It’s that time of year - back to school, back to books, and our annual must-read books for data and analytics leaders. Given the pace of change in our industry, continuous learning is a must, whether through networking, podcasting, or reading.
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.
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.
The Data Detective: Ten Easy Rules to Make Sense of Statistics by Tim Harford
I love this book. The author could have just as easily made the subtitle Ten Easy Rules to Make Sense of Data and in fact in Europe, the book goes by a different title: How to Make the World Add Up. This book should be part of every company’s data literacy program and ideally one that you introduce to your family and friends. Harford reveals how data is used and abused by business people and politicians and takes the concept of “vanity metrics” to new heights when an economist faces death threats because a government does not like the data.I’d like to see his 10 rules as a postcard for reminders in interpreting and using data. Get your copy.
Data Mesh: Delivering Data-Driven Value at Scale by Zhamak Dehghani
This book was in pre-release for last year’s book list and I predicted then that it would be as important as the original Inmon and Kimball books. This is proving out based on 2022 trend notes and the caliber of customers evolving towards a data mesh operating principle such as Capital One and Roche. Dehghani cuts through the hype and lays the foundation for why so many complicated design choices have been made by early technical limitations, leading to a patchwork of data silos. She calls for a change in thinking, technical design, and ownership models to leverage new technologies and agile ways of working. As the data mesh is not a single technology you can buy, we are seeing some data mesh washing in the industry as well as those who fear this change. Regardless of the strategy your company employs, the book is a must read for leaders and practitioners alike. Get your copy.
Data for All by John Thompson
Thompson is the author of How to Organize Your Data teams so when I heard he had another book in the works, I asked for a preview. It is the quintessential book for the digital generation, recognizing how society is at a cross roads in who owns data and who creates data. John offers practical advice on how we can restore the balance in power between companies who exploit our data and us as individual owners of our data. With masterful storytelling, I hope this book will become required reading in high schools and business schools. Preview here.
Data Means Business: Level up your organisation to adapt, evolve, and scale in an ever-changing world by Jason Foster and Barry Green
I discovered this book while attending Big Data London in 2021, the industry’s first conference post pandemic and met with author Jason Foster. Foster is the founder of Cynozure, a data consultancy, with repeat appearances on DataIQs top influencers list. This is a practical book most useful to those crafting a data strategy. This book helps data leaders focus on business outcomes, assess why self-service, and how to organize for better collaboration. It is also suitable for business people wanting to have a more productive conversation with data people. Get your copy.
Data Governance: The Definitive Guide: People, Processes, and Tools to Operationalize Data Trustworthiness 1st Edition, by by Evren Eryurek (Author), Uri Gilad (Author), Valliappa Lakshmanan (Author), Anita Kibunguchy-Grant (Author), Jessi Ashdown (Author)
Data governance has become a dirty word with business stakeholders, code for “no.” Overly restrictive governance rules prevent collaboration and innovation. It is sometimes only after a breach that organizations get more serious about governance and recognize it’s not just IT or the data team’s job, governance is everyone’s responsibility. While many books cover the modeling and securing of data, this one covers the people side of who is responsible for data at different stages in its lifecycle from capture to analytics. The authors share different approaches depending on the company size and industry. Get your copy.
Don't Trust Your Gut: Using Data to Get What You Really Want in Life by Seth Stephens-Davidowitz
You know your stakeholder who is skeptical of the data and thinks their intuition is better than the data? This is the book to read to help win them over. Thank you Brian Gillit of dbt labs for the recommendation. Get your copy.
Effective Storytelling with Data: How to Drive Change with Data, Narrative and Visuals by Brent Dykes
Whether it’s an economist educating politicians on the impact of war scenarios as Daniel Kahneman once did, or an analyst trying to explain data as part of a product launch, data alone does not lead to insights. There must be context, effective presentation techniques, and storytelling. Dykes uses his experience as an analyst, vendor, and professor to distill the best practices in a beautiful, well-written book. Dykes hooks you in the beginning with his own storytelling technique, “Many factors contributed to the demise of my insight: my poor delivery, the executive’s closed-mindedness, and cultural inertia.” Get your copy.
Fundamentals of Data Engineering: Plan and Build Robust Data Systems 1st Edition by Joe Reis and Matt Housley
At a conference in fall 2019, when speaking about how to organize the data team, I replaced the ETL developer with a data engineer. People asked me how this role was different, what tools did they use, and why didn’t it belong in IT. This book provides a much better answer than I could give that cold day (“a data engineer manages the data engineering lifecycle, beginning with getting data from source systems and ending with serving data for use cases, such as analysis and machine learning.”) I wrestled with whether this more practitioner-oriented book belonged on this list for leaders, but it is precisely because of the importance of this role why leaders should be versed in the content. There is a good balance between specific technologies and conceptual approaches and I like the way the authors include what they call “undercurrents” such as security, data ops, and data architecture. I also like that this book covers emerging concepts such as data mesh and data observability and tools of the trade in a vendor-agnostic way. My one nit is that they use the term OLAP to apply to analytical cloud data platforms, but I see the term as too synonymous with old school cubing technology. Get your copy.
Radically Human: How New Technology Is Transforming Business and Shaping Our Future Hardcover – April 26, 2022 by Paul Daugherty and H. James Wilson
As AI has been gaining adoption, many feared AI would replace humans in decision-making. This book has a more balanced approach recognizing how human plus AI is not only necessary, but in reality, the more powerful combination. While some AI books focus on AI alone, Daugherty and Wilson address how AI can only begin with data and provide a useful framework (Intelligence, Data, Expertise, Architecture, and Strategy (IDEAS) for organizations to craft a comprehensive strategy in which AI and data is a forethought, not an afterthought in business operations. In this regard, the book is well researched, reinforcing that there is a winner-take-all in a digital world, but offers hope for analytics laggards to catch up and leapfrog others. Written by thought leaders from Accenture, this book is packed with case studies and anecdotes across industries as proof points and inspiration. Get your copy.
Real World AI: A Practical Guide for Responsible Machine Learning by Alyssa Simpson Rochwerger and Wilson Pang
Data and AI can be used for good or for evil. The authors bring their expertise as vendors and in working as practitioners to ensure AI is deployed responsibly. Many AI books start with the assumption of clean and complete data. This book hightlights why this is a problem with practical ways to address it, covering everything from organizational design, the right business problem, and scaling the solution. Get your copy.
Other noteworthy mentions:
Although these books are not specifically data related, you’ll find them useful in addressing collaboration and culture:
Getting to "Yes And": The Art of Business Improv, by Bob Kulhan (Author), Chuck Crisafulli Recommended by Heid Lanford, CDO of Fitch
ReCulturing: Design Your Company Culture to Connect with Strategy and Purpose for Lasting Success by Melissa Daimler. Because culture remains the top-cited barrier to being data driven. Daimler is the Chief Learning Officer and Udemy.
Remote Not Distant: Design a Company Culture That Will Help You Thrive in a Hybrid Workplace Hardcover by Gustavo Razzetti, because every company and every data and analytics leader is trying to figure out how to retain talent and instill culture in a new work-from- anywhere world.
And for those who also enjoy a left brain read, my neighborhood book club has been going strong for 20 years now. These monthly reads make up most of my leisure time reading. Some of my favorites from the last year: Next Year in Havana, The Secret Keeper of Jaipur, and Great Circle.
Happy reading!
