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Waking Up to Data Quality

May 12, 2018 • STRATEGY & MANAGEMENT, TECHNOLOGY, Business Process, Digital Transformation

By Tadhg Nagle, Thomas Redman and, David Sammon

Bad data impacts managers and their companies far more than most realise today and presents an enormous hurdle for any data strategy. They can take many simple steps to improve, but they must first “wake up” to the issue. Here, we present three ways that can help them do so.

 

Most managers are vaguely aware that data quality is an issue. They may hear of re-stated financial statements, difficulties in meeting regulatory issues such as GDPR, or technical issues involving systems that do not interconnect.  Even though they themselves may be victimised by bad data from time to time, they see the issues as “belonging to someone else” and assume there is little they can do about them anyway.

All managers can, and must, take some rather simple steps to address data quality.

The reality is completely different. Bad data hurts managers, the people that report to them, their departments, and their companies every day, wasting time, adding enormous expense, compromising decisions, and generally making anything they do more difficult.1 Further, through their inattention, they both contribute to their own problems and to data quality issues that impact others. All managers can, and must, take some rather simple steps to address data quality. Fortunately, over the last thirty years, the basic frameworks, approaches, methods and organisational structures needed to attack data quality have worked and proven themselves.2  

This article aims to help managers and executives “wake up” to data quality, in three ways.  First, it relays the stories of those who’ve made their first-ever data quality measurements and come to grips with the implications in their own words. All managers should take themselves through the experience. Second, it summarises actual data quality statistics, providing a sobering alert that all may be victims of bad data, without even knowing it. Third, it puts data quality in a forward-looking context. After all, smart companies are investing in analytics, artificial intelligence, data-driven cultures, and monetising their data. All such efforts will be slowed and many doomed at today’s quality levels. 

 

Awakening to Data Quality

As part of Executive Education Programs we conduct in Ireland, we ask participants to make their first quality measurment on data critical to their departments, using the Friday Afternoon Measurement (FAM) method.3 The method is simple, taking no more than two hours to complete (even on a Friday afternoon), and best conducted by teams. Critically, FAM narrows the focus to the most recent business activity and the most important data. We advise all managers, everywhere, to conduct their own FAMs, following the steps provided in the references.

The result is a number, called DQ, ranging from 0 to 100 that represents the percent of data records created correctly the first time. Importantly, DQ score is also interpreted as the fraction of time the work was done properly, the first time. We then ask executives to reflect on their results, to explore the implications, and to tee up improvement projects. This effort, lasting at most a few weeks, comprises their awakening to data quality.  From 2014 – 16, we took 75 executives through this exercise. Their stories are fascinating – and instructive!



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About the Authors

Dr. Tadhg Nagle is an associate faculty member and the co-Director of the MSc in Data Business at the Irish Management Institute (IMI). Tadhg is also a Lecturer (Business Information Systems) at Cork University Business School (CUBS), University College Cork (UCC). Specialising in the business value of data, Tadhg has created a number of tools and techniques (such as the Data Value Map – http://datavaluemap.com) to aid organisations in getting the most out of data assets. He has also developed a brand of applied research (Practitioner Design Science Research) that arms practitioners with a simple and scientific methodology in solving wicked problems.

Dr. Thomas C. Redman, “the Data Doc,” President of Data Quality Solutions, helps start-ups and multinationals; senior executives, Chief Data Officers, and leaders buried deep in their organisations, chart their courses to data-driven futures, with special emphasis on quality and analytics.  Tom’s most important article is “Data’s Credibility Problem” (Harvard Business Review, December 2013) He has a Ph.D. in Statistics and two patents.

David Sammon is a Professor (Information Systems) at Cork University Business School, University College Cork, Ireland. He is co-Director of the IMI Data Business executive masters programme and is co-Founder of the VIVID Research Centre.

 

References

1. Redman, T., “Seizing Opportunity in Data Quality, Sloan Management Review,https://sloanreview.mit.edu/article/seizing – opportunity – in – data – quality/

2. See, for examples, English, L., Information Quality Applied, Wiley, 2009, Loshin, D., The Practitioner’s Guide to Data Quality Improvement, Elsevier, 2011, McGilvray, D., Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information, Morgan Kaufmann, 2008, Redman, T., “Opinion: Improve Data Quality for Competitive Advantage,” Sloan Management Review, p. 99, Winter 1995, and Redman, T., Getting in Front on Data:  Who Does What, Technics, 2016.

3. See Redman, T., Getting in Front on Data: Who Does What, for full details or https://www.youtube.com/watch?v=X8iacfMX1nw for a quick instructional video.

4. Redman, T., Data Driven: Profiting from your Most Important Business Asset, Harvard Business Press, 2008.

5. “Data and Organizational Issues Reduce Confidence,” Harvard Business Review, 2013.

6. Dietvorst, Berkeley J. and Michelman, Paul, “When People Don’t Trust Algorithms,” Sloan Management Review, p. 11, Fall, 2017.

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