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Big Data: A Revolution That Will Transform How We Live, Work, and Think Hardcover – March 5, 2013

4.2 out of 5 stars 379 customer reviews

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Editorial Reviews

Amazon.com Review

Amazon Exclusive: Q&A with Kenneth Cukier and Viktor Mayer-Schonberger

Kenneth CukierViktor Mayer-Schonberger

Q. What did it take to write Big Data?

A. Kenn has written about technology and business from Europe, Asia, and the US for The Economist, and is well-connected to the data community. Viktor had researched the information economy as a professor at Harvard and now at Oxford, and his book Delete had been well received. So we thought we had a good basis to make a contribution in the area. As we wrote the book, we had to dig deep to find unheard stories about big data pioneers and interview them. We wanted Big Data to be about a big idea, but also to be full of examples and success stories -- and be engrossing to read.

Q. Are you big data’s cheerleaders?

A. Absolutely not. We are the messengers of big data, not its evangelists. The big data age is happening, and in the book we take a look at the drivers, and big data’s likely trajectory: how it will change how we work and live. We emphasize that the fundamental shift is not in the machines that calculate data, but in the data itself and how we use it.

Q. In discovering big data applications, what was your biggest surprise?

A. It is tempting to say that it was predicting exploding manholes, tracking inflation in real time, or how big data saves the lives of premature babies. But the biggest surprise for us perhaps was the very diversity of the uses of big data, and how it already is changing people’s everyday world. Many people see big data through the lens of the Internet economy, since Google and Facebook have so much data. But that misses the point: big data is everywhere.

Q. Is Big Data then primarily a story about economic efficiency?

A. Big data improves economic efficiency, but that’s only a very small part of the story. We realized when talking to dozens and dozens of big data pioneers that it improves health care, advances better education, and helps predict societal change—from urban sprawl to the spread of the flu. Big data is roaring through all sectors of the economy and all areas of life.

Q. So big data offers only “upside”?

A. Not at all. We are very concerned about what we call in our book “the dark side of big data.” However the real challenge is that the problem is not necessarily where we initially tend to think it is, such as surveillance and privacy. After looking into the potential misuses of big data, we became much more troubled by “propensity” -- that is, big data predictions being used to police and punish. And by the “fetishization” of data that may occur, whereby organizations may blindly defer to what the data says without understanding its limitations.

Q. What can we do about this “dark side”?

A. Knowing about it is the first step. We thought hard to suggest concrete steps that can be taken to minimize and mitigate big data’s risk, and came up with a few ways to ensure transparency, guarantee human free will, and strike a better balance on privacy and the use of personal information. These are deeply serious issues. If we do not take action soon, it might be too late.

From Booklist

Academic Mayer-Schönberger and editor Cukier consider big data the new ability to crunch vast collections of information, analyze it instantly, and draw conclusions from it. Big data is about predictions: math applied to large quantities of data in order to infer probabilities. Because big data allows us to analyze far more data, we will move beyond expecting exactness and can no longer be fixated on causation. The authors state, The correlations may not tell us precisely why something is happening, but they alert us that it is happening. For individuals, big data risks an invasion of privacy, as vast amounts of personal data are collected and the potential exists to accuse a person of some possible future behavior that has not happened. The authors conclude that big data is a tool that doesn’t offer ultimate answers, just good-enough ones to help us now until better methods and hence better answers come along. This book offers important insights and information for many library patrons. --Mary Whaley

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Product Details

  • Hardcover: 256 pages
  • Publisher: Eamon Dolan/Houghton Mifflin Harcourt; 1 edition (March 5, 2013)
  • Language: English
  • ISBN-10: 0544002695
  • ISBN-13: 978-0544002692
  • Product Dimensions: 9.1 x 6.2 x 1.1 inches
  • Shipping Weight: 1 pounds
  • Average Customer Review: 4.2 out of 5 stars  See all reviews (379 customer reviews)
  • Amazon Best Sellers Rank: #200,413 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

By Loyd Eskildson HALL OF FAME on March 7, 2013
Format: Hardcover
The book opens by relating how Google, on its own initiative, devised a means to track the spread and intensity of flu prior to the 2009 flu season. Their methodology began by comparing the 50 million most common American search terms with CDC data on the spread of seasonal flu between 2003 and 2008. Google's software found a combination of search terms that, appropriately weighted, strongly correlated with official data. However, unlike the CDC, Google was able to make those assessments in real time, not a week or two later.

