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Data is fundamental to effective, evidence-based decision-making. It underpins everything from major policy decisions to routine operational process.

Learn practical tools and techniques to assess, communicate and improve data quality at our Data Quality: From Collection to Disposal course.

Learn the importance of good data quality across the data lifecycle from data creation and collection through to disposal.  By using The Government Data Quality Framework, you can demonstrate that your data is fit for purpose throughout the entire data lifecycle.

Leave the day with the full confidence in your data and ensure your organisation reaps the many benefits of higher data quality.

Unlocking the Power of Virtual

Our virtual courses have been designed with you in mind. From group exercises in breakout rooms to live chat, whiteboards, and interactive polls, we use a range of tools and techniques to ensure that you can connect with your trainer; network and share best practice with your peers and leave the day with the skills you need.

Our courses provide you with an interactive and engaging learning environment that can be accessed from any location, helping you to continue to connect, learn and grow. Click here to discover more!

Please note we will use Zoom to virtually deliver this course.

trainer photo
Nigel Turner
Information Management Consultant - Global Data Strategy Ltd

Nigel has worked in data management for over 25 years.  This experience has embraced Data Quality, Data Governance, Information Strategy, Master Data Management, & Business Intelligence.  He is currently Principal Information Management Consultant, EMEA for Global Data Strategy Ltd, an international data consultancy where he delivers consulting and training to variety of organisations in government, retail, manufacturing, education, insurance, non-profit and others.

Nigel spent much of his early career in British Telecommunications Group (BT) where he led a series of enterprise wide data quality & data governance initiatives which brought large benefits to BT. He also created and ...

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Learning Outcomes

  • Understand what ‘fit for purpose’ data is and is not
  • Identify the main causes of poor data quality and its impact on individuals and organisations
  • Be able to apply a simple five stage approach to assure and improve data quality throughout the data lifecycle
  • Describe the main dimensions of data quality and how they apply across the data lifecycle
  • Be aware of tools and techniques that can help ensure fit for purpose data
All the Understanding ModernGov courses are Continuing Professional Development (CPD) certified, with signed certificates available upon request for event.

Enquire About In-House Training

To speak to someone about a bespoke training programme, please contact us:
0800 542 9414
[email protected]


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09:25 - 09:30


09:30 - 09:45

Trainer’s Welcome and Clarification of Learning Objectives

09:45 - 11:00

Introduction to Data Quality

  • Define data quality and fit for purpose data
  • Understand the data lifecycle and highlight the importance of data quality at each stage
  • Recognise the causes of poor data quality
  • Understand its impact on individuals and organisations
  • Identify the relationship of data quality to other data management disciplines
11:00 - 11:15


11:15 - 11:30

Holistic Approaches to Data Quality Improvement

  • Highlight why traditional approaches to data quality improvement have limited success
  • Recognise the need for a holistic approach encompassing people, process and technology
  • Provide an overview of the five step A2E Data Quality Framework (A2E)
11:30 - 12:15

Data Quality Framework Stage 1: Assess

  • Understand your organisation’s data quality needs – today and tomorrow
  • Identify key data quality stakeholders
  • Evaluate data quality ‘fitness for purpose’
  • Uncover and tackle data quality problems
  • Apply this to the data management lifecycle
12:15 - 13:00

Data Quality Framework Stage 2 : Baseline

  • Introduce the six Data Quality Dimensions as defined in the UK government’s Data Quality Framework
  • Understand how to profile data sources – how to baseline and quantify data quality
  • Assess the organisational impact of data quality problems
13:00 - 14:00


14:00 - 14:30

Data Quality Framework Stage 3: Converge

  • Prioritise data quality requirements and problems
  • Produce data quality improvement business cases
  • Gain business and IT commitment to improvement
14:30 - 15:45

Data Quality Framework Stage 4: Develop

  • Form data quality assurance / improvement teams
  • Identify key roles and responsibilities
  • Analyse root causes of data quality problems
  • Outline the importance of data quality business rules, strong data lineage and detailed metadata
  • Create and deliver Data Improvement Plans (DIPs)
  • Learn to reduce errors, inefficiencies and other flaws that can occur when accessing, sharing and transferring data
  • Outline best practices for validating and cleaning data
15:45 - 16:00

Data Quality Framework Stage 5: Evaluate

  • Recognise the importance of managing data quality assurance and improvement as a continuous process throughout the data lifecycle
  • Be aware of useful tools and technologies which support continuous improvement
  • Understand how to sustain data quality through data governance
  • Design and deploy systematic data quality monitoring and reporting
  • Know how to highlight the benefits of better data quality
16:00 - 16:15

Round-Up and Key Takeaways