Data and Analytics Assessment

Master Data and Data Quality Management Maturity Assessment

A maturity assessment of master data management and data quality, from program scope and stewardship through standards, hub architecture, quality measurement, entry controls, matching and golden records to issue resolution and improvement.

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At a glance

Questions: 53
Domains: 8
Working time: Initial assessment: 10 to 12 hours of facilitated sessions over two weeks with the master data office, two or three domain owners and stewards, data architecture, data quality and security, starting with a 30-minute session with every area for the four all-area questions. Evidence gathering: the program plan, standards, model, system of record register, blueprint, quality framework and rules catalog, scorecards, issue register, match specification and samples of records, merges and changes. Reassessment: 5 to 6 hours.
Repeat: Annually, and after a hub implementation or replacement, a merger or major system migration, a significant data quality incident or the start of AI use of master data.
Roles: 6
Updated: October 2026

Who it is for

Master Data Office
Business Data Owners and Stewards
Data Architecture and Integration
Data Quality and Analytics
Security, Privacy and Compliance
Process Owner/Full Model Assessor

Primary: head of master data management, data quality lead and the MDM team. Secondary: business data owners and stewards, data and integration architects, analytics and AI teams, security, privacy and compliance, finance and internal audit.

What you will gain

Shows where master data is trusted and where duplicates, gaps and silent errors are costing the business, which controls exist only on paper, and which fixes pay back first. Gives the master data office an evidence-based roadmap and gives analytics, AI and regulatory reporting teams a clear view of how far they can rely on master data.

About this assessment

Duplicate suppliers, customers with three addresses and products coded differently in every system cost money every day: wrong payments, failed deliveries, rejected regulatory returns and AI models trained on inconsistent data. This assessment tests whether master data is run as a discipline: a funded program with clear domain priorities and stewards, attribute standards and a governed model with permanent identifiers, a designated system of record and a hub that publishes trusted records within agreed times, a quality framework with rules on every critical data element, controlled entry and high-risk change, sound matching and survivorship with audited golden records, and an issue process that fixes causes rather than symptoms. It also tests new risks: AI agents and bots writing master data, AI-assisted matching, and quality for analytics and machine learning. It is a deep dive in the ITSG data family and replaces v5.9.4. Governance, ownership, literacy and value come from Data Governance and Management; the glossary, critical data element selection and lineage come from Metadata, Catalog and Lineage; interfaces and API and event contract terms come from API and Integration Lifecycle, and the Data Contract Standard for data products and dataset feeds comes from Data Products and Data Sharing; classification, consent, residency and retention stay with their owning practices and are tested here only as hand-offs. The model is neutral to jurisdiction and vendor.

What we assess: 8 domains

Each domain is a group of related questions, scored on its own and rolled up into your overall result.

01 · 5 questions

1 Master Data Scope and Stewardship

Master data program plan, domain priorities, operating model, domain stewardship (ownership from the hub), role training

02 · 5 questions

2 Master Data Standards and Models

Attribute standards, master data model, identifiers and cross-references, hierarchies, reference data

03 · 7 questions

3 Master Data Architecture and Integration

Systems of record, hub topology, publishing, synchronization, cloud and SaaS sources, lineage hand-off, hub resilience

04 · 7 questions

4 Data Quality Measurement and Monitoring

Quality framework, rule coverage of critical data elements, profiling, threshold alerting, scorecards, quality terms in data contracts, quality for analytics and ML

05 · 9 questions

5 Data Entry, Change and Validation

Entry validation, standardization, governed high-risk changes, enrichment, bulk loads, quarantine, onboarding, migration, AI agent and automation writes

06 · 7 questions

6 Match, Merge and Golden Record

Match rules, survivorship, review queue, golden record audit, unmerge, runtime conflicts, AI-assisted matching

07 · 5 questions

7 Master Data Protection Hand-offs

Access and segregation of duties, classification tags, change audit trail, consent attributes, regulatory reporting data

08 · 8 questions

8 Issue Resolution and Improvement

Issue management, user reporting, root cause, retuning, hub adoption and benefits, AI data readiness hand-off, improvement program, data correction rules

KPIs to track

Measures that show whether the improvements hold.

KPI
Critical Elements with Live Rules - Share of critical data elements with at least one approved quality rule running in production. Target: 100%.
Consumers Fed by the Hub - Share of consuming systems that receive master data from the hub rather than point-to-point. Target: at least 90%.
Golden Records Confirmed Correct - Share of sampled golden records confirmed correct against source evidence. Target: at least 98%.
Unverified High-Risk Changes - Number of high-risk attribute changes applied without independent approval and counterparty confirmation. Target: 0.
Issues Closed on Time - Share of data quality issues closed within the target time for their severity. Target: at least 90%.

Framework alignment

Questions are written in plain language and mapped to:

DAMA-DMBOK2 Revised
COBIT 2019
ISO 8000-8:2015
ISO 8000-61:2016
ISO 8000-110:2021
ISO 8000-115:2024
ISO 8000-116:2019
ISO 8000-150:2022
ISO/IEC 25012:2008
ISO/IEC 25024:2015
ISO/IEC 5259-1:2024
ISO/IEC 5259-2:2024
ISO/IEC 5259-4:2024
Open Data Contract Standard v3.1.0
BCBS 239
ISO/IEC 27001:2022
ISO/IEC 27701:2025
ISO/IEC 29100:2024
ISO/IEC 42001:2023
NIST AI RMF 1.0
OWASP Top 10 for Agentic Applications 2026

Works best with

Practices this assessment relies on. Assessing them too gives a fuller picture.

Data Governance and Management (v1.9.6): owns the Data Management Policy, Data Governance Charter, Data Ownership Register, Data Literacy Program and Data Value Register; this model uses them as Inputs (1.1, 1.3, 1.4, 1.5, 7.1, 8.5).

Metadata, Catalog and Lineage (v1.9.6): owns the Business Glossary, including the critical data element list, and the Data Catalog lineage; this model sets quality rules for those elements and tests the lineage hand-off (2.1, 3.6, 4.1, 4.2, 7.5).

API and Integration Lifecycle (v6.9.6): owns the Integration Flow and Event Catalog and the Schema Registry and Canonical Data Model, including API and event contract terms (data products and dataset feeds use the Data Contract Standard owned by Data Products and Data Sharing v1.9.6); this model sets the master data design and quality terms (2.2, 3.2, 3.3, 3.4, 4.6, 5.7).

AI Governance (v11.9.6) and AI Use Case Lifecycle Management (v4.9.6): own the AI Agent Governance Standard and the Data Readiness Assessment (4.7, 5.9, 6.7, 8.6).

Security Operations Center Cyber and Incident Response (v8.9.6) and Business Continuity and Disaster Recovery Management (v6.9.6): own the logging standard and the IT Disaster Recovery Plan (7.3, 3.7).

Data Classification and Handling (v9.9.6) and Privacy Program Operations (v1.9.6): own classification levels and masking, and consent rules and residency; this model tests the hand-offs only (7.2, 7.4, 3.5).

Enterprise Architecture (v7.9.6): uses the System of Record Register (Source Declarations) owned here.

Recommended: Run after Data Governance and Management and Metadata, Catalog and Lineage; run before AI programs rely on master data.