Enterprise Data Management and Governance Leadership Training Course

Programme Summary

Enterprise data management and governance leadership is a five-day training course for senior leaders at strategic level, culminating in a data management roadmap for their own organisation. Many organisations cannot agree who owns their data, which version is correct or how far it can be trusted. The approach treats data as an organisational asset, linking the governance operating model, stewardship roles, quality controls, master data and metadata to a staged improvement plan. Mars Training Centre UK delivers this programme in enterprise data management and governance.

The Business Case

Unclear ownership and inconsistent definitions turn reports into reconciliation exercises and slow analytics projects. When accountability is formalised and quality is measured at source, leaders spend less time disputing figures and more time acting on them.

• Decision and Reporting Reliability: Improves as critical data elements carry agreed definitions, owners and quality thresholds.
• Operating Cost of Data: Falls as duplicate records, manual reconciliation and redundant data stores are reduced.
• Information Risk Exposure: Narrows as classification, access and retention rules are applied consistently across systems.

Principles and Evidence Base

• DAMA-DMBOK2 Knowledge Areas: Provide the reference map of data management functions used to scope the operating model in Session 1.
• Data Governance Operating Model and Stewardship Roles: Define decision rights between owners, stewards and custodians, examined in Session 2.
• Data Quality Dimensions and Root Cause Analysis: Frame how completeness, accuracy, consistency and timeliness are measured and improved in Session 3.
• Data Management Maturity Assessment: Structures the baseline and staged roadmap built in Sessions 4 and 5.

Intended Participants

• Leaders accountable for enterprise data strategy and policy across business units.
• Executives who own critical business data and approve definitions, access and quality targets.
• Senior technology leaders responsible for data platforms, architecture and integration.
• Heads of functions setting up or leading a data management office.

The programme serves government entities, banking and insurance, energy and utilities, healthcare and telecommunications.

Learning Outcomes

1. Participants will be able to evaluate current data management against the DAMA-DMBOK2 knowledge areas.
2. Participants will be able to design a data governance operating model with clear decision rights, councils and stewardship roles.
3. Participants will be able to diagnose data quality problems and prioritise remediation by business impact.
4. Participants will be able to justify an approach to master data, reference data and metadata for their organisation.
5. Participants will be able to assess data architecture, classification and lifecycle controls for gaps in information risk.
6. Participants will be able to construct a staged enterprise data management roadmap with measures of progress.

Programme Structure

Day 1: Enterprise Data Management Foundations

What does it mean to manage data as an organisational asset, and where does the organisation stand today?

• DAMA-DMBOK2 Knowledge Area Map
• Data Strategy Linked to Business Objectives
• Critical Data Element Identification
• Data Value and Cost Drivers
• Current State Data Management Assessment

Day 2: Data Governance Operating Model

Who decides what about data, and how are those decisions enforced?

• Centralised, Federated and Hybrid Governance Models
• Data Owners, Stewards and Custodians
• Data Governance Council Charter and Escalation Paths
• Policy, Standard and Procedure Hierarchy
• Decision Rights RACI Matrix

Day 3: Data Quality and Master Data

How is data quality measured, and how is one trusted version of core entities maintained?

• Data Quality Dimensions and Business Rules
• Data Profiling and Quality Scorecards
• Root Cause Analysis of Data Defects
• Master Data Management Implementation Styles
• Reference Data and Code List Control

Day 4: Metadata, Architecture and Information Risk

How do metadata, architecture and lifecycle controls keep data findable, connected and protected?

• Business Glossary and Data Catalogue
• Data Lineage for Reporting and Analytics
• Enterprise Data Architecture and Integration Patterns
• Data Classification and Access Control
• Data Retention and Lifecycle Management

Day 5: Applying the Enterprise Data Management Roadmap

How are governance, quality, master data and metadata combined into one staged plan?

• Data Management Maturity Scoring of Participant Organisations
• Prioritising Governance and Quality Initiatives
• Data Management Office Staffing and Funding Case
• Peer Challenge of Roadmap Sequencing
• Enterprise Data Management Roadmap Presentation

Methodology

The programme relies on case analysis supported by structured exercises. Participants work through governance and quality cases from public sector, financial services and energy organisations, test operating model choices against ownership disputes and score their own maturity before defending a roadmap with peers.

• Suggested activity: Drafting a decision rights matrix for a disputed customer or supplier data domain.
• Suggested activity: Profiling a sample dataset and building a quality scorecard for critical data elements.
• Suggested activity: Scoring organisational maturity and ranking improvement initiatives by value and effort.

Capstone Output

Each participant completes an enterprise data management roadmap for their organisation. It sets out the maturity baseline, target governance model, stewardship assignments, priority quality and master data initiatives and a sequenced plan with progress measures, ready for executive sponsors to approve.

Frequently Asked Questions

Is enterprise data management and governance leadership suitable for business leaders without a technical background?

Yes. It suits strategic leaders from business and technology functions and assumes experience of using data in decisions, but no database or programming knowledge.

Which framework underpins the enterprise data management and governance programme?

It rests on the DAMA-DMBOK2 knowledge areas, supported by data governance operating model design, data quality dimensions with root cause analysis, and data management maturity assessment.

How is the enterprise data management roadmap used at work?

You return with a staged plan sponsors can approve, setting governance roles, quality and master data priorities and progress measures for your data programme.

How does enterprise data management and governance differ from a data analytics programme?

It focuses on ownership, quality and control of data across the organisation, rather than on statistical analysis, dashboards or analytical models.