About the Course
Organizations do not buy data governance and data quality management because they want documentation; they invest because they need data they can defend in audits, board reporting, operational planning, and customer-facing decisions. To do that credibly, you need to show capability in data ownership, data stewardship, data profiling, data quality measurement, and policy enforcement, all within a structured governance model informed by DAMA-DMBOK and ISO/IEC 38500 thinking.
This course turns scattered knowledge into a working system. You will practice building a governance charter, mapping data owners and stewards, designing data quality rules, creating a quality scorecard, and drafting an issue management workflow. You will also be introduced to data catalog concepts, metadata management, master data management practices, and privacy-by-design controls so you can connect governance, quality, and compliance in one operational view. In practical terms, you will learn how to assess data quality dimensions, establish controls for critical data elements, build a governance operating model, and report issues in a format decision-makers can act on.
The course is designed for professionals who must deliver under real constraints such as fragmented systems, limited tooling, competing priorities, and pressure to support analytics and AI use cases without creating new data risk. This course teaches data governance and data quality management through structured workshops and applied exercises so you can move from informal data handling to repeatable control, measurable improvement, and stronger reporting confidence.
Target Audience
This course is designed for professionals who need to govern data, improve data quality, and support reliable reporting across business functions.
- Data Governance Manager responsible for policy, ownership, and stewardship operating models
- Data Steward managing definitions, issue resolution, and control enforcement
- Data Quality Analyst measuring accuracy, completeness, consistency, and timeliness
- Master Data Specialist maintaining reference data and critical business records
- Data Governance Analyst tracking governance KPIs, controls, and remediation actions
- Business Intelligence Analyst depending on trusted data for dashboards and reporting
- Information Governance Lead aligning controls across data, records, and metadata
- Compliance Manager reviewing data handling practices, retention, and evidence trails
- Enterprise Architect connecting governance rules to data platforms and workflows
- Operations Manager overseeing data-dependent processes and escalation paths
Course Objectives
This course equips you to design, execute, and measure data governance and data quality management initiatives that improve data trust, strengthen control, and support strategic reporting.
- Assess current-state governance using DAMA-DMBOK concepts and a data ownership map.
- Apply data profiling and validation techniques to identify critical data quality defects.
- Design a data governance charter with roles, decision rights, and stewardship accountabilities.
- Build a data quality scorecard using dimensions such as accuracy and completeness.
- Calculate baseline quality metrics from sample datasets and issue logs.
- Evaluate governance controls against ISO/IEC 38500 principles and internal policy requirements.
- Implement stakeholder escalation paths and remediation workflows for high-risk data issues.
- Synthesize findings into a data quality improvement report and executive briefing deck.
Requirements & Prerequisites
Participants should have a working understanding of organizational data, reporting processes, or information systems. No coding is required, but familiarity with spreadsheets, basic data definitions, and common reporting workflows will help you apply the exercises more effectively. Experience in data management, compliance, analytics, operations, or business systems is useful, especially for the hands-on governance and quality artefacts developed during the course.
Local Application and Business Return in your market
How participants can apply the training in local operating conditions, and the return their organisation can plan for.
How participants apply this
Expected ROI
Training Methodology
This is a practical, outcome-driven course designed to turn data governance and data quality management aspiration into measurable action and credible reporting.
Methodology includes:
- Calculate data quality metrics from a sample dataset and build a scorecard.
- Run a scenario simulation for a critical customer record correction delay.
- Use a governance assessment checklist based on DAMA-DMBOK and ISO/IEC 38500.
- Map stakeholders, owners, and approvers across a data issue escalation chain.
- Review case studies from banking, healthcare, manufacturing, and retail data environments.
- Develop a stewardship workflow and remediation tracker in a group workshop.
- Challenge current practices using benchmarked data quality dimensions and control evidence.
Upcoming Sessions
Next available dates worldwide
Certification
Recognized credentials that advance your career
Participants who complete the Data Governance and Data Quality Management 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.
Tools and platforms relevant to this field
Examples local teams may encounter, and that may be featured in training where they support the confirmed course scope.
These are field-relevant examples, not a promise that every tool will be covered. Exact coverage depends on the confirmed course scope, participant needs, and delivery format.
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Collibra Data Intelligence Cloud CollibraUsed to manage data cataloging, stewardship workflows, policy alignment, and data issue tracking.
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Informatica Intelligent Data Management Cloud InformaticaUsed for data quality rules, master data management, reference data controls, and remediation workflows.
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Microsoft Purview MicrosoftUsed to classify data, support governance policies, and improve visibility across enterprise data assets.
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Tableau SalesforceUsed to expose quality scorecards and stewardship KPIs to business and executive users.
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Power BI MicrosoftUsed to build governance dashboards, monitor data quality trends, and report exceptions to stakeholders.
Real-World Case Studies from your market
Real organisations putting these methods into practice — what they did, what changed, and the measurable outcome. No hypothetical scenarios.
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Data governance and quality as prerequisites for AI and analytics 2025KPMG International
KPMG argues that organizations need unified data and AI governance, with data quality and risk management built into the operating model rather than treated as after-the-fact checks.
The report links stronger governance and better data quality to faster innovation, lower risk, and more consistent oversight of information used in AI and analytics.
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Data strategy and quality in financial-services AI 2024Office of the Superintendent of Financial Institutions Canada
OSFI states that AI performance depends on an effective data strategy that prioritizes data governance and quality, and that controls should ensure data is accurate, accessible, and fit for purpose.
The guidance reinforces the business case for documented controls, testing, and auditing so data issues do not propagate into high-stakes financial decisions.
View source
