best practices

10 books on data, analytics, and AI that leaders should read in 2023

Somebody recently said to me, “Nobody reads books anymore. It’s all about articles and podcasts.”  “Sacrilege,” I say!  Data leaders must be continuous learners, so we need content in different media—books for longer form, timeless content and blogs and podcasts for this frenetic, fast-paced world. 

2023 marks our third annual round up of must-read books for data and analytics leaders. Two things I noticed about this year’s round up:

AI is not surprisingly a hot topic but so is business value and data monetization. Authors are writing more and faster than ever, with a few writing multiple books in a year. Kudos to them for sharing their knowledge (and please, tell me how you find the time!)

Once again, to cull this year’s list, I focused mainly on books published in the last two years with the themes of data, analytics, and AI. I scoured lists and reviews on Amazon, solicited ideas from social networks and got to reading. There are some books I enjoyed for their creativity, but time is not limitless so while I liked many, I am being ruthless about sticking to a list of 10 musts.

You can tune into The Data Chief podcast to hear from three of the authors on this year’s list, and check out last year's list to round out your reading list. So without further ado, here are the 10 must read books for data, analytics, and AI leaders in 2023, sorted alphabetically by title.

1. AI & Data Literacy: Empowering Citizens of Data Science by Bill Schmarzo

Bill is a repeat must-read book author and industry veteran as a triple-threat practitioner, thought leader, and professor.  In his latest book, he provides a wealth of information to approach through a critical lens in ways that few have fully addressed. These include various decision-making biases and questions to ask about the sources of data.  As we move from data literacy and fluency to AI literacy, he also explains the basics of predictive analytics and generative AI in terms that are digestible by all business users, not just data scientists. There is also good content on ethics and culture with companion discussion groups on Discord.  Get your copy here and tune into this episode of The Data Chief to hear from Bill on going from data-driven to value-driven.

2. The AI Factor: How to Apply Artificial Intelligence and Use Big Data to Grow Your Business Exponentially by Asha Saxena 

I love this book for both its clarity and timeliness.  Asha provides a useful framework for evaluating where to apply AI based on your company values and constraints. She includes best practices on data readiness, AI ethics, and numerous case studies to bring the concept to life.  Get your copy here and hear from Asha on The Data Chief podcast

3. Crimes Against Data: 101 true crime stories of people abusing and misusing data by Merrill Albert

The title of this book alone enticed me, and this book was just released as we were finalizing the 2023 must-read list. Crime stories are fun thrillers…except when it’s not fiction and a very real problem in our data world. As a digital economy increasingly powers our world, the degree that data is used to lie is something every person—leaders and practitioners alike—must battle against. It requires understanding the profound impact data distortions and outages have on a business, the economy, and life. Merrill interweaves personal experiences from bad data in names, addresses, travel as well as industry case studies that shows what happens when those capturing data have no idea how problematic their errors are. The crimes range from accidental to sloppy coding to intentional manipulation. Get your copy here.

4. Data is Everybody’s Business: The fundamentals of data monetization by  Barbara Wixom et al.

This book is a collaborative effort with MIT CISR, an organization that conducts research and seminars to further our industry. In a digital economy, indeed, data is everybody’s business, from corporations to startups to governments to citizens. At a time when everyone is trying to become data-driven and with board-level attention, the harsh reality is that few know where to start or what it truly takes to create a data culture. Barb and her co-authors address this head on with step by step plans that will resonate with business and technical people alike. The authors take a broader view of data monetization that selling data, but rather, on turning data from a cost to a value generator through insight and action. Their sage advice is supported by excellent case studies from a range of industries including Pepsi, HealthcareIQ, BBVA, and Microsoft. A quote to live by:  “As a basic business principle, organizations should generate more money from their data assets than they invest in producing and managing them.” Get your copy here and tune into this episode of The Data Chief to hear from Barb.

5. The Datapreneurs: The Promise of AI and the Creators Building Our Future by Bob Muglia and Steve Hamm 

Quite frankly, I prevaricated if this book is a must read or noteworthy. This book is both a delightful memoir and a source of inspiration on making bold bets when the path before us is not certain - very much needed in this new era of Generative AI and digital economy. Bob is a long-time industry veteran in the tech space, working in the early days of Microsoft and leading Snowflake in its first five years, and now serving on multiple boards shaping our industry. As I read this book, I found myself flashing back to where was I in my career when Bob was negotiating with SAP’s Hasso Platner or Microsoft’s Amir Netz of in memory compression and now Data Fabric fame; I felt the synchronicity in the intersections of people from our unique sides in the tech industry. Room for improvement: Bob’s profiles of techpreneurs are based on his experiences, so I could see where his writings are about all male leaders in the industry. But as the title is the Datapreneurs, I would have liked to see female founders here who are doing disruptive things in data observability and data governance for example. Get your copy here.

