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Metadata, Catalog and Lineage Maturity Assessment

A 44-question diagnostic across 7 domains that evaluates whether people and machines can find the organization's data, understand what it means and see where it came from: metadata standards, the business glossary, catal...

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Overview

A 44-question diagnostic across 7 domains that evaluates whether people and machines can find the organization's data, understand what it means and see where it came from: metadata standards, the business glossary, catalog coverage and curation, lineage and impact analysis, operational metadata, discovery and adoption, and AI and active metadata. Written for the head of data governance or data architecture who needs to answer: can people and machines find our data, trust what it means and see where it came from?

Detailed Description

Many organizations buy a data catalog, scan their main databases and then watch usage fade: definitions are missing or disputed, owners are blank, lineage stops at the warehouse and nobody checks whether the metadata is still true. This assessment tests whether metadata is managed as a product: a plan and metamodel, clear naming and definition rules, an approved glossary with critical data elements, a catalog that covers the estate and is curated and certified, lineage that is captured automatically, validated and used before change, operational signals that show whether data is fresh and fit, and adoption that is measured. It also tests the newest uses of metadata: dataset cards, AI-drafted metadata with human review, governed access for AI agents, metadata as trusted context for AI assistants, and controls driven automatically by metadata tags. It is a deep dive in the ITSG data family. Ownership, critical data criteria and literacy come from Data Governance and Management; quality rules stay with Master Data and Data Quality Management; classification levels stay with Data Classification and Handling. The model is neutral to jurisdiction and vendor.

Assessment Details

Audience

Primary: head of data governance, data architecture lead and the catalog product owner. Secondary: business data owners and stewards, data engineers, analytics and AI teams, security and privacy teams and internal audit.

Purpose

Shows whether metadata is complete, trusted and used, where the catalog exists only as a tool rather than a working practice, and which gaps to close first. Gives the data office a prioritized roadmap for the glossary, catalog and lineage, and a baseline that Master Data, Classification and AI teams can rely on.

Effort

Initial assessment: 8 to 10 hours of facilitated sessions over one to two weeks with the data office, two or three stewards, data architecture and engineering, analytics and AI leads, and security. Evidence gathering: the metadata plan and standard, the glossary and critical data element list, catalog exports and coverage reports, lineage samples, change requests and usage analytics. Start with a 30-minute session with every area for the three all-area questions. Reassessment: 4 to 5 hours.

Cadence

Annually, and after a catalog platform change, a major data platform migration, a new AI program or a significant data incident traced to missing or wrong metadata.

Framework Alignment

ISO/IEC 11179-1:2023 ISO/IEC 11179-3:2023 DAMA-DMBOK2 Revised COBIT 2019 W3C DCAT Version 3 OpenLineage specification ISO 15836-1:2017 ISO 8000-150:2022 ISO 8000-61:2016 ISO/IEC 8183:2023 Open Data Contract Standard v3.1.0 ISO/IEC 42001:2023 ISO/IEC 5259-1:2024 MLCommons Croissant 1.1 NIST AI RMF 1.0 OWASP Top 10 for Agentic Applications 2026 ISO/IEC 27001:2022

KPIs

  • Catalog Coverage - Share of in-scope systems with their data assets registered in the catalog. Target: at least 90%.
  • Asset Record Completeness - Share of catalog assets with all required attributes, including a named owner. Target: at least 90%.
  • Critical Element Lineage - Share of critical data elements with complete column-level lineage from source to consumer. Target: 100%.
  • Changes with Impact Analysis - Share of data-affecting changes with a lineage-based impact analysis before approval. Target: at least 95%.
  • Active Catalog Users - Share of target data users who used the catalog in the last 90 days. Target: at least 70%.

Dependencies

Data Governance and Management (v1.9.6): owns the Data Ownership Register, critical data criteria, charter, strategy and literacy program; this model uses them as Inputs (1.1, 1.2, 1.5, 2.1, 2.3, 3.3, 6.3, 6.4). Enterprise Architecture (v7.9.6): owns the Enterprise Information Model; glossary terms align with its subject areas (2.6). EA cites the Business Glossary, Approved Terms section owned here. AI Governance (v11.9.6): owns the AI Dataset Catalog; this model links AI lineage and dataset cards to it rather than duplicating it (4.7, 7.1). AI Pipeline and MLOps (v11.9.6): captures catalog entries and lineage for ML pipelines using this model's Metadata Standard (4.2, 4.7). Data Classification and Handling (planned rebuild): owns classification levels and labels; this model tests that they are shown on assets and drive controls (6.2, 7.5). Master Data and Data Quality Management (planned rebuild): owns quality rules and uses the critical data element list held in the Business Glossary (2.3, 5.3). Data Engineering, Platforms and Observability (v1.9.6) and Analytics, BI and Insight Delivery (v1.9.6): own pipeline observability and metric definitions; this model tests that their signals and lineage reach the catalog (5.1, 5.4, 4.6). Recommended: Run after Data Governance and Management; run before Master Data and Data Quality Management and Data Classification and Handling so they can rely on the glossary, catalog and lineage.

