The saying “knowledge is power” has never been truer, thanks to the widespread commercial use of big data and data analytics. This trend has been brought about by the new demands of the modern marketplace, and it’s here to stay. Both small and big companies are seeking the best ways to leverage their data into a competitive advantage. With that in mind, we have prepared a top-10 list of data analytics and big data books, along with magazines or authentified readers’ reviews upvoted by the Amazon or Goodreads communities. Whether you are a complete beginner or a seasoned business intelligence professional, you will find here some books on data analytics that will help you to grow in your understanding of the field. And with that understanding, you’ll be able to tap into the potential of data analytics to create strategic advantages, exploit your metrics to shape them into stunning business dashboards, and identify new business opportunities or at least participate in the process.
The Best Big Data & Data Analytics Books Of All Time
1) Data Analytics Made Accessible, by A. Maheshwari
Best for: the new intern who has no idea what data science even means
Example of a rave review:
“I would definitely recommend this book to everyone interested in learning about Data Analytics from scratch and would say it is the best resource available among all other Data Analytics books.” —reader’s review
If we had to pick one book for an absolute newbie to the field of Data Science to read, it would be this one. Updated for 2017, “Data Analytics Made Accessible” is one of the best books on data analytics, and does exactly what its name implies: it exaplains data analytics in an easy way, and makes them understandable and digestible for the uninitiated.
The book promotes easy understanding through:
- Concrete, real world examples at the beginning of each chapter
- An intuitively organized layout structured like a one semester college course
- Case studies throughout each chapter to tie the material together
Due to its scope of content and clear explanation, “Data Analytics Made Accessible” has been made a college textbook for many universities in the US and worldwide. The author, Anil Maheshwari, Ph.D., has both practical and intellectual knowledge of data analytics, as he worked in data science at IBM for 9 years before becoming a professor.
The book also has some “crowdsourced” material, as the 2017 edition had 4 chapters added based on feedback from reviewers and readers. At 156 pages on Kindle, this is a book you could finish in one (long) sitting if you were so inclined, that you can also use as an inspiration when you work with your business intelligence software.
2) Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by E. Siegel
Best for: someone who has heard a lot of buzz about predictive analytics, but doesn’t have a firm grasp of the subject
An example of a rave review:
“The Freakonomics of big data.”
—Stein Kretsinger, founding executive, Advertising.com
We have included predictive analytics in our list of the most prominent business intelligence trends for 2017 as it has been widely recognized as the strategy that makes it possible to unleash the power of big data. From a business perspective, predictive analytics is used to analyze current data and historical facts in order to better understand customers, products and competitors and to identify potential risks and opportunities for a company.
However, due to its vast applications, predictive analytics should not concern only business professionals. Most people are aware that companies collect our GPS locale, text messages, credit card purchases, social media posts, Google search history etc. and this book will give you the insight into their data collecting procedures and the reasons behind them.
Eric Siegel’s data analytics book is an eye-opening read for anyone who wants to learn what predictive analytics is, and how predictive analytics may be deployed across a wide range of disciplines. It is not a manual, so a data scientist looking for instructions would be disappointed. Although there is some discussion of algorithms including linear regression or decision trees, it’s easy to understand even for a lay person.
Siegel’s book makes it clear that predictive analytics is not a sneaky procedure used by companies to sell more, but a significant leap in technology which by predicting human behavior can help combat financial risk, improve health care, reduce spam, toughens crime-fighting and yes, boost sales. It was lately revised and updated in January 2016.
3) Too Big to Ignore: The Business Case for Big Data, by award-winning author P. Simon
Best for: the member of your management team who rolls their eyes whenever big data or predictive analytics are brought up
Example of a rave review:
“Simon provides a very thorough exploration for non-technologists into the new world of “Big Data” with many illustrations of how companies are beginning to exploit this resource to their advantage.” —reader’s review
There are two types of people who should read this book: people who don’t believe in the merits of big data and predictive analytics, and people who are so interested in these topics that they love learning about current use cases of these technologies —and this is what makes it one of the best big data books.
“Too Big To Ignore” examines many examples of how companies (and local governments!) are using big data to their advantage, including:
- Progressive Insurance’s use of GPS trackers / accelerometers which determine customer safety ratings
- Google’s ability to predict local flu outbreaks by measuring spikes in flu related local searches
- The government of Boston fixing potholes using data from residents enter into smartphones
The author, Phil Simon, in an expert at making technical information simple, as he is a speaker who has made keynotes at companies like EA, Cisco, Zappos, and Netflix. Simon makes the case that big data is not only an area of potential innovation- it’s a crucial factor your company must address now to survive in the modern marketplace. His argument contains urgency and clarity, centering around this point: big data is no fad. It’s a huge change in how business is conducted, and it’s already happening.
