Data is permeating our personal and professional lives. Whether it was tracking COVID case numbers during the pandemic or updating customer contact information, the spread and importance of data has continued to grow. Alongside this trend, we also see a rise in professional roles associated with data, such as data scientists, data engineers and data analysts. These formal roles can create the impression that there are gatekeepers who are solely responsible for leveraging the value of data and ensuring its quality on behalf of everyone else. But this is a detrimental approach to business, as it risks alienating those whose jobs are consistently impacted by poor data quality and who have the potential to transform the status quo.
Thomas C. Redman’s book People and Data: United to Transform Your Business targets these diverging trends of the growing spread of data and the increased silos departmentalizing those who work with data. The central premise of the book is that “regular people,” employees without data in their formal job title, should feel empowered to more fully engage to both leverage the benefits of company data and be responsible for maintaining its quality.
The importance of engaging everyone in data conversations is not simply a theoretical or philosophical discussion. Bad data quality has dangerous, real-life consequences. One study cited in the book finds that the cost of bad data to be 15% to 25% of revenue for most companies. Another estimates that knowledge workers spend 50% of their time dealing with mundane data quality issues. This can involve going into datasets to manually correct individual records, requiring secondary confirmation before making decisions with data and fixing the inevitable mixtapes that emerge from faulty datasets. Finally, an IBM study states that bad data costs the US economy $3.1 trillion per year.
In light of these systemic challenges, this book provides strategic advice to corporate leadership on how to address these fundamental concerns. The core message is visualized in the figure pictured below with “regular people” being put at the center of the organizational process. Through the guidance of senior leadership and technical support from core data teams and information technologies, this vision allows regular people to tackle the challenges of data quality head on. The glue that connects these various segments is identified in the book as “fat organizational pipes” that include a focus on common language and change management. These concepts focus on minimizing the use of technical jargon to improve collaboration between teams and to have a clear vision for how a project will produce positive change for internal and external stakeholders.
While tackling substantive and complex challenges, this book does an excellent job weaving together personal stories and case studies to showcase how these principles play out in practice. For data quality considerations, for example, focusing on a plethora of low hanging fruit allowed many clients Tom has worked with to build momentum and gain progress in motivating regular people to feel more empowered with data. You’ll see in my interview below some more of the motivation and insights that emerged from reading this book.
Joshua Pine (JP): What was the inspiration for writing this book?
Tom Redman (TR): I was trained as a statistician and was deeply rooted in technology. What I found was that every successful project that I worked on had someone who basically knew nothing about statistics who was the linchpin of it all. They often had a business problem to solve or were disappointed in how something was working. These people kept appearing on my radar screen as they found a way to empower themselves, to challenge the status quo that provided bad data quality and take the initiative to make a difference. As I was reflecting over the pandemic and writing various articles summarizing these insights, I quickly realized that fundamentally we can’t do anything with data without the people behind it and that I wanted to write a book about it.
JP: Throughout the book you reference how data impacts both our personal and professional life. What are ways to make the connection between data in these seemingly distinct spheres?
TR: During the pandemic especially, we heard so many stories about people facing tough questions, like if there was going to be enough toilet paper or whether their kid would be able to go to summer camp. In some respects, these weren’t all that different from problems that people had faced at work, but they were just more intense and more personal. It really starts with being a good data customer and being able to understand and leverage data to solve problems, whether they are personal or professional.
JP: Could you share more about the concept of data quality that is a central theme in your book?
TR: Data quality frequently ranks low on people’s priority list and is often placed on the back burner. While everyone knows the concept of “garbage in, garbage out,” a lot of people are able to rationalize it and say that they aren’t that much worse than everyone else. If you look at the facts though, it is deeply troubling the impact that bad data has on businesses and decision making.
Another piece of the puzzle related to data quality is that if you have 100 issues, maybe two of them are really complex and nearly impossible to solve. But going down the list, probably about 90 of them have a really simple and straightforward solution to address the immediate problem as well as the root cause. Oftentimes however, it is easy to gravitate and only focus on the really challenging ones while ignoring a lot of the low hanging fruit that is especially well-suited to address with the collective support of regular people throughout an organization.
JP: Finally, while this isn’t a book specifically on data visualization, what are some of the connections that you see between data quality and its importance within the data visualization industry?
TR: The two main facets of data quality are whether the data is right and whether you are using the right data. Extrapolated to data visualization, you should also be asking if the data is being visualized in the right way as part of a quality check. In order to answer these questions you need to be really clear on who the customer is and what they want to know. That may mean that you end up presenting the data in different ways to different audiences depending on what their needs are. Focusing on the user-centric mindset will be essential for integrating data quality into the data visualization process.
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Joshua works on the Urban Innovation team at the National League of Cities (NLC) where he leads the organization’s data visualization portfolio. He specializes in leveraging data to inform local policymaking and in amplifying best practices through data storytelling. Based in Cincinnati OH, he is an electric bike enthusiast and passionate advocate for active transportation.
