Day 1: Data Management Overview
Data management is an emerging discipline, without a mature definition, scope and purpose. The Data Management Association (DAMA) has put together a complete Data Management Body of Knowledge to define this discipline. This course introduces each function using immediately usable insights and exercises. The course prepares attendees for Certified Data Management Professional (CDMP) Associate certification.
Day 2: Practical Data Governance
Data Governance is the exercise of authority and control over data asset management. How do you do this in the context of your organisation’s existing data management practices and culture?
The second day provides a foundation in Data Governance and builds your understanding of this complex area using practical examples. The course goes into the detail on the core Data Governance functions defined by the leading industry best-practice standard, the DAMA Data Management Body of Knowledge® (DAMA DMBOK®).
The course is rich in exercises to reinforce the study material presented in a take-home workbook. The course incorporates time to review your particular organisation and experiences in Data Governance. The aim is to equip participants to implement or improve Data Governance in their organisations.
Day 3: Practical Data Quality
Data Quality is often overlooked by organisations as it’s tomorrow’s problem. This course is illustrated with real case studies, covers:
- What is Data Quality vs Data Quality Management and why does it matter?
- The Data Quality Reference Model, including architecture and processes
- Diagnosing Quality problems
- Writing business case studies and exercises
- Getting started and sustaining Data Quality Initiative
- The DAMA dimensions of Data Quality, plus alternative views on Data Quality dimensions
- The relationship between Data Quality, Data Governance and the other Information Disciples
- Starting and sustaining a Data Quality initiative: steps for achieving Data Quality
- The roles, responsibilities and activities involved in establishing successful Data Quality.