Assessment Outline

(7 domains · 44 questions)
  1. 1

    1 Metadata Strategy and Standards

    Metadata management plan, roles and ownership of metadata, metamodel, naming and definition rules, required attributes, tool strategy and open standards

    • 1.1 Metadata Management Plan 1 questions
    • 1.2 Metadata Roles and Ownership 1 questions
    • 1.3 Metamodel 1 questions
    • 1.4 Naming and Definition Rules 1 questions
    • 1.5 Required Attributes 1 questions
    • 1.6 Metadata Tool Strategy 1 questions
  2. 2

    2 Business Glossary and Semantics

    Term workflow, approved terms, critical data elements, synonyms and hierarchies, glossary use in delivery, alignment with the enterprise information model, semantic model for AI

    • 2.1 Glossary Term Workflow 1 questions
    • 2.2 Approved Terms 1 questions
    • 2.3 Critical Data Elements 1 questions
    • 2.4 Synonyms and Hierarchies 1 questions
    • 2.5 Glossary Use in Delivery 1 questions
    • 2.6 Information Model Alignment 1 questions
    • 2.7 Semantic Model for AI 1 questions
  3. 3

    3 Data Catalog Coverage and Curation

    Catalog scope and coverage, automated harvesting, asset record completeness, term-to-asset links, curation workflow, certification, deprecation

    • 3.1 Catalog Scope and Coverage 1 questions
    • 3.2 Automated Harvesting 1 questions
    • 3.3 Asset Record Completeness 1 questions
    • 3.4 Term-to-Asset Links 1 questions
    • 3.5 Curation Workflow 1 questions
    • 3.6 Certification and Endorsement 1 questions
    • 3.7 Deprecation and Retirement 1 questions
  4. 4

    4 Lineage and Impact Analysis

    Lineage requirements, automated capture, column-level lineage for critical data elements, lineage validation, impact analysis before change, report lineage, lineage to AI models

    • 4.1 Lineage Requirements 1 questions
    • 4.2 Automated Lineage Capture 1 questions
    • 4.3 Critical Element Lineage 1 questions
    • 4.4 Lineage Validation 1 questions
    • 4.5 Impact Analysis Before Change 1 questions
    • 4.6 Report and Metric Lineage 1 questions
    • 4.7 Lineage to AI Models 1 questions
  5. 5

    5 Operational Metadata and Observability

    Freshness, volume and run metadata, profiling statistics, quality status and data contracts in the catalog, schema change alerts, quality of the metadata itself

    • 5.1 Operational Metadata Capture 1 questions
    • 5.2 Profiling Statistics 1 questions
    • 5.3 Quality Status and Data Contracts 1 questions
    • 5.4 Schema Change Alerts 1 questions
    • 5.5 Metadata Quality Monitoring 1 questions
  6. 6

    6 Discovery, Adoption and Performance

    Search and discovery, classification shown on assets, access requests, role-based adoption, usage analytics and feedback, performance reporting and improvement

    • 6.1 Search and Discovery 1 questions
    • 6.2 Classification Shown on Assets 1 questions
    • 6.3 Access Requests 1 questions
    • 6.4 Role-Based Adoption 1 questions
    • 6.5 Usage Analytics and Feedback 1 questions
    • 6.6 Performance Reporting and Improvement 1 questions
  7. 7

    7 AI and Active Metadata

    Dataset cards, AI-generated metadata with human review, agent-readable catalog, metadata as AI context, metadata-driven automation, cataloging AI-created data

    • 7.1 Dataset Cards 1 questions
    • 7.2 AI-Generated Metadata Review 1 questions
    • 7.3 Agent-Readable Catalog 1 questions
    • 7.4 Metadata as AI Context 1 questions
    • 7.5 Metadata-Driven Automation 1 questions
    • 7.6 Cataloging AI-Created Data 1 questions

At a Glance

Category Data and Analytics
Type Free
Roles 6
Domains 7
Questions 44
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