Bespoke 3-Day Training Programmes

 Understand the principles of data profiling, cleansing, and governance — and why quality data is essential for reliable insights

 Objective: Equip participants with a practical understanding of data quality principles, tools, and governance practices to ensure reliable data for business decision-making.

 Learn best practices for master data, reference data, metadata, and data lifecycle management to ensure accuracy, consistency, and accessibility.

Objective: Provide participants with an understanding of data management best practices, enabling effective governance of master data, reference data, and metadata.

Explore frameworks, tools, and best practices for designing scalable, coherent data architectures aligned to business strategy.

Objective: Teach participants how to design, govern, and maintain a coherent data architecture aligned to business strategy.

• Understanding data quality: dimensions and importance
• Common challenges and risks of poor data quality
• Introduction to data profiling and data quality assessment

Objective: Participants will understand the value of data quality and how to measure it.
 

• Data cleansing techniques and automation tools 
• Data quality rules creation and management
• Hands-on: Profiling and cleansing a dataset using industry tools

Objective: Enable participants to identify and resolve common data quality issues.
 

• Data quality frameworks and governance structures
• Monitoring and reporting data quality
• Embedding data quality into business processes

Objective: Ensure participants can create a data quality strategy aligned to organisational needs.
 

• Overview of data management concepts and lifecycle 
• Importance of master data and reference data
• Data governance principles

Objective: Participants will grasp core concepts and the role of data management in organisations.
 

• Master and reference data management strategies 
• Metadata management and data cataloguing
• Hands-on: Building a simple data model for master data

Objective: Enable participants to apply practical data management techniques.
 

• Implementing data management frameworks 
• Data stewardship and governance roles
• Tools and technology considerations

Objective: Equip participants to develop a data management strategy for their organisation

• Understanding data architecture principles
• Key frameworks (e.g., TOGAF, DAMA DMBOK)
• The role of data architecture in business transformation

Objective: Participants will understand why data architecture matters and how it supports enterprise strategy.
 

• Building data models, data lakes, and warehouses 
• Integration strategies and APIs
• Hands-on: Designing a simple data architecture blueprint

Objective: Enable participants to design data architectures that are scalable and coherent.
 

• Data architecture governance models
• Architecture coherence and compliance
• Future trends and emerging technologies

Objective: Ensure participants can develop a coherent data architecture strategy and governance approach