Tackling Data Quality Problems

Five step approach – As simple as A to E

[Let op: dit compacte ochtendseminar wordt verzorgd als online virtuele training uiteraard met behoud van interactie!]

In our digital age many organisations seek to become data driven and are investing heavily in new technologies to make it happen. But this investment will not deliver its promised riches unless the data itself on which it depends is fit for purpose and of the required quality. Having accurate, complete and reliable data is a ‘must have’ for any digital business or organisation.

One of the oldest maxims in data management is ‘Garbage in, Garbage Out’. If data is of poor quality other data management disciplines can never fulfil their promise. Business Intelligence initiatives will generate information and outcomes that cannot be relied on, leading to poor decision making. Master Data Management will fail to deliver a single, integrated view of a customer or product, and so on. 

More importantly, poor data quality has a significant adverse impact on the business or organisation itself. When data is not fit for business purpose, revenue is lost, unnecessary costs are incurred, productivity and efficiency suffer, and the risks of breaching regulatory and legal requirements (for instance with GDPR) are greatly increased.

Despite this, the reality is that data quality in most organisations remains poor, and often very poor. A study conducted by The Harvard Business Review and MIT in 2017 found that fully 97% of records surveyed in key organisational databases contained critical data quality errors.[1] Identifying and tackling these issues is a top priority for any organisation which seeks to extract the maximum value from its data assets. 

The Workshop

In this half-day workshop Nigel Turner will answer a number of fundamental questions that need to be addressed to deliver fit for purpose data quality. These include:

• Why should I care about data quality?
• How do I know what my main data quality problems are?
• What is the impact of these on my organisation and how can these be measured?
• What problems should I seek to address first?
• How can the problems be improved?
• Who should be responsible for taking action?
• How can I ensure that I prevent further data quality problems arising in the future as the volume, variety and velocity of data increases?

The workshop will help you answer these questions by taking you through a five step approach to identify, deliver and sustain data quality improvement. The five steps of Assess, Baseline, Converge, Develop & Evaluate (A2E) provide a simple framework and methodology to enable data quality to be tackled in a systematic and repeatable way. The session will also contain practical exercises to help participants apply A2E before doing it for real back in their own organisations.

Learning Objectives

• Understand what ‘fit for purpose’ data is, and is not
• Describe the dimensions of data quality
• Know the main causes of poor data quality
• Highlight the impact of poor data quality on individuals and organisations
• Understand the relationship between data quality and other data management disciplines, with particular emphasis on data governance
• Highlight the shortcomings of traditional ways of tackling poor data quality and the importance of a holistic approach, involving people, process and technology
• Learn the five steps of the A2E methodology and how to apply it to identify, prioritise and address data quality problems
• Specify and apply the main activities and deliverables of each of the five steps
• Be able to understand and develop business rules to baseline data quality and to set improvement thresholds
• Be aware of software tools that can help to support and automate the A2E approach.

Who is it for?

• Business people with a strong interest in data and how to improve it
• Data Governance professionals
• IT professionals who are dealing with data quality problems in their role in developing and supporting operational systems
• BI and Analytics specialists who want to ensure that the quality of data they rely on is fit for purpose
• Business Analysts
• Data Architects
• Data and IT consultants.

[1] “Only 3% of Companies’ Data Meets Basic Quality Standards”, Tadhg Nagle, Thomas C. Redman & David Sammon, Harvard Business Review, 11 September 2017