Are You New to Data Quality? Three Essential Best Practices

In Data Management, Data Quality by IRM UK2 Comments

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As I was growing up my mother was a fantastic seamstress. Depending on my needs at any point in time, she would make me school or casual clothes, a club uniform, or a prom dress. I was about 13 years old when my mother decided to teach me how to sew. As we worked together, we would argue and it wasn’t a pleasant situation. My mom proposed “I will sew all your clothes until you are 18 and graduate from high school. After that you’re on your own.” There was peace in the house and I still had clothes to wear.

Danette McGilvray, President, Granite Falls Consulting, [email protected]
Danette will be presenting the course, ‘Ten Steps to Data Quality‘ 17-19 June 2019 in London

I eventually got married and had children of my own. When they were young, I decided to make clothes for them. I followed the pattern for a simple shirt. It was only at that time, many years later, that I understood why my mother and I had conflicts as she tried to teach me to sew.

I was trying to learn by following the pattern step-by-step-by-step. My mother was such an experienced seamstress that she could see shortcuts to take. She knew some instructions would not apply in a particular situation or she had faster ways of doing them. However, I did not know the basics, didn’t understand, and was confused and frustrated. I needed to learn by following the instructions before taking the shortcuts. The difficulty of the pattern made a difference also. In other words, the first time you learn how to sew you might want to make a T-shirt and not a wedding dress!

Applying this to data quality, when you are tackling data quality issues for the first time, realize you are doing something new and allow for that learning curve. Here are three best practices:

  • Focus on the most important business needs and the most important data that supports those needs
  • Design a project with a scope that will provide results the business cares about within a reasonable time period
  • Use a solid methodology to guide your work, such as the Ten Steps to Quality Data and Trusted Information™, known as the Ten Steps

In your first project, you may be following the methodology closely. Through experience you will learn how to use it for various situations – deciding what works well in your environment and selecting which steps, which techniques, and the appropriate level of detail for any activity to best meet your goals. As you become more experienced, you will be able to tackle any data quality project (large or small) successfully and with confidence.

Danette 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 any organization. A Chinese-language edition is also available and her book is used as a textbook in university graduate programs. Contact her at [email protected], connect with her on LinkedIn: Danette McGilvray, and follow her on Twitter: @Danette_McG.

Note: Portions of this article contain material from the book Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™ by Danette McGilvray, published by Morgan Kaufmann Publishers, copyright 2008 Elsevier, Inc.


This article Copyright 2019 by Danette McGilvray, Granite Falls Consulting, Inc. ( All rights reserved worldwide.


  1. Great job, Danette! I couldn’t agree more with the central lesson – follow the method closely until you gain enough experience to make informed decisions how to adapt it for different situations. In recent years, I’ve had a number of clients approach me to say “we followed the method you taught us closely – almost slavishly – and the expected resistance to change never materialised.” (That was a key intent of some of the more unusual aspects of my approach – recognising what helps people get on board with significant change vs. resisting it.) Now, those same clients are coming up with novel (and effective!) variations that surprise and impress me.

  2. In this short article, Danette demonstrates the value of learning the best practices and concepts of any discipline properly before attempting to apply short-cuts or situation-specific techniques to a task. Whether the discipline is sewing, or data quality, or data governance, it is absolutely essential that the practitioner learn all the best practices and concepts fully (“the right ways”) before he or she starts to adapt the pattern or the process to fit their situation. Otherwise, the dress will not be wearable, the data quality initiative will not yield the expected results, the data governance program will fail. As Danette stated, “in the first projects, follow the methodology closely.” Once you have gained experience, you will be able to tailor the methodology to fit your situation, but only after you have learned the best practices and concepts thoroughly, so you know where you can adapt and where the best practices should remain intact. Even the best seamstress knows there are some things that should be included in every item of clothing.

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