The saying “knowledge is power” has never been more relevant, 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. The rate at which data is generated has increased exponentially in recent years. To put this into perspective, 40,000 search queries are performed per second via Google alone - this equates to 3.46 million searches per day and 1.2 trillion each year.

Companies, both big and small, are seeking the best ways to leverage their data into a competitive advantage. With that in mind, we have prepared a list of top 15 definitive data analytics and big data books, along with magazines and authentic readers’ reviews upvoted by the Goodreads community. Whether you are a complete novice or a seasoned business intelligence professional, you will find here some books on data analytics that will help you cultivate your understanding of this essential 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.

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Before we delve any deeper, here are three big data analytics insights to put its relevance and importance into perspective.

Essential Big Data And Data Analytics Insights

In a mere five years from now, the number of smart connected devices on the planet will be more than 50 billion - all of which will generate data that can be shared, collected, and analyzed.

The White House has invested an incredible $200 million in big data projects - a true testament to the growing importance and relevance of big data analytics across sectors.

As of this moment, just 5% of all accessible data is analyzed and used - just think of the potential.

The Best Data Analytics And Big Data 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

An excerpt from 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.”

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 2018, “Bussiness Intelligence and Data Mining Made Accessible” is inarguably the best book there is on data analytics, and does exactly what its name implies: it explains data analytics in an easy way, and makes it 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 semester-long college course

Case studies in 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; 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, and that you can also use as an inspiration when you work on your business intelligence strategy.

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 on the subject

An excerpt from 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, 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 application, 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 an 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 can 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 layman.

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, toughen 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

An excerpt from 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.”

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 the 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 that residents enter into their smartphones

The author, Phil Simon, being a speaker who has made keynotes at EA, Cisco, Zappos, and Netflix, is an expert at making technical information simple. Simon makes the case that big data is not only an area of potential innovation- it’s a crucial factor that 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 in your company who wants to deeply understand your customers through the use of data analytics

Excerpts from 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 which 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

An excerpt from 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.”

‘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 who must work with data sets.

Each chapter covers a different technique in a spreadsheet, including nonlinear 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 by 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 in how big data can improve the quality of our lives (and not just in a business sense)

An excerpt from 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.

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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

An excerpt from 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…”

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

An excerpt from 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 a 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 reporting and best 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 the 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

An excerpt from a rave review:

“In a domain overwhelmed with hype and hyperboles, ‘Analytics in a Big Data World’ provides a no-nonsense, focused coverage on specifics and implementation best practices.”

This is a real data analytics manual that would suit readers who already have the basic knowledge of 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 statistics, machine learning, and artificial intelligence. The author then 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 business analytics 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 an 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

An excerpt from 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.”

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.

11) Numsense! Data Science for the Layman: No Math Added by Annalyn Ng & Kenneth Soo

Best for: Any lay person with no prior background in math or analytics, who wants to work in this field or to manage other data scientists

An excerpt from a rave review:

“Numsense! Data Science for the Layman is a great little book. Not only could it be a fine introduction for someone with little if any knowledge of data science, but it also provides nice summaries of several different areas for those with familiarity. Five stars for doing what the title says."

For big data books geared toward the practical application of digital insights, Numsense! is one of the best on the market. Not only does this digestible guide speak to the reader in a clear, decipherable language, but it is also rich in actionable tips in areas including A/B testing, social network analysis, regression analytics, clustering, and more.

Boasting inspiring real-world examples and a comprehensive glossary of terms, this data analysis book is a must-read for anyone looking to embark on a lifelong journey toward analytical enlightenment.

12) Hacking Growth: How Today’s Fastest-Growing Companies Drive Breakout Success by Sean Ellis & Morgan Brown

Best for: a budding startup entrepreneur looking to grow and evolve their empire by leveraging the power of big data

An excerpt from a rave review:

“Must read book for anyone interested in the subject matter. The author(s) lay out a very thorough yet concise picture of what growth hacking involves and a step by step method on how to do it. They convincingly show that growth hacking methods or mindset can and should apply for you whether you work for a startup or a large company.”

Growth hacking is a relatively new phenomenon, bestowing the term of using key insights, data, and digital strategies to connect with your target audience on a more meaningful, more personal level. And if executed the right way, it works.

Of all the growth hacking-themed books available today, this is the most inspiring, the most understandable and ultimately, the most rewarding. Not only will you gain tangible insight into how brands like Airbnb and Pinterest became global sensations, but you'll also gain access to a toolkit for growth hacking based on informed data-driven decisions.

