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Training: Introduction to Data Management

Introduction To Data Management

Course dates

19 April, 03 May, 07 June, 16 August, 18 October

Course type



Half day

No. of participants

No limit



Workshop overview

This workshop will provide the participants with an understanding of the basics of Data Management, and why it is important to achieving the organisation’s business goals.

Throughout the Data Management Introduction Workshop we use analogies and anecdotes that are simple to understand and relate Data and Data Management to real life experience. This makes it easier for delegates to understand the Data Management Concepts.

The workshop covers the following aspects of Data Management

  • The costs and risks of not managing Data
  • How to drive Data Management Improvement
  • The need for corrective improvement
  • The need for preventative improvement Understand Data Management terminology such as Data Quality Dimensions, (completeness, currency
    accuracy etc.), metrics and thresholds and why they are important
  • Understand the different types of Data (e.g. Master Data, Transactional Data)
  • Understand the Data Life Cycle
  • Understand why some Data is more important than other Data – Critical Data
  • Understand the basics of Root Cause Analysis, how it helps us and why it is important
  • What Data Quality is, and how it can be measured
  • How Data Quality impacts business, applications, and reporting systems
  • Understand the concept of ‘Fit for Purpose’ using analogies that the delegates can relate to
  • Identify the upstream and downstream impacts that poor Data Quality has on an organization
  • Understand the importance of Continuous Improvement

Learning outcomes

Delegates will:

  • acquire an understanding of Data Management principles, practices and technologies
  • learn how to handle data to prevent data quality issues and create value

Intended participants

This course will be of interest to:

  • Business Managers seeking assurance that the Information they use is based on reliable Data
  • IT staff and Analysts involved in developing new processes and systems that manipulate Data
  • Data Owners
  • System Analysts
  • Data Warehouse Analysts
  • Business Intelligence Analysts

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