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Perennial Data Problems

By: , Posted on: April 5, 2016

perennials

Spring is here, which for many of us means that perennial flowers are beginning to bloom. These flowers are often favored by gardeners and landscapers because they die off in the winter and re-emerge from their roots each spring.

For many organizations, data quality issues crop up in a similar manner. Issues arrive, data is corrected, and the issue disappears—until the next time. Like perennial bulbs, the roots are still there and the data quality problems pop up again. Unlike perennial bulbs, which we encourage to grow, the data quality problems are not welcome.

Also unlike yearly flowers, the cycle of data quality problems is not “just the way things are”. These reactive cycles are ultimately wasteful of person hours, resources, and money. Undergoing massive data-cleaning efforts or searching for a cure-all technology are often ineffective in the long term. If data quality efforts continue to temporarily address problems, the cycle of bad data can continue indefinitely.

The news is not all bleak however. Once an organization realizes the value of being proactive they can begin the path to addressing the root causes driving the frequent “blooming” of data quality issues.

Figure 1 illustrates a typical trajectory of moving from being unaware of data quality to ultimately optimizing the quality of the data

data quality maturity curve
Figure 1. The Data Quality Maturity Curve

While this trajectory shows a smooth movement upward, the reality of the journey is not so simple. It is often two steps forward and one step back, and there may be pauses in progress along the way. To truly realize the value from trusted data, it is imperative to move from a place of reacting to problems to addressing the issues at the root.

Identifying root causes is only one of the important steps to managing the quality of data. For a systematic approach to improve and create data and information quality within any organization, see my book: Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™, available through Morgan Kauffman.

To learn more about tactical methods to move up the data quality maturity curve and gain insight from the journeys of two real companies, attend my Enterprise Data World course: “Climbing the Data Quality Maturity Curve,” April 18, 2016 in San Diego, California.

Executing Data Quality Projects Cover

Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™ is available for purchase on the Elsevier Store.

Use discount code STC215 and save 30% on your very own copy!

 About the Author

Danette McGilvaryDanette McGilvray is president and principal of Granite Falls Consulting, a firm that helps organizations increase their success by addressing the information quality and data governance aspects of their business efforts. Focusing on bottom-line results, Granite Falls’ strength is in helping clients connect their business strategy to practical steps for implementation.  Granite Falls also emphasizes the inclusion of communication, change management, and other human aspects in data quality and governance work.

Danette is the author of Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™ (Morgan Kaufmann, 2008).  An internationally respected expert, Danette’s Ten Steps™ approach to information quality has been embraced as a proven method for both understanding and creating information and data quality in the enterprise. A Chinese-language edition is also available and her book is used as a textbook in university graduate programs.  You can reach her through email: danette@gfalls.com, LinkedIn: Danette McGilvray, Twitter: @Danette_McG or phone: +1 510-501-8234.

This article Copyright 2016 by Danette McGilvray, Granite Falls Consulting, Inc.  All rights reserved worldwide.

 

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