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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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Amazon Exclusive: Q&A with Kenneth Cukier and Viktor 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.
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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Top Customer Reviews
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)
But how does one choose a sample? Some argued that purposefully constructing a sample that was representative of the whole would be most the suitable way forward. But in 1934, Jerzy Neyman, a Polish statistician, forcefully showed that such an approach leads to huge errors. The key to avoid them is to aim for randomness in choosing whom to sample. Statisticians have shown that sampling precision improves most dramatically with randomness, not with increased sample size.
Today a third of all of Amazon's sales are said to result from its recommendation and personalization systems. With these systems, Amazon has driven many competitors out of business: not only large bookstores and music stores, but also local booksellers who thought their personal touch would insulate them from the winds of change.
Will a world of predictions dampen our enthusiasm to greet the sunrise, our desire to put our own human imprint on the world? The opposite is actually more likely. Knowing how actions may play out in the future will allow us to take remedial steps to prevent problems or improve outcomes. We will spot students who are starting to slip long before the final exam. We will detect tiny cancers and treat them before the full-blown disease has a chance to emerge. We will see the liklihood on unwanted teenage pregnancy or a life of crime and intervene to change, as much as we can, that predicted outcome. We will prevent deadly fires from consuming overcrowded New York tenements by knowing which building to inspect first."
1. Sampling was important when collecting data was expensive and difficult, but we now we have access by one means or another to all data.
2. Since we have so much data, the quality of individual data points is not important and we can allow inexactness in measurement processes as long as there isn't a systematic bias.
3. Causality and understanding why things happen is no longer as important as correlation and discovering the correct strategies or course of action based upon large bodies of data.
All of these points could have been made in a hundred pages I think, and reading just the first half of the book would give a reader the basic ideas intended by the author.
While there may be areas in which data analysis can point out solutions to problems, I'm not convinced by the author's assertion that Big Data will make experts in various fields obsolete. I would guess that Big Data will be a tool in the hands of experts, but I don't think we'll find data analysts replacing doctors and subject matter experts on a large scale.