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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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The authors start by discussing how Google using its analysis of people's queries is more predictive about flu epidemics than medical experts have been. The human genome can be codified in a fraction of the time that was required when it was being decoded for the first time. They discuss how big data has enabled entrepreneurs to inform customers about the optimal time to buy flight tickets given that airlines vary their prices according to hidden methods that big data statistics has helped to make more sense of. The examples are a good starting point to start the discussion with the reader. The authors start by discussing how we have always been trying to come up with data about our populations, desires to do census analysis has been with us for a long time. We made progress through sampling techniques and statistics helped to enable data gathering about the population at large using smaller and less time consuming samples. The authors discuss how big data is messy, it is imprecise and is helpful for overviews but not for model building with respect to figuring out the mechanics of what is being observed. When you try to get all of the data about something there will inevitably be noise and looking for correlations can sometimes be the most fruitful way to use the data to figure out empirical relationships rather than search for underlying dynamics. The authors discuss datification which means the consolidation of data into a larger database that can then be used to give much more useful guidance to the population at large about phenomenon that required a look from above at all the data together. Matthew Maury is used to reinforce the usefulness of this approach, he was a naval officer who aggregated ships logs to help inform ship captains about most useful routes and more efficient transiting. The authors move on to the more concrete and start to discuss the value of big data. They give the obvious background on the value of traditional data and then give food for thought on how having data for everything can lead to new ideas and utility that was unimaginable in the past. Big data analytics will be required for document translation, smart device coordination, smart cities and social network analysis. The value in big data is of course, the data, but the utility of that data might be further midstream or downstream that others are better placed to harvest. The authors move on to discuss the data value chain and how to think about it. The authors discuss the implication of the big data revolution and how it is enabling consumers to get the best deals and how statisticians are a highly desirable skill set. The authors move on to the risks of big data which are numerous of course. Much discussed are the privacy of the data that is generated. The ownership of that data and the licensing of it are topics which will continue to surface and the legal framework to analyze disputes will need to be further developed. Misunderstanding correlation and causation will also be a risk in big data analytics and hypotheticals like the government quarantining those who search for flu on google are used as hyperbolized examples. The authors finally leave the reader with a view on the future. They use an example of how big data statistics was used to substantially improve the ability to find overcrowded illegal slum housing as a concrete example of how we can use data to enhance our cities and improve governance and efficiency.
Big data is a subject which continues to step into more and more categories as our ability to measure continues to improve. How big data can be used will be a continued subject that both academics and practitioners will continue to be thought about and experimented on. It will give rise to a new consumer culture and potentially to new ways of organizing people and infrastructure. Big Data is an excellent readable overview of how data has always been used to guide policy, how big data is being used today, what the value chain of the data industry looks like, what the risks are of big data and how big data can enhance the future. Its easy to read and illuminating.
I encountered two huge issues in the text. First, the authors repeatedly argue that it's OK if Big Data contains "messy" data, because they assert that when "n=all" then the statistical rules about sampling don't apply. This argument fails two ways: first, if n=all but if the data contains "messy" (erroneous) data points in critical places, then it will be misleading and perhaps even completely wrong. Second, when using past data where "n=all" to project future events, then it's no longer true that "n=all." Instead, we have data for "n=all(where(time=past))" and we're using that data to try to predict events in a completely separate data set ("time=future"), and it's entirely possible that there are critical differences demarcated by "time=now."
The second huge issue, for me, was the authors' focus on the concept that Big Data brings with it a huge risk that we will use data to predict future behavior -- and that we will then use those predictions to punish people for acts they have not committed (e.g., the "Minority Report" problem). They distort this argument in two ways: first, by assuming that society would actually do this, and second, by asserting that any action taken based on these predictions (such as increasing scrutiny or assigning social workers to visit at-risk juveniles) is "punishment."
I was also skeptical of the authors' general reverence of, and deference to, data scientists as professionals and experts. The author believe that it's plausible to expect a new profession of internal and external "algorithmists" to arise, to protect consumers' privacy interests and society's interests against the potential abuses by Big Data users.
The book also failed to provide real-world "how-to" examples, instead providing only "end result" examples and conclusions that often seem incomplete and sometimes implausible. Their many useful examples of useful information extracted from Big Data all doubtless represent the end-point of many, many explorations of Big Data; they probably also represent a subset of correlations derived, after many misleading correlations were removed.
Finally, note that the book's lengthy end notes, bibliography, and index represent a full one-third of the book's length.
There's a lot of useful information in this book, especially for someone just trying to learn about the concept of Big Data. But there's also a lot of hype, and a lot of repetition of ideas without meaningful factual support.