About the Course
Organizations do not buy data governance because they want more documentation; they invest in it because they need results they can prove in metadata management, data quality management, ownership assignment, issue resolution, and accountability reporting. In practice, that means you must show how your governance approach connects to data definitions, stewardship responsibilities, control evidence, and business-facing reporting, often informed by frameworks such as DAMA-DMBOK, COBIT, and ISO/IEC 38500. This data governance and data stewardship course focuses on the operating disciplines that make governance real: defining data domains, assigning stewards, tracking quality defects, and escalating issues through a clear decision path.
This course turns scattered governance tasks into a structured system you can use immediately. You will practice building a data ownership model, mapping stewardship responsibilities, designing a data quality scorecard, creating a policy-to-control matrix, and drafting a governance escalation workflow. You will also be introduced to AI-assisted data classification, automated data quality monitoring, and metadata catalog workflows so you can understand how modern data environments change stewardship work. What you will learn is straightforward: how to assess current-state governance maturity, how to design practical stewardship routines, and how to produce governance artefacts that decision-makers can act on. You will practice the core tools hands-on, while broader governance operating-model design and technology adoption will be covered at an applied overview level.
The reality for most teams is constraint, not ideal conditions. Data governance and data stewardship must work across legacy systems, fragmented business ownership, competing reporting priorities, and limited time for central teams to chase every issue. This course is built for professionals who need to deliver under those conditions, with realistic methods for balancing policy, accountability, and execution without assuming perfect data platforms or unlimited governance capacity.
Target Audience
This course is designed for professionals who own, steward, manage, or rely on business data and need a practical approach to governance, quality, and accountability.
- Data Governance Managers who run operating models and decision forums
- Data Stewards who maintain definitions, ownership, and issue tracking
- Data Quality Analysts who monitor defects, rules, and exception trends
- Master Data Specialists who govern reference data and golden records
- Information Governance Leads who coordinate policy, retention, and control alignment
- Business Data Owners who approve definitions and escalation paths
- Data Architects who embed governance into metadata and data models
- BI and Reporting Managers who need trusted metrics and definitions
- Compliance Analysts who map governance controls to evidence requirements
- Risk and Control Specialists who oversee data-related control activities
Course Objectives
This course equips you to plan, execute, and measure data governance and data stewardship initiatives that improve data quality, strengthen accountability, and support compliant reporting.
- Assess current-state data governance maturity using DAMA-DMBOK and a governance maturity checklist.
- Apply data stewardship methods to assign ownership, define escalation paths, and resolve data issues.
- Build a stewardship RACI, data ownership matrix, and policy-to-control mapping for priority data domains.
- Create a data quality scorecard using defect rules, exception logs, and KPI thresholds.
- Evaluate governance controls against COBIT principles and ISO/IEC 38500 guidance.
- Navigate stewardship and compliance requirements across business owners, IT teams, and control functions.
- Implement measurable governance targets using metadata catalog workflows and automated data quality monitoring.
- Synthesize governance findings into a board-ready dashboard, issue register, and action plan.
Requirements & Prerequisites
Prerequisites required: working knowledge of business data processes, reporting workflows, and basic data quality concepts. You do not need programming experience, but you should be comfortable reviewing data dictionaries, issue logs, and governance templates. A laptop is recommended for hands-on exercises using spreadsheets, sample stewardship registers, and governance dashboards. The course is taught at an intermediate level, with operational application of governance methods and conceptual awareness of advanced AI-assisted classification workflows.
Professional and Organizational Impact
When you lead data governance and data stewardship with credible data and practical structures, you become a trusted driver of data integrity and accountability.
- Build sharper expertise in metadata, ownership, and control design.
- Gain confidence in resolving data issues through formal stewardship workflows.
- Strengthen your ability to balance business speed with governance discipline.
- Enhance your credibility when presenting data quality evidence to leadership.
- Develop practical skill in stewardship charters, RACI models, and scorecards.
- Position yourself as a reliable partner across business, IT, and compliance teams.
- Expand your career options in governance, data quality, and information management roles.
Organizations that embed data governance and data stewardship into reporting, operations, and control environments reduce costs, mitigate risks, and build lasting competitive advantage.
- Reduce reporting errors caused by inconsistent definitions and duplicate records.
- Lower rework costs by fixing data quality issues at source.
- Improve audit readiness through documented controls and stewardship evidence.
- Strengthen decision reliability with trusted definitions and approved ownership.
- Reduce compliance exposure from weak access, retention, and accountability practices.
- Improve operational efficiency by shortening issue resolution cycles.
- Support enterprise data strategy with reusable governance standards and scorecards.
Training Methodology
This is a practical, outcome-driven course designed to turn data governance and data stewardship aspiration into measurable action and credible reporting.
Methodology includes:
- Hands-on calculation using data quality defect rates and a sample scorecard dataset.
- Scenario simulation involving a critical master data dispute and escalation decision.
- Governance diagnostic using a maturity checklist aligned to DAMA-DMBOK.
- Stakeholder mapping exercise for business owners, stewards, IT, and compliance reviewers.
- Case study analysis from banking, healthcare, manufacturing, and public sector data environments.
- Group workshop to produce a stewardship RACI and policy-to-control matrix.
- Reflection exercise using benchmarked governance gaps and AI-assisted metadata classification trends.
Upcoming Sessions
Next available dates worldwide
No international sessions scheduled
Certification
Recognized credentials that advance your career
Participants who complete the Data Governance and Data Stewardship Training Program earn a Trainingcred Certificate of Achievement, demonstrating professional competence and alignment with global standards in learning and development.
NITA Accredited
Accredited by the National Industrial Training Authority, ensuring programs meet nationally recognized standards of quality and relevance.
CPD Certified
Recognized by the CPD Certification Service, ensuring every program meets internationally benchmarked standards of professional excellence.
Why this course earns its place on your CV
Accredited training, practitioner trainers, and peers on the same career track — the three things real expertise is built on.
Effective Learning & Skill Development
- Build expertise with structured, outcome-driven learning.
- Equip individuals and teams with skills that grow with industry needs.
- Reinforce learning through real-world scenarios, case studies and practical exercises.
Career Growth & Professional Advancement
- Apply what you learn with a proven methodology that ensures lasting impact.
- Develop immediately usable skills that translate directly into workplace success.
- Gain the expertise needed for career advancement and leadership roles.
Training Optimization & Learning Excellence
- Tailor training to industry-specific challenges and organizational goals.
- Use data-driven insights and automation to enhance training effectiveness.
- Evaluate progress and ensure long-term learning success.
