About This Course
In today's data-driven economy, the value of any analytical insight, operational decision, or strategic plan is only as good as the data underpinning it. Poor data quality costs organisations significant resources — through rework, misinformed decisions, regulatory penalties, and eroded stakeholder trust. Yet many organisations continue to treat data quality as an afterthought rather than a strategic discipline.
The Data Quality Management for Organizations Training programme offered by TrainingComplex is designed to change that. Over five intensive days, participants develop a structured understanding of what constitutes high-quality data, how to measure it against internationally recognised dimensions, and how to design and implement governance structures that sustain data integrity over time.
The course draws on globally recognised frameworks — including DAMA-DMBOK, ISO 8000, and Six Sigma data quality principles — while remaining grounded in the practical realities faced by African and Middle Eastern organisations operating across sectors such as finance, health, government, telecommunications, and NGOs.
Participants will leave equipped to lead data quality initiatives, communicate data issues to executive stakeholders, design remediation workflows, and embed data stewardship roles within their teams. Whether you are establishing a data quality programme from scratch or seeking to mature an existing one, this course delivers the knowledge, tools, and confidence to make data a reliable organisational asset.
Who Should Attend
- Data Analysts and Business Intelligence Professionals
- Data Stewards and Data Owners
- Database Administrators and IT Managers
- Monitoring and Evaluation Officers
- Compliance and Risk Management Officers
- Business Process Improvement Managers
- Chief Data Officers and Data Governance Leads
- Finance and Reporting Professionals who rely on organisational data
- Project Managers overseeing data-intensive initiatives
- Senior Managers responsible for operational and strategic reporting
Learning Objectives
- Understand the core dimensions of data quality and explain their impact on organisational performance
- Apply internationally recognised data quality frameworks including DAMA-DMBOK and ISO 8000 to real organisational contexts
- Conduct a comprehensive data quality assessment and produce a structured findings report
- Design and implement data quality metrics, scorecards, and monitoring dashboards
- Identify root causes of data quality failures using structured diagnostic tools
- Develop and execute data cleansing, enrichment, and remediation plans
- Establish data governance structures including stewardship roles, policies, and accountability mechanisms
- Communicate data quality issues and improvement plans effectively to technical and non-technical stakeholders
- Embed data quality controls within data pipelines, ETL processes, and operational workflows
- Build a sustainable data quality culture through training, incentives, and organisational change management
Course Modules
Module 1: Foundations of Data Quality Management
- What is data quality and why it matters to organisations
- The six core dimensions of data quality: accuracy, completeness, consistency, timeliness, validity, and uniqueness
- The business cost of poor data: financial, operational, and reputational impacts
- Key concepts: data vs. information vs. insight
- Overview of leading frameworks: DAMA-DMBOK, ISO 8000, and Six Sigma for data quality
- The data quality maturity model: from ad hoc to optimised
Module 2: Data Quality Assessment and Profiling
- Planning and scoping a data quality assessment
- Data profiling techniques: column analysis, pattern analysis, and completeness checks
- Using tools and templates to document data quality findings
- Quantitative scoring of datasets against quality dimensions
- Prioritising data quality issues by business impact
- Producing a structured data quality assessment report
Module 3: Root Cause Analysis for Data Quality Issues
- Categories of data quality failure: people, process, technology, and governance
- Using fishbone (Ishikawa) diagrams to map data quality root causes
- The 5-Whys technique applied to data defects
- Linking data entry behaviour, system design, and process gaps to quality problems
- Distinguishing symptoms from underlying causes in data investigations
- Documenting root cause findings for stakeholder communication
Module 4: Data Governance Structures and Accountability
- What is data governance and how it relates to data quality
- Defining data ownership, data stewardship, and custodianship roles
- Designing a data governance organisational structure for different types of organisations
- Developing a data governance charter and terms of reference
- Establishing escalation paths and accountability mechanisms for data issues
- Aligning data governance with existing organisational structures and culture
Module 5: Designing Data Quality Metrics and Scorecards
- Identifying the right data quality metrics for different business processes
- Setting data quality thresholds, targets, and tolerance levels
- Building a data quality scorecard: dimensions, weights, and scoring logic
- Visualising data quality performance for operational and executive audiences
- Linking data quality metrics to organisational KPIs and performance frameworks
- Reviewing and updating metrics as business needs evolve
Module 6: Data Cleansing and Remediation Planning
- Principles and approaches to data cleansing: standardisation, deduplication, and enrichment
- Prioritising records and datasets for remediation based on criticality
- Designing a data remediation workflow with clear roles and approval steps
- Tools and techniques for bulk data cleansing in spreadsheets and databases
- Validating cleansed data: acceptance criteria and sign-off processes
- Preventing recurrence of cleansing issues through upstream controls
Module 7: Data Quality Controls in Processes and Systems
- Embedding data quality rules at the point of data collection and entry
- Input validation controls: field constraints, mandatory fields, and reference lookups
- Data quality checks within ETL and data integration pipelines
- System-level controls: referential integrity, business rules, and workflow approvals
- Audit trails and change logs for data quality accountability
- Reviewing and testing data quality controls before system go-live
Module 8: Master Data Management and Reference Data Quality
- What is master data and why its quality is critical to the organisation
- Common master data domains: customers, suppliers, employees, products, and locations
- Designing a master data management process with clear ownership
- Reference data management: code lists, taxonomies, and classification schemes
- Identifying and resolving duplicate master records
- Governance of master data change requests and version control
Module 9: Communicating Data Quality to Stakeholders
- Tailoring data quality communication for technical, operational, and executive audiences
- Creating executive-ready data quality dashboards and briefing notes
- Building a business case for data quality investment
- Facilitating data quality review meetings with cross-functional teams
- Managing resistance and scepticism about data quality initiatives
- Reporting progress on data quality improvement programmes
Module 10: Building a Sustainable Data Quality Culture
- Why culture is the most important factor in long-term data quality success
- Designing data quality training and awareness programmes for staff
- Incentive and recognition mechanisms that reinforce data quality behaviour
- Embedding data quality responsibilities into job descriptions and performance reviews
- Change management principles applied to data quality programmes
- Developing a personal and organisational data quality improvement action plan
Requirements & Prerequisites
Participants should have at least one year of professional experience working with organisational data in any capacity — such as data entry, reporting, analysis, or system administration. A basic familiarity with spreadsheets or database tools is helpful. No advanced technical or programming knowledge is required to attend this course.
