The Little Book of Data, Justin Evans. HarperCollins Leadership (ISBN: 9781400248353) 2025.
Summary: Stories of how people have used data to solve big problems and how that might apply in one’s own work.
Some of us are in data denial. We don’t think we need to understand it. Or its too complicated. Then, it’s just intimidating. And for some, it’s just downright evil. Justin Evans passionately believes that when we are in data denial, we forfeit a key resource for advancing our careers and our organizations. Data can help us solve big problems. Fundamentally, it’s not about math but about ideas. As an undergrad English major, Evans says anyone can understand this stuff if we don’t “give power to the twerps.” And while there are real concerns with surveillance capitalism, it is a Promethean fire. We wield both great power that charges us with responsibility.
Evans learned about the power of data to solve problems in a career that included work with Nielsen, Comcast, Samsung, and a start-up along the way. He wrote The Little Book of Data to tell stories of how data has solved a variety of big problems. And he helps us consider the opportunities this presents each of us in our chosen work.
But first he begins with a personal account of how we “shed” vast amounts of data every day. Our email accounts, our rideshare apps, GPS, streaming services, medical information systems…and so much more. A whole infrastructure has been created to identify, store, and utilize that information. And chances are, in whatever line of work you are in, data is there to help with the problems you are trying to solve.
For example, we are introduced to:
- Herman Hollerith, who worked with the Census Bureau preparing for the 1890 census. There were an unprecedented number of variables on which they were to collect information. All of it would need to be cross-matchable. Hollerith created the punch card to collect this information and a tabulating machine to analyze different combinations of data. And so was born the enterprise we now know as IBM.
- More contemporarily, we meet Priya, who developed analytics to study websites used to traffic women, enabling the NGO she worked for to build cases to rescue underage women.
- Barry Glick started working for a company that had a division distributing maps to gas stations. It was called Mapquest. He figured out how to connect the vector data of driving directions to raster data used to make visual maps. And then they put it online…
- Sharon Greene was an epidemiologist in New York City when COVID broke loose. Her team figured out a way to use daily testing data to identify hot spots, surge resources to them, resulting in dropping death rates in each of these spots.
- Adam Greene developed textual analysis to identify loneliness among senior adults through phone conversation, helping seniors get more socially connected.
The stories help illustrate different aspects of data science from the development of artificial intelligence to how we use data to count, track, spot anomalies like impending earthquakes, match genetic attributes, certify grades of meat and measure performance. We learn about the use of data to crystallize complex information by meeting railroad nerd Henry Varnum Poor. Poor went from editing a railroad journal to create an objective resource to help those investing in railroads. Poor’s Manual of Railroads provided information on road miles, rolling stock, passenger numbers, freight tonnage…and the names of each director. Eventually this became Standard & Poor, and crystallized all this data into a rating, AAA to D (bankrupt).
Along the way, Evans tells stories from his own career journey. Each of the chapters concludes with a ‘key points” summary, thought starters, and “Where do we go next?”. Evans offers both inspiring stories combined with a “see, you can understand this” approach.
Most of the book was pretty positive about the potential of the world of big data. But Evans includes a chapter on data bullies along the way, those who use their expertise to conceal information. He offers a humorous account of how he asked such people to break down their claims and explain everything he didn’t understand.
At the end of the book, he returns to the power of large tech firms and the issue of secrecy, illustrating it with how the AI industry used large amounts of copyrighted material secretly to train its Large Language Models. He argues that our data might be tagged in such a way to establish provenance, allowing its licensed or unlicensed use to be tracked. He also argues for data advocates for industries where the use of data to make decisions having implications for the rest of us would be less opaque–health insurance companies for example.
On the whole, Evans approach is to illustrate different ways data has been used to solve problems that matter. He helps readers think about the problems they are trying to solve in this light. Therefore, data becomes a useful tool instead of an amorphous, intimidating reality. For me, one of the biggest takeaways was that data ultimately isn’t about crunching numbers but about asking good questions. Then we look for the data sets that will help us answer those questions. I found this an encouraging and empowering approach. Evans acknowledges the realities of our world, including the AI explosion. And helps us see the opportunity all this data represents.
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Disclosure of Material Connection: I received a complimentary copy of this book for review from the publisher through LibraryThing’s Early Reviewers Program.
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