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The Little Book of Data: Understanding the Powerful Analytics that Fuel AI, Make or Break Careers, and Could Just End Up Saving the World
We have been using data to solve problems for millennia. Ancient Sumerian culture used tally sticks to count sheep. John Graunt, a London hat-maker and perhaps the first data nerd, helped authorities fight the bubonic plague in 1651 by collecting data about all the ways people died. (Deaths by 110. Deaths by Fainting in the 1.)
So, what’s different now? With personal technology like the PC, and then the smartphone, combined with internet access, our data production exploded. In the U.S., internet penetration broke 80% in 2012. Smartphone penetration reached 80% in 2019. With those advances, internet-connected human beings started producing data about themselves at an astonishing velocity.
Our browsing. Our purchases. Our videos. Our maps. Our messages. Our meetings. As a civilization we are producing data at the rate of 79 zettabytes per year. How can we put that in context? In pure data terms, that’s 35 billion times the works of Shakespeare… every minute. The planet doth protest too much.
Justin Evans, an English major who ended up working a 20 plus year career as a marketing and advertising data professional, shares insights from his experience and through his interviews with other data people and how they solve problems. Turn to The Little Book of Data to better understand how the current data revolution impacts us all, learn how to spot the opportunities as well as the dangers that our increasing reliance on data poses, and eventually even become data people ourselves.
So, what’s different now? With personal technology like the PC, and then the smartphone, combined with internet access, our data production exploded. In the U.S., internet penetration broke 80% in 2012. Smartphone penetration reached 80% in 2019. With those advances, internet-connected human beings started producing data about themselves at an astonishing velocity.
Our browsing. Our purchases. Our videos. Our maps. Our messages. Our meetings. As a civilization we are producing data at the rate of 79 zettabytes per year. How can we put that in context? In pure data terms, that’s 35 billion times the works of Shakespeare… every minute. The planet doth protest too much.
Justin Evans, an English major who ended up working a 20 plus year career as a marketing and advertising data professional, shares insights from his experience and through his interviews with other data people and how they solve problems. Turn to The Little Book of Data to better understand how the current data revolution impacts us all, learn how to spot the opportunities as well as the dangers that our increasing reliance on data poses, and eventually even become data people ourselves.
304 pages, Hardcover
Published June 3, 2025
32 people are currently reading
2134 people want to read
About the author
Justin Evans
25 books156 followersJustin Evans is a digital media executive based in New York City where he lives with his family. He received a BA in English from Columbia University and a MBA in Finance from NYU Stern. His first novel, A Good and Happy Child, was named a Best Book of 2007 by the Washington Post, was translated into six languages, and optioned by a major film studio. Justin attended Harrow School for one year at the end of the eighties.
Write to Justin at justin@justinevans.com.
Write to Justin at justin@justinevans.com.
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Displaying 1 - 20 of 20 reviews
July 3, 2025
I'm not entirely sure what the point of this book was, but it was entertaining. I liked the little stories the author told to make his point about the gathering of data on the internet...especially with social media.
I wasn't at all in the target audience but still enjoyed this short read. The idea of data mining irks me, but by the end of this book, he made his case on why it exists and why it will never go away.
I wasn't at all in the target audience but still enjoyed this short read. The idea of data mining irks me, but by the end of this book, he made his case on why it exists and why it will never go away.
September 12, 2025
I was probably not the intended audience of this book because I am a data person and my career is in data analysis. What I think the author wanted to do with this book is to encourage other people not to be scared of data and data people taking over the world. But it is a weird book. It's some history of data, a bit of (rather deep) philosophy and then the powers of data and how we can use it. At the end of each chapter there are key points, thought starters and where we go next, which make it look like a text book. But for the rest of it it doesn't really feel like a text book. It's quite entertaining and full of interesting anecdotes. The problem for me was that when I got invested in one story, the author jumped to another story. Sometimes I didn't really understand what he wanted to tell with these stories until I reached the key points. In this way, it sometimes felt like a poorly written term paper. But I learnt some very interesting facts about climate, earthquake, and cancer research, for example, so it wasn't a complete waste of time.