Training Methodology
- Data quality audits using real anonymised datasets, where participants score data against quality dimensions and document findings
- Root cause analysis workshops using fishbone diagrams and the 5-Whys technique applied to common organisational data failures
- Case study reviews drawn from African and Middle Eastern organisations across health, finance, government, and NGO sectors
- Group exercises to design data governance charters, stewardship role profiles, and data quality policies
- Hands-on practice building data quality scorecards and simple monitoring dashboards using provided templates
- Scenario-based role plays simulating conversations between data stewards, IT teams, and executive stakeholders
- Remediation planning exercises where teams develop step-by-step data cleansing and enrichment strategies
- Peer critique sessions where participants review and provide structured feedback on each other's governance artefacts
- Pre-course diagnostic questionnaire to surface participants' existing data quality challenges for integration into live examples
- Personal action planning at the close of each day to connect learning directly to participants' organisational roles
Organisational Benefits
- Reduced cost of poor data through fewer errors, rework cycles, and data-related incidents
- Improved confidence in organisational reports, dashboards, and KPIs used for decision-making
- Stronger regulatory compliance posture and reduced risk of audit findings related to data integrity
- Clearer accountability for data across departments through defined stewardship roles and policies
- Faster and more reliable data integration across systems and platforms
- Enhanced credibility of M&E, finance, and operational reporting with donors, regulators, and boards
- A structured roadmap for maturing the organisation's data governance and quality capabilities
- Reduced duplication and inconsistency in master data such as client, supplier, and beneficiary records
- Improved staff competence and shared vocabulary around data quality across departments
- Greater return on investment from analytics, ERP, and digital transformation initiatives
Where Is Data Quality Management for Organizations Training Offered?
This course runs in-person across: Abuja, Nairobi, Pretoria, Addis Ababa, Nakuru, Kisumu, Arusha, Dubai, Zanzibar, Riyadh, Kigali, Cape Town, Accra, Lagos, Naivasha, Mombasa, Johannesburg, Kampala, Dar es Salaam, plus Live Virtual (online) cohorts. Browse live dates for your city below.
Upcoming Schedules
| Location | Format | Dates | Price |
|---|
| Live Virtual |
Online |
12 Oct 2026 – 16 Oct 2026 |
KES 80,000 |
| Live Virtual |
Online |
24 Oct 2026 – 15 Nov 2026 |
KES 80,000 |
| Abuja, Nigeria |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,800 |
| Nairobi, Kenya |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
KES 115,000 |
| Pretoria, South Africa |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 3,300 |
| Addis Ababa, Ethiopia |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,400 |
| Nakuru, Kenya |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
KES 120,000 |
| Kisumu, Kenya |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
KES 120,000 |
| Arusha, Tanzania |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,100 |
| Dubai, United Arab Emirates |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 4,100 |
| Zanzibar, Tanzania |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,500 |
| Abuja, Nigeria |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
USD 2,800 |
| Nairobi, Kenya |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
KES 105,000 |
| Dubai, United Arab Emirates |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
USD 4,100 |
| Zanzibar, Tanzania |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
USD 2,500 |
+227 more dates — view live schedule →
Frequently Asked Questions
Do I need a technical background to attend this course?
No advanced technical background is required. The course is designed for a mixed audience of business professionals, analysts, and IT practitioners. Participants with a basic familiarity with data or reporting tools will be well-prepared to engage with all modules.
Will I receive a certificate upon completion?
Yes. Participants who attend the full programme and complete the practical exercises will receive a TrainingComplex Certificate of Completion in Data Quality Management for Organizations. This certificate is a recognised professional development credential.
Is this course aligned with any international frameworks or standards?
Yes. The course draws on the DAMA-DMBOK (Data Management Body of Knowledge), ISO 8000 data quality standards, and Six Sigma quality principles, ensuring that content reflects globally accepted best practices in data quality management.
Can this course be delivered in-house for my organisation?
Absolutely. TrainingComplex offers customised in-house delivery of this programme, tailored to your organisation's specific data environment, systems, and strategic priorities. Contact us to discuss a bespoke training solution.
What should I bring or prepare before attending?
Participants are encouraged to bring a real data quality challenge from their own organisation to use as a reference point during exercises. A laptop is helpful but not mandatory, as printed materials and tools will be provided throughout the programme.
