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.
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. (www.gfalls.com) All rights reserved worldwide.