I received a free copy from LibraryThing's Early Reviewers program, for which I’m very thankful. This is my honest review.
I received a free copy from LibraryThing's Early Reviewers program, for which I’m very thankful. This is my honest review.
October 13, 2025
Wanna write non-fiction? Start every chapter with a story :)
E.g:
“Priya had just relocated to the thriving, up-and-coming city of Pune, India, the day she saw the line.”
E.g:
“Priya had just relocated to the thriving, up-and-coming city of Pune, India, the day she saw the line.”
August 18, 2025
Data is not a phenomenon, it’s here to stay. This book will make you feel more confident in understanding the vast amount of data available to us all, and how best to leverage it in an ever evolving AI landscape.
October 16, 2025
I enjoyed The Little Book of Data.i hope i can figure out how to take the concept of identifying the data I have or is available for use in my field.
June 14, 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.
____________________
Disclosure of Material Connection: I received a complimentary copy of this book for review from the publisher through LibraryThing’s Early Reviewers Program.
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.
____________________
Disclosure of Material Connection: I received a complimentary copy of this book for review from the publisher through LibraryThing’s Early Reviewers Program.
October 4, 2025
Data became a thing. Not just a thing about a thing.
This line sums up why data is a powerful tool for business
Everyone around us is gathering data. A small transaction at the shop prompts the cashier to ask your phone number and email address. A reel we watch, a photo we share, a search we type in, a purchase we make, a show or an ad that we watch, everything translates into data with the objective of selling something to us.
No matter what we do, we end up becoming a data point and sometimes it is like being trapped in a maze from which there is no escape.
I enjoyed the anecdotal parts of the book with which the author started every chapter. While I am not unaware of the importance an application of data the book puts things in perspective.
My advice- do not rush over the book.Take it in bite sized bits and google as much as possible to understand certain aspects and stories.
The book is largely American in setting. Someone must write one for India. Imagine the stories that would make it into the book.
Readometer - not so little, detailed, anecdotal, summaries to deliver the crux of the chapter, definitely sets the mind thinking on application of data
Read more at
https://itchingtoread.blogspot.com/20...
This line sums up why data is a powerful tool for business
Everyone around us is gathering data. A small transaction at the shop prompts the cashier to ask your phone number and email address. A reel we watch, a photo we share, a search we type in, a purchase we make, a show or an ad that we watch, everything translates into data with the objective of selling something to us.
No matter what we do, we end up becoming a data point and sometimes it is like being trapped in a maze from which there is no escape.
I enjoyed the anecdotal parts of the book with which the author started every chapter. While I am not unaware of the importance an application of data the book puts things in perspective.
My advice- do not rush over the book.Take it in bite sized bits and google as much as possible to understand certain aspects and stories.
The book is largely American in setting. Someone must write one for India. Imagine the stories that would make it into the book.
Readometer - not so little, detailed, anecdotal, summaries to deliver the crux of the chapter, definitely sets the mind thinking on application of data
Read more at
https://itchingtoread.blogspot.com/20...
Want to read
October 28, 2025As heard on Clear+Vivid with Alan Alda (Justin Evans: Data Heroes)
https://stitcher.simplecastaudio.com/...
Data: dry and boring, right? Not in the hands of Justin Evans, a data expert himself, who set out to show that data is not only the lifeblood of today’s world; it is also the source of moving stories of other data experts who have achieved remarkable things – like the epidemiologist whose inspired use of data in the early days of Covid helped save hundreds of thousands of lives in New York city alone.
Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
https://stitcher.simplecastaudio.com/...
January 31, 2025
Thank you to NetGalley, the author, and the publisher for this complementary ARC in exchange for my honest review!
This book gives a quick look through the history of data up to current times from the perspective of an English major that ended up becoming a data professional without writing code (I'm a little jealous).
