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.
Professional and Organizational Impact
When you lead data governance and data quality management with credible evidence and practical controls, you become a trusted driver of data reliability and reporting confidence.
- Build stronger capability in data profiling, validation, and issue triage.
- Gain confidence using governance charters, stewardship logs, and quality scorecards.
- Strengthen your ability to balance business speed with data control.
- Enhance your credibility when discussing data defects with technical teams.
- Develop practical fluency in metadata, master data, and policy enforcement.
- Position yourself to support audits, remediation plans, and governance reporting.
- Expand your value across analytics, operations, compliance, and transformation work.
Organizations that embed data governance and data quality management into reporting, analytics, and operational workflows reduce risk, improve decision quality, and strengthen trust in enterprise data.
- Reduce rework caused by inaccurate, duplicated, or incomplete data.
- Improve the reliability of dashboards, KPIs, and management reports.
- Lower compliance exposure through clearer ownership and control evidence.
- Accelerate issue resolution with defined stewardship and escalation paths.
- Improve master data consistency across systems and business units.
- Strengthen executive confidence in performance reviews and planning data.
- Support AI and automation initiatives with better governed source data.
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.
Industry Tools and Platforms Featured in this Training
The platforms and vendors Türkiye teams are running today — taught against real configurations, not generic vendor demos.
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Microsoft Power BI MicrosoftTeams use it to build dashboards and track data quality metrics, issue trends, and stewardship KPIs across business units.
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Tableau SalesforceIt is used for self-service analytics, making it important to govern definitions, lineage, and trusted data sources before dashboards are published.
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SAP Master Data Governance SAPIt supports master data standardization, approval workflows, and controlled updates for customer, supplier, and product records.
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Informatica Data Quality InformaticaIt is used to profile data, validate rules, standardize fields, and monitor recurring quality defects.
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Collibra Data Intelligence Platform CollibraIt helps document ownership, policies, glossary terms, and stewardship workflows for governance operating models.