Oren Etzioni, frustrated to learn that many passengers booking a flight after he had, were able to pay less - contrary to conventional wisdom. He then 'scraped' information from a travel website from a 41-day period to forecast whether a price was a good deal or not, founding Farecast to offer this new ability. Etzioni next went on to improve the system by digesting data from a travel stie that covered most American commercial routes for a year - nearly 200 billion flight-price records. Before expanding to hotel rooms, concert tickets and used cars, Microsoft snapped up his firm ($110 million) and incorporated it into it Bing.

New processing technologies like open-source Hadoop allow managing far larger quantities of data. Hadoop uses a computational paradigm named MapReduce (by Google) to divide an application into many small fragments, each of which may be executed on any computer node in a cluster. Visa was able to reduce processing time for two years worth of data (73 billion transactions) from 1 month to 13 minutes using Hadoop.

The authors define 'big data' as things that can be done on a large scale that cannot be done on a smaller one, and see it as offering a major transformation.
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For anyone looking for a great primer on Big Data and the concerns that surround it, this is the book for you. I would highly recommend for business analysts and managers, including c-level execs. Mayer-Schonberger does a great job on identifying the key issues around Big Data and offering his opinion and insights on how we should move forward.

The only reason why I did not give this a five-star review is that the beginning starts off a bit slow and then the book hits it's stride about midway through. Truthfully, this would be a solid 4 1/2 star review, if Amazon allowed. If you can be patient through the first few chapters, you will not be disappointed. However, if you are completely new to the Big Data revolution, this books would make my top five list of must-reads to get your mind around the phenomenon. The first few chapters do a great job setting the stage.

To the initiated in Big Data, there are some fantastic arguments and well thought out opinions on how the industry should proceed as a whole. Frankly, I know I am wiser and have a more rounded understanding after reading. Should make any Big Data person's bookshelf.

(Just in case the author ever reads this review - I appreciate that you wrote this book for a broad audience. I would love to read some material written by you that is more focused on the issues surrounding Big Data. As an example, I think you could do a great job of writing a book simply on the ethics of data and our responsibilities as data stewards)
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Format: Hardcover Vine Customer Review of Free Product ( What's this? )
Various popular books have been written about number crunchers, analytics and data mining; most of the popular works which cannot adequately explain the mathematics of machine learning and data mining cite various examples of firms such as Google and financial powerhouses that have achieved success through these methods. While this excellent popularization certainly cites many examples of successful exploitation of these computational methods--this popular exposition does more. It reveals trends such as the completeness of data (as opposed to sampling), the ability to accept less than perfect accuracy (signals and data) when there is a profundity of data and large "sample populations", the ability to "data-ify" (quantify and digitize) various kinds of information that were previously only subject to vague summarization, the ability to use new databases (like Hadoop and No-Sql) and statistical tools (machine learning and data mining) to describe huge quantities of data that could not be analyzed through traditional methods.

Other popularizations up until now only revealed the general flavor of analytics becoming available and applicable through data mining and machine learning. This excellent summarization reveals trends that might otherwise be hidden by the forest of numerical and computational methods and will even be valuable in its observations to expert practitioners caught up in the details of computation.

--Ira Laefsky MSE (Computer Science)/MBA formerly on the Senior Consulting Staff of Arthur D. Little, Inc. and Digital Equipment Corporation
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Format: Hardcover Vine Customer Review of Free Product ( What's this? )
First, I agree with the authors' premise that "Big Data" is going to transform the world in ways we don't even understand yet. The book did a decent job of explaining "Big Data" and gave a few examples. What left me somewhat disappointed was a paucity of real examples and constant repetition. It was as if the book was actually an expansion of a long magazine article--forcing the authors to repeat much of the same content over and over to provide enough filler for a book. There were some very intriguing examples of real world applications and suggestions of future uses of data. However, the book would have been better with more real world examples. Now maybe we're a little early into this and there are fewer publicly available examples. Regardless, the book promised a lot and then didn't deliver to the level I expected. I'm still glad to have read the book and gotten a better understanding of "Big Data" as I believe the authors are right about the changes it is bringing--some good, and some not so good.
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