Introduction

In the pursuit of reliable and actionable business intelligence, the integrity of underlying data is paramount, making robust Data Quality and Master Data Management (MDM) indispensable pillars for building trustworthy analytical foundations and fostering confident, data-driven decision-making. Poor data quality leads to flawed insights, misguided strategies, and significant operational inefficiencies, eroding trust in BI initiatives and hindering competitive advantage. This training course is meticulously designed to equip data governance professionals, data stewards, BI architects, data engineers, database administrators, and IT managers with cutting-edge knowledge and practical skills in understanding the critical importance of data quality, mastering methodologies for data profiling and cleansing, establishing comprehensive data quality rules, implementing effective Master Data Management strategies, understanding metadata management, and building robust governance frameworks to ensure the accuracy, consistency, and reliability of an organization's most critical data assets. Participants will gain a comprehensive understanding of how to proactively manage data quality and master data, transforming raw information into a dependable strategic asset that fuels accurate BI and operational excellence.

Duration

10 days

Target Audience

• Data Governance Professionals
• Data Stewards & Custodians
• Business Intelligence (BI) Architects
• Data Engineers
• Database Administrators (DBAs)
• IT Managers & Directors
• Data Quality Analysts
• Solution Architects
• Enterprise Architects
• Project Managers (Data-focused)
• Anyone responsible for data integrity and trustworthiness

Objectives

• Understand the critical importance of data quality and its impact on business intelligence.
• Master methodologies for data profiling, assessment, and measurement.
• Learn to identify, classify, and resolve various data quality issues.
• Develop proficiency in designing and implementing data quality rules and processes.
• Understand the core concepts and benefits of Master Data Management (MDM).
• Explore various MDM architectural styles and implementation approaches.
• Learn about metadata management and its role in data governance.
• Develop skills in designing and managing data governance frameworks.
• Understand the technologies and tools used for data quality and MDM.
• Formulate strategies for continuous data quality improvement and MDM adoption.
• Apply data quality and MDM principles to ensure trustworthy data for BI initiatives.

Course Content

Module 1. Introduction to Data Quality and its Impact on BI

• Defining Data Quality: Characteristics, dimensions (accuracy, completeness, consistency, timeliness, validity, uniqueness)
• The cost of poor data quality: Business impacts and risks
• The value of high-quality data for Business Intelligence and decision-making
• Data as an asset: Principles of data stewardship
• Real-world examples of data quality issues and their consequences

Module 2. Data Profiling and Assessment

• What is Data Profiling?: Discovering data characteristics and quality issues
• Techniques for Data Profiling: Statistical analysis, pattern matching, value distributions
• Identifying data quality dimensions at scale
• Using tools for automated data profiling (conceptual overview)
• Presenting data quality assessment findings

Module 3. Data Cleansing and Remediation Techniques

• Standardization: Formatting, unit conversion, domain alignment
• Parsing: Deconstructing complex data fields (e.g., addresses, names)
• Matching and Merging: Identifying and consolidating duplicate records
• Error Correction: Handling invalid values, correcting misspellings
• Data enrichment: Adding external data for completeness

Module 4. Establishing Data Quality Rules and Processes

• Defining Data Quality Rules: Business rules, technical rules
• Implementing data quality checks in ETL pipelines
• Automated vs. Manual Data Quality Processes
• Data Quality Scorecards and Dashboards for monitoring
• Continuous data quality improvement cycles

Module 5. Introduction to Master Data Management (MDM)

• Defining Master Data: Key business entities (Customer, Product, Employee, Location, etc.)
• What is MDM?: Principles, purpose, and benefits
• Why MDM is crucial for BI and enterprise-wide consistency
• Challenges of managing master data without MDM
• Case studies of MDM implementation value

Module 6. MDM Architectural Styles and Implementations

• Centralized MDM: Hub and Spoke, Registry, Coexistence
• Consolidation Style: Golden record creation
• Coexistence Style: Distributed master data management
• Implementation considerations: Build vs. Buy, phased approach
• Governance models for MDM implementations

Module 7. Data Governance and its Relationship to Data Quality & MDM

• Defining Data Governance: Policies, processes, roles, responsibilities
• Data Governance Frameworks: DAMA-DMBOK, CDMC
• The role of Data Stewards and Data Owners in DQ and MDM
• Establishing a Data Governance Council
• Data policies for quality, security, and privacy

Module 8. Metadata Management

• What is Metadata?: Technical, Business, Operational Metadata
• Metadata Repositories and Data Catalogs: Tools and functions
• Importance of metadata for data discovery, understanding, and lineage
• Automating metadata capture and synchronization
• How metadata supports DQ and MDM initiatives

Module 9. Data Lineage and Traceability

• Understanding Data Lineage: Tracking data flow from source to consumption
• Benefits of data lineage for auditing, compliance, and impact analysis
• Tools for visualizing and managing data lineage
• Establishing data traceability for BI reports
• Impact of lineage on data trust and regulatory compliance

Module 10. Data Security and Privacy in the Context of DQ/MDM

• Securing Master and High-Quality Data: Access control, encryption
• Data Privacy Regulations: GDPR, CCPA, HIPAA and their impact on DQ/MDM
• Data Masking and Anonymization techniques
• Role-based access control for sensitive data
• Ensuring ethical data use and compliance

Module 11. Data Quality and MDM Tools & Technologies

• Overview of Data Quality Tools: Data profiling, cleansing, matching capabilities
• Overview of MDM Solutions: Commercial vendors and open-source options
• Data Governance Platforms: Integrating DQ, MDM, and metadata
• Cloud-based DQ and MDM services
• Evaluation criteria for selecting appropriate tools

Module 12. Implementing Data Quality Initiatives

• Phased Approach to DQ Projects: Pilot, rollout, continuous improvement
• Setting Measurable DQ Goals: Baselines and targets
• User Engagement and Change Management for DQ adoption
• Integrating DQ into data pipelines (ETL/ELT)
• Monitoring and reporting on data quality metrics

Module 13. Implementing MDM Initiatives

• MDM Project Planning: Scoping, requirements gathering, stakeholder alignment
• Data Model Design for Master Data: Canonical data models
• Data Harmonization and Consolidation: Creating a "golden record"
• Integration with source and consuming systems
• Post-implementation support and maintenance

Module 14. Measuring ROI and Value of DQ & MDM

• Quantifying the Benefits: Cost savings, revenue growth, risk reduction
• Developing a business case for DQ and MDM investments
• Key performance indicators (KPIs) for DQ and MDM programs
• Communicating value to executive stakeholders
• Continuous justification and refinement of DQ/MDM programs

Module 15. Future Trends in Data Quality and Master Data Management

• AI and Machine Learning in DQ/MDM: Automated data cleansing, matching, entity resolution
• Graph Databases for MDM: Representing complex relationships
• Real-time Data Quality and MDM
• Data Fabric and Data Mesh architectures and their impact on MDM
• The evolving role of the Chief Data Officer (CDO) and integrated data management.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.