Training How To Implement Practical Data Quality Management Graphic

How to Implement Practical Data Quality Management

Data is at the heart of every organisation. However, if not managed properly, this can be detrimental to a business.

All organisations rely on the information created from Data stored in computer systems, to run operations and to make decisions. If this Data is incomplete, invalid, inconsistent, non-standard, misinterpreted and/or not compliant with business rules – you have a Data Quality problem. When your business applications use unreliable Data, you have an information quality problem.

Unless properly addressed, Data and Information Quality issues will lead to untrustworthy information, contributing to:

  • Inefficient delivery
  • Unnecessary risks
  • Non-productive costs.

Employees waste a lot of time when they have to do work over again due to frequently having to search for the right information. Studies have shown that this costs our economy hundreds of millions of Rands every year.

Course Overview

This course will provide the participants with the knowledge they need to implement Data Quality Management. The Practitioners Course is designed to help those who are (or will be) responsible for managing Data Quality.

The Course covers the following aspects of Data Management:

  • Data Quality Awareness – What your team must understand about the importance of Data Quality (DQ)
  • Data Quality in Context – The role of Frameworks, DQ Dimensions, Data Models and Metadata in the context of DQ Management
  • Data Quality Management – The theory of the Data Quality practice
  • Assessment – How to approach conducting a Health Check on Data, how to profile Data, and what the profiling statistics tell us
  • Requirements – The requirements of business Data Producers and Consumers, rules to which Data must conform and DQ Targets and Thresholds
  • Measurement – How to measure the Data based on the Business Requirements
  • Correction – How to “fix” incorrect Data in terms of missing, incorrect and duplicate Data
  • Prevention – How to identify the Root Causes of Data defects and stop the issues from re-occurring
  • Tools – An overview of the technology to assist you, what the tools can do and some of the available products
  • Conclusion – We put it all together within the context of Data Management.
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DAMA

This course is
endorsed by DAMA
South Africa and is helpful
preparation for the DAMA CDMP Data
Quality Specialist exam.

Course type

Classroom & Virtual

Duration

4 days

Course dates

26 - 29 May

What delegates have to say

“The course exceeded my expectations. The course was carried out exceptionally well. I did not expect to enjoy it, but the concepts were very relevant to our everyday work. I was also able to follow the pace. The course was run well by the instructor. The breakaway exercises also helped me to understand the logic behind the theory better”   Vendaanta Devar (Intern); Altron People Solutions

"A short, punchy course to equip participants in their understanding of how to identify and quantify the underlying symptoms that highlight poor data quality, in order to develop a practical action plan to address those issues in a controlled and repeatable way in their respective organisations."
Dries Kotze, Data Quality & Process Team Lead, Shoprite

Learning outcomes

We designed this course to help those who are (or will be) responsible for managing Data quality to:

  • Understand the issues affecting Data Quality and recognise and quantify the symptoms
  • Develop an action plan to address those issues in a controlled and repeatable process
  • Demonstrate how Data Quality Management improves the business and operational ‘bottom-line’
  • Prepare you to obtain the DAMA CDMP Data Quality specialist certification.

* A certificate of attendance will be provided on completion of the course

Intended participants

This course will be of interest to Data Management Practitioners, and anyone involved in Data or Information Quality Management, such as people from areas dealing with:

  • Data Governance
  • Master Data Management
  • Business and Systems Analysis
  • Data Stewardship
  • Data Analysis and Cleansing.

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