6. Embedded Analytics: Integrating Analysis with the Business Workflow, by Donald Farmer and Jim Horbury 

There are two reasons why I had to read this book and many more why it makes the top list:  

1. I’ve worked with Donald Farmer for about 20 years as a trusted advisor (we had such lively debates when he was at Qlik and I at BI Scorecard, then Gartner … and now the tables have turned as he critiques us at ThoughtSpot). He knows the industry and has a deep understanding of customer challenges and user personas. It was a delightful to surprise to learn his coauthor Jim Horbury is from renowned consultancy Interworks.  

2. Embedded analytics for both internal deployments (invisible BI) as well as customer facing applications is big business. Acumen Research estimates it is a $55B market. The authors provide a range of examples and recommendations on how to tier pricing—are you using embedded analytics to ensure customer loyalty or as a new revenue stream?  

Tip: The quality of the images in the print edition aren't as good as the digital version so I recommend picking up one of those copies instead, available here

7. From Data To Profit: How Businesses Leverage Data to Grow Their Top and Bottom Lines, by Vin Vashishta

At conferences throughout 2023 Gartner has shared how two thirds of data leaders struggle to deliver measurable ROI. As I used to teach an ROI class at TDWI 10 years ago, I’m disappointed that this is still the state of our industry. But it makes sense: many data leaders grew up in the industry from the tech side, not necessarily from the business or economics side. This is why Vin’s book is so critical right now. He brings his unique combination of skills and industry experiences to the table to help leaders quantify the value of their data and analytics investments. He provides a useful framework for sizing up readiness and opportunities. Compared to other books on this list, there were fewer checklists and conceptual diagrams. I would have also liked to see a broader cross section of companies for the case studies. Nevertheless, it makes this year’s top list. Quote to live by: “It’s not just the technology that must change, so we can’t ignore where the business and its people are today…When technology leads the way, monetization is elusive, and adoption rarely materializes.” Get your copy here and tune into this episode of The Data Chief to hear from Vin. 

8. Read, Write, Think Data:  A step-by-step guide to turning data into wisdom by Ben Jones  

At a time when data fluency has moved from a nice-to-have skill for data professionals to a must-have skill for citizens and business people alike, Ben’s resources are useful ingredients for your upskilling strategies. While other books might use the term data literacy as hype to talk about technical literacy, Ben nicely breaks down the steps and skills to go from data to “making decisions and enacting changes.” This book is presented as the third in a series, but Ben provides a sufficient recap from the first two, such that this stands on its own. “Understanding your data’s short comings” is one of my favorite chapters because far too often, business people assume data is the full truth, when in reality, it is only a version of the truth because of these shortcomings. Room for improvement: some of the concept visuals and data dashboards are difficult to read in the print edition. Note: Ben also just released Leading in the Age of Data that covers the people and process aspects of building a data fluent culture, still on my shelf to read. Get your copy here.

9. Working with AI: Real Stories of Human-Machine Collaboration by Tom Davenport and Steven Miller 

I sometimes forget that not everyone eats, drinks, sleeps data and AI and did not grow up in the industry with Professor Davenport’s groundbreaking book, “Competing on Analytics” back in 2007.  In other words, what Tom writes, I read. For business leaders newly learning the AI and analytics space, what I like most about this latest book is the breadth of case studies that offer inspiration for the myriad ways analytics and AI can be applied. Tom also released All In on AI in 2023 co-written with head of AI at Deloitte, Nitin Mittal, still on my shelf to read. Get your copy here and tune into this episode of The Data Chief to hear Tom’s predictions and resolutions for 2023.

10. Precisely: Working with Precision Systems in a World of Data by Zachary Tumin and Madeleine Want

With the authors coming from both the betting world (Fanatics) and higher education (Columbia University professor), the authors combine practical case studies and best practices for leveraging predictive analytics. I like that they cover both funding projects and organizing teams, both topics that few others in this year’s round up cover. Get your copy here.


While the following did not make the top 10 must read for data leaders, they are noteworthy in our industry and for data analysts:

ColorWise, by Kate Strachnyi. Kate gives back to the data and analytics industry in so many creative ways. This is her first book and it is a practical one in following best practices in using color when communicating data insights. This feels like a condensed version of other visualization experts such as Stephen Few and Cole Knussbaumer Knaflic  

Football Analytics with Python & R 1st Edition, Kindle Edition by Eric A. Eager &, Richard A. Erickson. What can I say, you know I love data and football! I shall be trying some of these exercises with my family, especially looking at Packers data! 

Where is Dr. Linda Hill’s book, Digital Transformation a Roadmap for Success? I heard Dr. Hill on an episode of Brené Brown’s podcast (my world’s collide) and kept thinking oh, my gosh, yes! She nails so much about why transformation and becoming data-driven is hard. She referenced an upcoming book, but I’ve yet to find it. Here is a link to this must-read series of papers.