13) Data-Driven HR: How to Use Analytics and Metrics to Drive Performance by Bernard Marr

Best for: business leaders, executives, and HR directors looking to improve their business practices through real-world big data analytics

An excerpt from a rave review:

“A practical, inspirational guide for human resources (HR) professionals, this book sets out to show how data collected by a company can be utilised to improve the HR function and, in turn, the company-as-a-whole. It is a lot more than just storing HR information on a computer, yet many professionals have yet to see the utility and potential of data-driven HR. A book like this can change that!"

In today’s world, data serves to enhance the productivity, output, and efficiency of all sectors, disciplines and departments - and human resources is no exception.

For the HR professional looking to establish detailed HR KPIs, leverage the value of digital metrics and insights to improve areas such as training & development, data protection, staff management, and organizational efficiency, this is one of the best books on digital data you could ever read.

Crammed with practical insights and easy-to-follow case studies, this HR-based big data bible will serve as an invaluable reference in your quest for human resources perfection.

14) Creating Value With Social Media Analytics: Managing, Aligning, and Mining Social Media Text, Networks, Actions, Location, Apps, Hyperlinks, Multimedia, & Search Engines Data by Gohar F. Khan

Best for: any individual looking to get under the skin of data-based insights and metrics through renowned social media platforms

An excerpt from a rave review:

“"Gohar Khan is a pioneer in the emerging domain of social media analytics. This latest text is a must-read for business leaders, managers, and academicians, as it provides a clear and concise understanding of business value creation through social media data from a social lens." -Laeeq Khan, Director, Social Media Analytics Research Team, Ohio University.

If you have a solid working grasp on the functionality of the world's most prominent social media platforms and digital marketing KPIs, but you'd like to squeeze more value from each channel, this big data book is a must-read.

Not only is the author’s knowledge on the subject vast and deeply impressive, but it is also presented in such a way that budding data scientists, digital marketers, social media executives, and business leaders can extract priceless nuggets of information with ease.

By using big data analytics to refine and drive your social media strategy, you stand to set yourself apart from the competition - and this big data book will help you do just that.

15) Analytic Philosophy: A Very Short Introduction by Michael Beaney

Best for: individuals looking to understand the history, origins, and core philosophies of the analytical, data-driven mindset

An excerpt from a rave review:

"A concise, delightfully accessible, and intellectually stimulating introduction to philosophy in the analytic tradition, especially its formative phase." - Erich Reck, Professor, University of California at Riverside

One of the most prolific data analysis books in existence, this insightful, informative, and refreshing work of prose serves as the ideal supplement to the more practical books and toolkits on our list.

Digging deep into the very ideation of the subject and the premise behind analytic thinking, this book defines precisely why big data analytics is so valuable while offering digestible concepts that will serve as the very foundations of everything you do with the digital insights available to you. A real must-read for anyone with a thirst for big data enlightenment.

"The most valuable commodity I know of is information." - Gordon Gekko, Wall Street

Exclusive Bonus Content: Download our summarized, free guide on top 15 best books on big data! Not Sure Which Big Data Book To Read? Download our summarized, free guide on top 15 best books on big data!

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 best business intelligence books or our data visualization books post to keep growing in your understanding of data science. And if you’d like to put your newfound knowledge of big data analytics into practice, explore our online dashboard tool.

So, what are the best big data books? Here is a summary:

Data Analytics Made Accessible, by A. Maheshwari Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by E. Siegel Too Big to Ignore: The Business Case for Big Data, by award-winning author P. Simon Lean Analytics: Use Data to Build a Better Startup Faster, by A. Croll and B. Yoskovitz Data Smart: Using Data Science to Transform Information into Insight, by J. W. Foreman Big Data: A Revolution That Will Transform How We Live, Work, and Think by V. Mayer-Schönberger and K. Cukier Business UnIntelligence: Insight and Innovation Beyond Analytics and Big Data, by B. Devlin Big Data at Work: Dispelling the Myths, Uncovering the Opportunities, by T. H. Davenport Analytics in a Big Data World: The Essential Guide to Data Science and its Applications, by B. Baesens Data Science For Business: What You Need to Know About Data Mining & Data-Analytic Thinking, by F. Provost & T. Fawcett Numsense! Data Science for the Layman: No Math Added by Annalyn Ng & Kenneth Soo Hacking Growth: How Today’s Fastest-Growing Companies Drive Breakout Success by Sean Ellis & Morgan Brown Data-Driven HR: How to Use Analytics and Metrics to Drive Performance by Bernard Marr Creating Value With Social Media Analytics: Managing, Aligning, and Mining Social Media Text, Networks, Actions, Location, Apps, Hyperlinks, Multimedia, & Search Engines Data by Gohar F. Khan Analytic Philosophy: A Very Short Introduction by Michael Beaney

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