Each of the stories was quick, interesting and relatable - I felt that the author did a great job connecting historical events to how data is used today. There were several points made throughout the book that really resonated to me as a data scientist - including how no model is perfect but focusing on if it's better than the alternatives.
I strongly recommend this book to anyone interested in data, it acts as a good intro to data and analytics that is also very readable for someone well-versed in the subject.
This book gives a quick look through the history of data up to current times from the perspective of an English major that ended up becoming a data professional without writing code (I'm a little jealous).
Each of the stories was quick, interesting and relatable - I felt that the author did a great job connecting historical events to how data is used today. There were several points made throughout the book that really resonated to me as a data scientist - including how no model is perfect but focusing on if it's better than the alternatives.
I strongly recommend this book to anyone interested in data, it acts as a good intro to data and analytics that is also very readable for someone well-versed in the subject.
November 12, 2025
What was the point it was trying to make? Who is this for? It feels like it's trying to appeal to everyone and yet to a very niche type of people all at once. It tries to be actionable and gives you things to think about, but that is more for either business owners or people already working with data, in which case this book tells you nothing new whatsoever. It feels like a data scientist trying to convince you that his job is cool.
I didn't like the narration either. I would not have gone with the choice of having the author also narrate the book.
I didn't like the narration either. I would not have gone with the choice of having the author also narrate the book.
June 7, 2025
I received this book as part of a Goodreads giveaway.
This is a great book for anyone over 50 trying to understand data and its many uses. The author takes the reader from a colonoscopy to learning to swim at summer camp and how data affects the most mundane steps of life-such as how dangerous the water is and what are the chances of getting eaten by a shark in the camp lake. With facts and humor the uses and effects of data on everyday life are presented
This is a great book for anyone over 50 trying to understand data and its many uses. The author takes the reader from a colonoscopy to learning to swim at summer camp and how data affects the most mundane steps of life-such as how dangerous the water is and what are the chances of getting eaten by a shark in the camp lake. With facts and humor the uses and effects of data on everyday life are presented
May 8, 2025
I got this book through NetGalley so had a chance to read it before it came out. I enjoyed the read a lot. It didn’t get too deep into the data, but shared many insightful examples of data things. The end of the chapter exercises were well thought out and put together as well.
June 23, 2025
Interesting to a point but too shallow for a person with information systems experience
he doesn’t distinguish between information and data and knowledge
It’s just little human interest stories that involve data
he doesn’t distinguish between information and data and knowledge
It’s just little human interest stories that involve data
July 31, 2025
Some good stuff here, but too riddled with personal opinions and philosophy. I did like how he broke down categorizations of data and how it is used. Unfortunately, examples on things like Covid-19 were limited and did not address the broader understandings of the data was really about.
August 16, 2025
I really appreciated the summary at the end of each chapter cause I found the writing tedious and I struggled to stay interested. This could have been a 10 page article and covered the same amount of information.
September 21, 2025
I'm giving it 4 stars because it's a topic I haven't thought about in this way and it opened my mind to new thoughts and areas and I liked that. There are lots of stories that keep all the data interesting.
October 15, 2025
A good entertaining read for analogies and stories around data. Maybe good for non technical readers but won't add anything tangible in terms of learning except for analogies to think and understand how data works.
August 9, 2025
Good book overall. Easy and enjoyable read. Wish there was more discussions on LLMs and AI woven into the main portion though. Felt like it was discussed most heavily in the conclusion.
October 11, 2025
Great book about how big data works and examples of how it is used. I have taken a data class and this is simple to understand, not too technical.
September 3, 2025
“I needed a certain kind of person… Why were members of my team always on the verge of burning themselves out? You would have to order them to stop working on a project.” (Page 73)
“When the data remains complex, human psychology takes over. Or rather, it does the opposite: the human brain shies away from complexity. It becomes anxious and itchy and uncomfortable. And no one pays attention.” (Page 172)
To check out:
- WattTime (mapping pollution from every power plant on earth)
To read:
- the myths of the norsemen
Displaying 1 - 20 of 20 reviews