Data Management Fundamentals

Data Management Fundamentals

Master Data Lifecycle, Governance, Quality, Modelling, Integration, and Compliance

|
Platform:
Online
In-class
Revised and Updated: 28 September 2026
Date Venue Duration
28 - 30 October 2026 Sandton, Gauteng 3 Days

Course Introduction

Effective data management is crucial for organisations to ensure data accuracy, consistency, and accessibility, as well as to comply with legal and regulatory requirements. It helps organisations make informed decisions, improves operational efficiency, and enhances customer satisfaction.

 

Prospen Africa’s 5-Day Data Management Fundamentals course covers all Information Management disciplines defined in the DAMA Body of Knowledge (DMBoK). This course provides a solid foundation across the entire Information Management spectrum. Attendees will gain a firm grounding in core Information Management concepts and learn their practical application through real examples of Information Architecture.

Learning Outcomes

By the end of this course, attendees will be able to:

  • Understand and apply Information Management disciplines to various organisational challenges
  • Explore an Information Management framework and its alignment with other architecture frameworks
  • Grasp key concepts such as lifecycle management, normalisation, dimensional modelling, and data virtualisation
  • Understand the importance of Master Data Management and Data Governance, and how to apply them effectively
  • Compare different MDM architectures and assess their suitability for various needs
  • Develop practical techniques for common information management challenges
  • Apply best practices for managing enterprise information needs
  • Apply information architecture planning techniques through practical examples

Course Objectives

This Data Management Fundamentals course equips you with the knowledge, methods, and techniques needed to analyse, mature, and implement information management solutions within your organisation. Topics covered include:

  • Data Lifecycle Management
  • Data Integration
  • Data Governance
  • Risk, Security, and Regulatory Compliance
  • Data Quality Management
  • Business Intelligence and Data Warehousing
  • The Essential Role of Data Modelling
  • Master and Reference Data Management
  • Metadata Management

Who should attend?

  • Information Managers
  • Information Architects
  • Data Architects
  • Enterprise Architects
  • MDM Managers
  • Data Governance Managers
  • Data Quality Managers
  • Information Quality Practitioners
  • Business Analysts
  • Executives and Technology Leaders
Microsoft (365) Office Courses

Training Methodology

Our diverse instructional approaches ensure effective learning:

– Lectures & Presentations: Engage with expert-driven, stimulating content.
– Course Material: Access well-crafted supporting resources.
– Group Work: Collaborate on discussions and case studies for practical insights.
– Workshops & Role-Play: Participate in immersive, scenario-based activities.
– Practical Application: Focus on applying theoretical knowledge in real situations.
– Post-Training Support: Receive extensive support after training for skill implementation.

Training Outline

Day 1 — Introduction to the DMBoK and Data Governance
Introduction to the DMBoK
  • Purpose and audience of the DMBoK
  • Changes in DMBoK 2.0 and its relationship with other frameworks (TOGAF / COBIT)
  • Overview of the DAMA Certified Data Management Professional (CDMP) designation
Data Governance
  • The importance of Data Governance
  • A typical Data Governance (DG) reference model
  • DG roles and responsibilities, the role of the Data Governance Office (DGO), and its relationship with the PMO
  • Getting started with Data Governance

Day 2 — Data Quality and Master & Reference Data Management
Data Quality Management
  • Dimensions of Data Quality
  • Policies, procedures, metrics, technology, and resources for ensuring Data Quality
  • The Data Quality reference model
  • Tools to support Data Quality management
Master & Reference Data Management
  • Differences between Reference Data and Master Data
  • Managing Master Data across the enterprise
  • MDM architectures and their suitability
  • MDM maturity assessment and business procedures
  • Incremental implementation of MDM

Day 3 — Data Warehousing, BI, Modelling, and Metadata Management
Data Warehousing & BI Management
  • Provisioning Business Intelligence (BI) and managing data for BI solutions and data warehouses
  • Types of BI, data warehousing, and analytics
Data Modelling & Metadata Management
  • Metadata repositories and business user access
  • Development and use of data models
  • Maturity assessment and integration within the System Development Life Cycle (SDLC)
  • Current Issue Discussion: How AI-assisted metadata cataloguing and automated data lineage tools are accelerating maturity in modern data warehousing environments

Day 4 — Data Integration, Architecture, and Lifecycle Management
Data Integration & Architecture Management
  • Business and technology issues addressed by Data Integration
  • Styles of Data Integration and their applicability
  • Approaches and guidelines for Data Integration
  • Consideration of P2P, ETL, CDC, Hub & Spoke, SOA, and Data Virtualisation
Data Lifecycle Management
  • Planning for data management across its lifecycle
  • Maturity assessment of Data Lifecycle Management

Day 5 — Risk, Compliance, and Data Management Tools
Data Risk Management, Security & Privacy
  • Identifying threats and defences for data protection
  • Exploration of threat categories, defence mechanisms, and the implications of security breaches
  • Risk identification and mitigation
Regulatory Compliance
  • Policies and assurance processes for compliance
  • Adapting to changing legal and regulatory requirements (including POPIA and the UAE's data-protection framework)
  • Approaches to regulatory compliance and understanding sanctions
Data Management Tools & Repository
  • Categories of tools supporting Information Management disciplines
  • Selecting appropriate toolsets
  • An example policy for technology use to ensure consistency and interoperability

Course Categories

Get a Quote Banner Outline

Request a Call Back

Your submission has been successful

Please check your email for confirmation

Success Stories

Discover how our courses enhance professionals’ effectiveness in their workplaces.

Tharisa Minerals

SQL Server Training

The course was very informative and interesting

Revenue Appeals Tribunal Eswatini

Basic Registry, Records and Archives

The facilitator is the best in the field and i personally learnt a lot on the subject matter.

Magalies Water

Document Control and Document Management Systems

The facilitator was knowledgeable, engaging, and presented the material clearly. The institution provided a well-organized learning environment with adequate resources and support throughout the training.

Central Bank of Lesotho

Data & Information Governance

The vast knowledge of Mr Selelepoo is really unmatched. I am totally happy and transformed from this training.

FAQs – Data Management Fundamentals

Master data management fundamentals, learning data governance frameworks, quality control, data architecture, security compliance, lifecycle management, and enterprise data integration strategies.

What is covered in the Data Management Fundamentals Training?
The course covers essential core concepts of data governance, data architecture, data quality management, relational database basics, metadata management, data security controls, and regulatory compliance frameworks.
Who should attend the Data Management Fundamentals course?
This course is designed for IT administrators, data officers, business analysts, records managers, compliance personnel, and office leaders responsible for maintaining accurate, secure, and well-organized organizational data.
How does this course address data governance and compliance standard?
Participants learn how to establish organizational data policies, enforce stewardship, maintain data lineage, and align internal information workflows with regulatory compliance frameworks like POPIA and GDPR.
Will delegates learn techniques for ensuring data cleansing and quality?
Yes. The training focuses on identifying data anomalies, establishing validation rules, cleaning duplicate or corrupt records, and implementing continuous data quality monitoring across enterprise systems.
Does the program include practical, hands-on data modeling workshops?
Yes. Attendees work through practical exercises designing data structures, organizing entity-relationship models, establishing master data management protocols, and setting up metadata catalogs.
Why is mastering data management fundamentals vital for business strategy?
Robust data management ensures information integrity across systems, protects sensitive enterprise assets from security breaches, eliminates duplicate effort, and establishes a solid foundation for trustworthy business analytics.

Related Courses