Data Quality – A Worldwide Issue

Danette McGilvray | Publish Date: March 2024

A personal message from an IRMUK instructor to IRMUK readers. Hello! My name is Danette McGilvray. I am a data quality expert, consultant, instructor, and author of Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™, 2^nd Ed.

Data quality is a worldwide issue. This is one reason I enjoy teaching my course, Ten Steps to Quality Data, through IRMUK. It gives me the chance to reach people in many different countries. I have taught people from every continent in the world, except Antarctica – people in countries such as Argentina, Australia, Belgium, Brazil, Canada, China, Czech Republic, Denmark, Egypt, Finland, Hungary, Indonesia, Ireland, Japan, New Zealand, South Africa, Thailand, United Kingdom, United Arab Emirates, United States – and the list goes one. I get messages from others who have picked up the book and put it to use themselves. The Chinese translation of Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™, 2^nd edition, was recently released

Why am I bringing this to your attention? If you struggle with data quality issues in your organization or if you are feeling the impacts from the poor-quality data, you are not alone. And yet people in many places around the world have been able to use my Ten Steps methodology to help. The Ten Steps applies to any type of data, for any kind of organization – such as for-profits in any industry, non-profits, government, education, and healthcare. The Ten Steps method has stood the test of time. Callout boxes called Ten Steps in Action (in the second edition of the book) highlight many ways, large and small, in which the Ten Steps has been applied, in different organizations, in various countries around the world. The Ten Steps works no matter the country, culture, or language. No matter where you are, now might be just the right time to look at data quality. I invite you to join my upcoming course, 15-17 May 2024, through IRMUK: https://irmuk.co.uk/event/ten-steps-to-quality-data-4/

In the meantime, let me give you a brief overview. The Ten Steps methodology is a fundamental, flexible, and scalable approach to assess, improve, manage, and sustain the quality of data on which every organization depends. Part of the beauty of the Ten Steps is the holistic view it takes, a broad look at data quality. Some people think data quality is only about data entry, or correcting data errors, or having a dashboard to monitor the state of data quality. Of course, these are part of managing the quality of our data. But data quality is much, much more. Let me talk about this broad view of data quality for a few minutes.

For any data work, start with business needs. Business needs – my over-arching phrase to mean the most important things to an organization to satisfy customers. Your customers may purchase your products or services or may be others within your organization who will make use of the data you pass on to them. Business needs also covers what is needed to manage risk, implement strategies, achieve goals, address issues, and take advantage of opportunities. What is important to your organization? I guarantee that data is part of what is needed to address your organization’s business needs at any point in time.

A broad view of data quality means we include the information life cycle, which is often referred to as lineage. We must understand what is happening to our data throughout its life – from planning for the data we need to obtaining the data, to storing and sharing the data, to applying the data and disposing of it when the time is right. POSMAD is my acronym for the basic phases of the information life cycle I just described.

We also need to understand and manage the processes which affect the data in every phase of the life cycle, the technology involved, and the people and organizations who are accountable and responsible – many who affect data every day but do not have data in their job titles and do not realize the role they play in ensuring the data is right – not just for their uses, but for others who use the data they pass on.

As we take this all-inclusive view, we must pay attention to people (people again). Step 10 of the Ten Steps is all about communicating, managing, and engaging with people throughout. This methodology is, I believe, one of the first data-related methodologies to clearly show how to include the human element in our data quality work. Addressing the human element is essential to building trust in the data. It is not enough to have high-quality data, people must have confidence in that data for it to be used effectively.

By bringing together all these aspects, you will see relationships that you might not have seen before. You will develop and implement more sustainable solutions that not only increase the quality of data, but help your organization be more successful – which is why we care about data quality in the first place.

But don’t get overwhelmed. You can apply the Ten Steps methodology as a single person to a short few-week project. The Ten Steps can be applied to a larger issue being addressed by a small team of 3 to 4 people. OR Essential activities and techniques from the Ten Steps can be included in other projects, such as technology or IT projects where data is migrated and integrated. Use the Ten Steps now for your immediate concerns. Come back to them later as other data-related situations arise.

I truly believe that each of us can make our organizations more successful, and our world a better place through data quality. Please join my course to learn more. Then take the lead, do your part, bring value, and enjoy the work!

Note: Portions of this article contain material from the book Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™, 2^nd Ed. (Elsevier/Academic Press, 2021) by Danette McGilvray, See https://shop.elsevier.com/books/executing-data-quality-projects/mcgilvray/978-0-12-818015-0

An internationally respected expert, Danette McGilvray guides leaders and staff as they increase business value through data quality and governance. This data approach benefits focused initiatives (such as security, analytics, digital transformation, artificial intelligence, data science, and compliance) and means that high-quality data will support whatever is most important to an organization, protect its data assets, and help manage risk. Focusing on bottom-line results, Granite Falls’ strength is in helping clients connect their business strategy to practical steps for addressing specific data quality/governance issues, implementing on-going foundational programs, and improving operational processes.

As president and principal of Granite Falls Consulting, Inc., Danette is committed to the appropriate and effective use of technology and also to addressing the human aspect of data management through communication and change management. Danette is known for her Ten Steps™ approach to data quality which has been embraced as a proven method for creating, managing, and sustaining high-quality data in any organization. She is the author of the book Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™, 2nd Ed. (Elsevier/Academic Press, 2021). Contact her at danette@gfalls.com, connect with her on LinkedIn: Danette McGilvray, and follow her on Twitter: @Danette_McG.