Remarkably free of jargon and filled with case studies and examples, “Too Big To ignore” is an excellent introduction to big data, as seen through the lens of: what can big data do for me and my business?
4) Lean Analytics: Use Data to Build a Better Startup Faster, by A. Croll and B. Yoskovitz
Best for: Anyone at your company who wants to deeply understand your customers through the use of data analytics
Examples of rave reviews:
“As useful for today’s multi-billion dollar companies as it is for entrepreneurs.”
— John Stormer, Salesforce.com
“Your competition will use this book to outgrow you.”
— Mike Volpe, Hubspot
Eric Reis started a global movement by releasing the book “The Lean Startup”. The philosophy of the book revolved around getting feedback from customers as quickly as possible and iterating rapidly based on that feedback. It was only a matter of time before the “lean philosophy” was applied to data analytics.
However, don’t be deceived – just as you don’t need to be a literal startup to gain a lot of value from Eric Ries’ book, companies of all sizes and shapes can learn a lot of valuable information from “Lean Analytics”. The book has three main ideas:
- The biggest risk your company faces is investing a lot of time and resources into building something that the market doesn’t want.
- Product / market fit is THE most important factor to get right.
- By using the right analytics metrics, you can determine what products or services to focus on or build – and how to market them.
In today’s world, every company faces the potential to be disrupted. It’s up to you: do you want to disrupt your own company from the inside by being an intrapreneur, or are you going to let someone else disrupt you in the market?
Reading this book will give you the toolkit you need to make sure the former happens and not the latter.
5) Data Smart: Using Data Science to Transform Information into Insight, by J. W. Foreman
Best for: a somewhat technical reader who is good with Excel, but doesn’t know much about data science
Example of a rave review:
“What I like most about the book is that it doesn’t try to wave a magic data wand to cure all of your company’s ills. Instead it focuses on a few areas where data and analytic techniques can deliver a concrete benefit, and gives you just enough to get started.” —reader’s review
‘Data Smart’ contains concrete hints on which analytic techniques to apply to effectively crunch data. It’s a useful read for anyone with a little background in applied mathematics and a spreadsheet program on their PC. It is a well thought out and designed tutorial with many easy to understand real world examples for a business professional that must work with data sets.
Each chapter covers a different technique in a spreadsheet, including non–linear programming and genetic algorithms, clustering, graph modularity, data mining in graphs, supervised AI through logistic regression, ensemble models, forecasting, seasonal adjustments, and prediction intervals through Monte Carlo simulation as well as moving from spreadsheets into the R programming language.
‘Data Smart’ contains enough practical information to actually start performing analyses using good old Microsoft Excel. Its goal isn’t to revolutionize your business with additional software, but rather to make incremental improvements to processes with accessible analytic techniques. However, once you start working with larger enterprise level data sets with millions of rows and hundreds of columns of information, Excel may not be capable of handling such volumes. At this point, turning to self-service business intelligence would be the most affordable and effective solution.
6) Big Data: A Revolution That Will Transform How We Live, Work, and Think by V. Mayer-Schönberger and K. Cukier
Best for: the reader interested how big data can improve the quality of our lives (and not just in a business sense)
Example of a rave review:
“An optimistic and practical look at the Big Data revolution — just the thing to get your head around the big changes already underway and the bigger changes to come.” —Cory Doctorow, boingboing.com
This is another big data book that provides readers with a more general view on key issues around Big Data, with the authors offering their opinions and insights on how the technology will proceed. This would be a perfect read for people new to the subject who want to understand in what way big data can be leveraged to improve people’s life quality – from identifying consumers’ shopping patterns to predicting flu outbreaks.
The book also sheds light on how big data’s key characteristics (volume, variety, velocity and veracity) will change the way we process and manage data. It mentions the completeness of data (as opposed to sampling), the power to quantify and digitize new formats of information that were previously inaccessible, as well as the ability to use new databases (like Hadoop and NoSQL) and statistical tools (machine learning and data mining) to describe huge quantities of data.
7) Business UnIntelligence: Insight and Innovation Beyond Analytics and Big Data, by B. Devlin
Best for: the seasoned business intelligence professional who is ready to think deep and hard about important issues in data analytics and big data
Example of a rave review:
“…a tour de force of the data warehouse and business intelligence landscape. It drills into every nook and cranny of the industry, the great successes as well as the depths of insanity (and there’s plenty of both revealed). This book details what the true ‘Father of Data Warehousing’ thinks of his children and it’s not always pretty…” —reader’s review
This book is most useful for someone who lives and breathes BI – and who is ready to critically look at their ideas surrounding the field. In this at-times contrarian and unflinching book, Dr. Barry Devlin shows how modern BI often fails to deal with data from mobile, social media, and the Internet of Things in a meaningful way. Devlin also makes the argument that modern business decisions must be made from a combination of data-driven (rational) and emotional (intuitive) sources, as opposed to only using data -and that business intelligence must reflect those needs.
