The Book Look: Data Literacy

With everyone talking about becoming more data literate, it’s a wonder that there are no books on data literacy – until now. Data Literacy by Peter Aiken and Todd Harbour is a very comprehensive study of and guidebook to data literacy, covering the levels of data literacy and providing a framework to increase data literacy both personally and within your organization.

Part I covers the importance of being data literate. What I found very interesting were all of the statistics that show how little most people know about data, including protecting their own data privacy. There are Data Lessons shared throughout the book, yet in Part I, I can relate most to Data Lesson #12: “Technology can only address specific parts of the data challenges. The other 90% consists of people and process challenges that currently are unmet.” I’ve worked with many organizations over the years, where I witness teams throw technology at problems instead of focusing on the sometimes less exciting (but more important) areas of people and processes.

Part II dives deep into the different kinds of data citizens within the Digital Civics Framework. I enjoyed learning about these different types of people, and considered how to characterize people I know (and even myself). The book covers Mobile Data Spreaders (MDS), Adult Data Spreaders (ADS), Knowledge Workers (KW), Data Teachers (DT), and Data Professionals (DP). I consider myself a DP, but as I read the data knowledge areas of each of the different types, I realized I also shared some of these characteristics.

Part III explains how to develop a data-literate organization, starting with a 12-step approach to improving organizational data literacy. A very useful Data Doctrine is shared in this part of the book, along with a series of challenges we might face in making our organizations more data literate. With permission from the publisher, here is an excerpt from the challenges section of the book:

The existence of what we call “the Data Matrix” is the primary reason that every citizen should want all citizens to become more data literate. The data matrix is our name for all unknown, unknowable, and opaque data exchanges between organizations. Our information has historically been viewed as private and constitutionally protected. However, information leaks are becoming more common. They disproportionately impact those who have the most to lose. These same individuals are also the most unaware of the inherent danger to their freedom posed by the data matrix. Today, nothing is off-limits, and nothing stops surveillance capitalists from accessing every part of our lives. Criminals and bad actors are already using citizens’ own data against them to influence their behavior unjustly.

Understanding these dangers alone might make data literate citizens pause the next time they receive a request for data. Sadly, we see little effort to make things better. Instead, we see citizens surrendering more personal data without understanding the consequences. We witness appalling data practices, a growing dependency on technology, and ruthless surveillance capitalists that track every aspect of our daily lives. Predictions were that 2020 would be remembered as the year when artificial intelligence and machine learning ran out of usable data! But we have done anything but run out of data—what’s lacking is the ability to make any sort of useable meaning from most of it. The accumulated data debt wreaked havoc as algorithm after algorithm fell victim to lack of training data sets that severely limited their value! Advances are unattainable due to resource allocation imbalance. The COVID pandemic that dominated 2020 will resonate louder than the training-data shortage, but a strong case can be made that the training data shortage will have a far greater negative citizen impact!

The pandemic revealed a previously hidden societal weakness—a tangible lack of data literacy. We saw the impact of this illiteracy during the early stages of the pandemic—too many citizens could not use data to make decisions or consider various action plans suggested. Entire countries demonstrated they were incapable of distinguishing fact from fantasy, and their leaders were willing to make ultimately dangerous decisions using unreliable or insufficient data and an utter lack of consequential understanding. When leaders don’t understand how data can help them understand a pandemic, society suffers, and the results are evident for all to see.

Like the pandemic, data literacy isn’t constrained to any geographic area, social class, or job. It affects everyone—politicians, corporations, and the public. And their behavior shows just how widespread is the problem. There is a great danger of not preparing citizens to live in a digital society. Lowering citizen data literacy reduces risk and creates benefits for all citizens. When citizens are data literate, each one becomes part of a frontline defense for the ethical use of data. But if the citizens do not understand data and the consequences of their digital actions, the balance of societal power shifts to the data literate at the expense of everyone else.Data nerds and others who are data literate must help the data illiterate to become data literate. We offer a framework to focus efforts, and we discuss issues related to increasing data literacy across the nation. The effort to develop and improve data literacy is no small feat by any measure and will take years to accomplish. However, we believe this effort is essential to a long-term and stable society.

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Steve Hoberman

Steve Hoberman

Steve Hoberman has trained more than 10,000 people in data modeling since 1992. Steve is known for his entertaining and interactive teaching style (watch out for flying candy!), and organizations around the globe have brought Steve in to teach his Data Modeling Master Class, which is recognized as the most comprehensive data modeling course in the industry. Steve is the author of nine books on data modeling, including the bestseller Data Modeling Made Simple. Steve is also the author of the bestseller, Blockchainopoly. One of Steve’s frequent data modeling consulting assignments is to review data models using his Data Model Scorecard® technique. He is the founder of the Design Challenges group, Conference Chair of the Data Modeling Zone conferences, director of Technics Publications, and recipient of the Data Administration Management Association (DAMA) International Professional Achievement Award. He can be reached at

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