Data Quality: A “must” for the Business Success
Many organisations are seeking to become data driven and are investing heavily in new technologies to make it happen. But this investment will not deliver its promised benefits 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 rules 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. Artificial Intelligence and Machine Learning will deliver faulty insights and generate misguided actions. All Data Management disciplines rely on a sound and trusted data foundation, but all too often this is not the case.
Worse still, poor data Quality has a significant direct and adverse impact on the business or organisation itself. When data is not fit for business purposes, 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. Poor Quality data can damage the brand and reputation of any organisation.
Despite this, the reality is that data Quality in most organisations remains poor, and often very poor. A major 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. Other surveys since have reinforced these findings and so identifying and tackling data Quality issues is a top priority for any organisation which seeks to extract the maximum value from its data assets.
In this two day seminar and workshop Nigel Turner will answer a number of fundamental questions that need to be answered to deliver fit for purpose data Quality. These include:
- What is good data Quality?
- Why should I care about it?
- How do I know what my main data Quality problems are?
- What is the impact of these on my organisation and how can this be measured?
- How do I make the case for action?
- What data Quality problems should I seek to address first?
- How can these data Quality problems be fixed or improved?
- Who should be responsible for taking action?
- How can I ensure that I prevent future data Quality problems as the volume, variety and velocity of data increases?
What you will learn
- Understand what ‘fit for purpose’ data is, and is not
- Define data Quality
- Describe the dimensions of data Quality
- Know the main causes of poor data Quality
- Highlight the impact of poor data Quality, both on individuals and organisations
- Understand the relationship between data Quality and other Data Management disciplines
- Highlight the shortcomings of traditional ways of tackling poor data Quality and the importance of a holistic approach, involving the data itself, people, process and technology
- Learn the five steps of the Data Quality Framework approach (Assess, Baseline, Converge, Develop & Evaluate) and how to apply them 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, including Artificial Intelligence and Machine Learning technologies
- Understand what future data Quality challenges organisations will face and how data Quality approaches and techniques are evolving to address these
- Data Quality definitions and drivers
- The Data Management lifecycle and Data Quality
- Causes and impacts of Data Quality
- Data Quality as the foundation of other Data Management disciplines
- The need for a holistic approach when solving Data Quality problems
- Data Quality Framework – a five step approach to improving Data Quality
- The future of Data Quality – new approaches and technologies