The book additionally serves as a history of the field of business intelligence, big data, and data analytics, as Devlin details the past, present, and future of the field. He does so in order to challenge many of the assumptions in modern data analytics and data gathering, by showing how quickly the old best practices have become outdated due to the sheer volume and velocity of modern data sources.
If you’re ready to be challenged to think differently, “Business unIntelligence” is amongst the best data analytics books to do so.
8) Big Data at Work: Dispelling the Myths, Uncovering the Opportunities, by T. H. Davenport
Best for: managers who want to start and manage the big data journey in both small and large organizations
Example of a rave review:
“It’s a required reading for managers that need a straightforward, hype-free introduction to big data, a clear and clarifying “signal” in the incredible noise around the confusing and mislabeled term.” — Forbes
With tips on how to develop a strategy and a plan of action regarding big data, what technology you need to embrace it and how to hire the right kinds of people to crunch big data, this book is clearly manager-oriented.
It also offers an overview of big data technologies, explains what is needed to succeed with big data, and gives examples of both successful and failed data practices undertaken by startups, online firms, and large companies. The author also introduces the concept of “analytics 3.0” to describe how companies can combine traditional analytics with the big data approach. He recognizes big online companies like Google or Facebook as the originators of best big data tools and technologies as well as data-driven management practices.
‘Big Data at Work’ is a pleasant read, however this approachability may be a merit for some readers and a flaw for others. Critics point out that the book offers rather a breezy approach to the subject as it refrains from using technical language, thus it avoids answering some of rudimentary questions.
9) Analytics in a Big Data World: The Essential Guide to Data Science and its Applications, by B. Baesens
Best for: business data analysts, consultants and graduate students in business analytics
Example of a rave review:
“In a domain overwhelmed with hype and hyperboles, ‘Analytics in a Big Data World’ provides no-nonsense, focused coverage on specifics and implementation best practices.” —reader’s review
This is a real data analytics manual that would suit readers who already have basic knowledge in data mining and business intelligence and are looking for structural and technical instructions on how to conduct big data analytics in real world business management.
With a very strong practical focus “Analytics in a Big Data World” starts with providing the readers with the basic nomenclature, the analytics process model, and its relation to other relevant disciplines, such as e.g. statistics, machine learning, and artificial intelligence. Then the author proceeds with highlighting the most important steps of the process model, such as sampling, treatment of missing values, and variable selection. The subsequent chapters focus on predictive and descriptive analytics.
Additionally, numerous case studies on risk management, fraud detection, customer relationship management, and web analytics are included and described in detail. In the seventh chapter the author provides us with concrete instructions on which tools and practices to use to put analytics to work. Topics covered here range from backtesting and benchmarking approaches to data quality issues, software tools, and model documentation practices.
Designed to be an accessible resource, this essential big data book does not include exhaustive coverage of all analytical techniques. Instead it highlights data analytics techniques that really provide added value in business environments.
10) Data Science For Business: What You Need to Know About Data Mining & Data-Analytic Thinking, by F. Provost & T. Fawcett
Best for: someone who has read a few intro books on data science and is ready to challenge themselves and dive deeper
Example of a rave review:
“The book strikes a satisfyingly good balance between technical fundamentals and business applications: just enough numbers and technical details for a solid foundation, complemented with numerous business cases and examples to see how the tech stuffs fall into place.” —reader’s review
Many books about data analytics and big data focus on the “how” of data science – the techniques and mechanisms. “Data Science for Business” does that as well, but also goes into the “why” of data science and provides insight into some useful ways to think about data science in a business setting.
The book reviews some underlying principles of data analytics, and is a great read for an aspiring data-driven decision maker who wants to intelligently participate in using big data and analytics to improve their company’s strategic and tactical choices.
Finally, “Data Science for Business” goes into just enough detail explaining the data mining techniques used today, using plenty of scientific thinking without overwhelming the reader with numbers and equations. This is facilitated by the use of technical sections which the reader can choose to skip or devour according to their interest.
If you found our list of the best data analytics and big data books useful, but your hunger for knowledge hasn’t been satisfied yet, take a look at our top 8 books to get you off the ground with business intelligence or our top 12 books on data visualization to keep growing in your understanding of data science.