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Big Data: A Revolution That Will Transform How We Live, Work, and Think Paperback – March 4, 2014

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Editorial Reviews 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.

--This text refers to an out of print or unavailable edition of this title.

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 --This text refers to an out of print or unavailable edition of this title.

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Product Details

  • Paperback: 272 pages
  • Publisher: Eamon Dolan/Mariner Books; Reprint edition (March 4, 2014)
  • Language: English
  • ISBN-10: 0544227751
  • ISBN-13: 978-0544227750
  • Product Dimensions: 5.3 x 0.8 x 8 inches
  • Shipping Weight: 5.6 ounces (View shipping rates and policies)
  • Average Customer Review: 4.2 out of 5 stars  See all reviews (355 customer reviews)
  • Amazon Best Sellers Rank: #35,287 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

Format: Hardcover Vine Customer Review of Free Product ( What's this? )
The precise definition of what constitutes big data does not exist, it is a term used to refer to the capture of enormous amounts of different types of data that often seems to be unrelated. Yet, that imprecise definition is part of the strength of using big data to make better decisions.
In the days when only small samples could be taken for analysis due to the cost, it was critical that everything be done right, the items in the sample must be randomly chosen and care had to be taken to eliminate any extreme outliers that would skew the result. This also meant that the models had to be very well constructed, for if the model was not applicable, the final results could be worthless or even have negative consequences.
The concept of big data basically means that all the data is examined to look for common characteristics. Outliers are included and are of less significance for they will be drowned out by the enormous number of data points in the middle. One of the examples of the use of big data is the prediction of high fevers in infants. Rather than developing a model for the events that would include many assumptions, not all of which are correct, the immediate history of the children that develop high fevers is examined. All of the vital signs and other data collected about the infants are then examined to determine if there are any common indicators that could be used as predictors. The data analysts are not trying to establish causality, only traits present before the events.
Doing this means that only the data matters, emotion and experience are almost insignificant. The authors describe many examples of where big data has been used to predict and prioritize; one of the most interesting examples is the development of translation software.
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169 of 193 people found the following review helpful By George Bush 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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49 of 62 people found the following review helpful By Ira Laefsky VINE VOICE on January 31, 2013
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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