information Inventory and Attributes

Assessment and Health attributes for the Data and Information Inventory

(Chapter 15 of Data and Information Inventory and Attributes)

Executive Summary: Chapter Overview

IF4IT

The Bottom Line

This chapter defines assessment and health attributes that measure the quality, completeness, accuracy, trustworthiness, and governance maturity of Data and Information records. These attributes help practitioners move beyond cataloging names toward managing whether the inventory can be trusted for impact analysis, compliance, AI use, and decision support.

Core Concepts

Concept	Definition & Strategic Role
Data Quality Health	Data quality health summarizes whether the data type meets accuracy, completeness, consistency, and timeliness expectations. It helps prioritize remediation and stewardship effort.
Inventory Completeness	Completeness indicates whether required attributes are populated for the record. It reveals whether the inventory is mature enough to support governance, analysis, and automation.
Trustworthiness	Trustworthiness reflects confidence in the definition, source, stewardship, and supporting evidence for a data type. It is especially important when AI agents use the inventory for reasoning.

Quick Q&A

Question: What do assessment and health attributes reveal that descriptive attributes do not?

Answer: Descriptive attributes identify and explain the data type, while assessment and health attributes indicate whether the record and the underlying governance are reliable. They expose quality gaps, missing metadata, stale definitions, and low-confidence records that should not be trusted for high-stakes analysis without remediation.

Assessment and Health attributes capture the evaluated quality, risk, and governance health of each Data and Information type.

Attribute Name	Maturity	Description and Notes
Data Quality Dimensions [Multi-Value]	Walk	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.
Quality Threshold	Walk	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.
Assessed Risk	Walk	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.

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