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Big Data: A Revolution That Will Transform How We Live, Work and Think Paperback – 10 Oct 2013

3.7 out of 5 stars 56 customer reviews

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Product details

  • Paperback: 256 pages
  • Publisher: John Murray (10 Oct. 2013)
  • Language: English
  • ISBN-10: 1848547927
  • ISBN-13: 978-1848547926
  • Product Dimensions: 13.1 x 1.7 x 19.7 cm
  • Average Customer Review: 3.7 out of 5 stars  See all reviews (56 customer reviews)
  • Amazon Bestsellers Rank: 39,248 in Books (See Top 100 in Books)

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Product Description


'Just as water is wet in a way that individual water molecules aren't, big data can reveal information in a way that individual bits of data can't. Mayer-Schonberger and Cukier show us the surprising ways that enormous, complex and messy collections of data can be used to predict everything from shopping patterns to flu outbreaks' (Clay Shirky, author of Cognitive Surplus and Here Comes Everybody)

'Every decade, there are a handful of books that change the way you look at everything. This is one of those books. Society has begun to reckon the change that big data will bring. This book is an incredibly important start' (Lawrence Lessig, Roy L. Furman Professor of Law, Harvard Law School, and author of Remix and Free Culture)

'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, Boing Boing)

'In Big Data, Mayer-Schonberger and Cukier break new ground in identifying how today's avalanche of information fundamentally shifts our basic understanding of the world. Argued boldly and written beautifully, the book clearly shows how companies can unlock value, how policymakers need to be on guard, and how everyone's cognitive models need to change' (Joi Ito, Director of the MIT Media Lab)

'This brilliant book cuts through the mystery and the hype surrounding big data. A must-read for anyone in business, information technology, public policy, intelligence, and medicine. And anyone else who is just plain curious about the future' (John Seely Brown, former Chief Scientist, Xerox Corp. and head of Xerox Palo Alto Research Centre)

'The book teems with great insights on the new ways of harnessing information, and offers a convincing vision of the future. It is essential reading for anyone who uses - or is affected by - big data' (Jeff Jonas, IBM Fellow & Chief Scientist, IBM Entity Analytics)

'Big Data is a must-read for anyone who wants to stay ahead of one of the key trends defining the future of business' (Marc Benioff, Chairman and CEO, salesforce.com)

An excellent primer (Financial Times)

Fascinating (Observer)

Raises profound questions (Evening Standard)

An elegant and readable primer (New Scientist)

Viktor Mayer-Schonberger is at Oxford, Kenneth Cukier is at The Economist and together they make a great team. They haven't just identified a new trend. They also understand double-edged swords. Much of their book goes into how, possibly to regulate Big Data, when most of our legal system, so far, has no awareness of the significance of the 'excess' data we slough off in such vast quantities (Literary Review)

Informative . . . Mayer-Schonberger and Cukier make interesting observations about data-crunching techniques (Guardian)

Book Description

Edward Snowdon exposed Big Data. Here is the ultimate guide. Cory Doctorow (Boing Boing) says: "Just the thing to get your head around the big changes already underway".

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Customer Reviews

Top Customer Reviews

Format: Hardcover Verified Purchase
In "Big Data", Mayer-Schönberger and Cukier discusses the shift in our society towards the ability to generate, store and analyze considerably larger amounts of data than before. There has been a trend towards more data for decades (even centuries, I suppose), but recent technological advances has given rise to a visible qualitative shift in the way which we manipulate data. Statistics used to focus more on getting the most out of few data, whereas in recent decades, there has been rising interest in trying to get information out of large, unruly sets of data (often labeled "machine learning" or "data mining"). The information extracted in such cases are often more vague, but as the authors argue, can nonetheless, based on sheer size and available computing power, lead to essential insights.

Most of Mayer-Schönberger and Cukiers book consists of discussions of examples where an innovative use of a large, unwieldy data set yields large insights or value added. The examples are diverse, ranging from air-ticket price prediction to constructing ocean navigation maps or predicting exploding sewer lids. They make it quite obvious that the usefulness of big data is not a hypothetical future possibility, the data are with us now, are already a part of our society, and will only increase in importance in the future. These facts make the book relevant: Big data is a rising trend, and the more people become conscious of this, the more we'll be able to harness its potential.

The book is not flawless, however. There were two main points which I found problematic:

1. The authors divide their discussions into basically seven chapters on the benefits of big data, two on the dangers of big data, and finally a summing up.
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Format: Paperback
This book takes a typical "management primer" approach - it is narrative-based, as deep as an oil slick and ultimately as intellectually nutritious as cotton candy. There is a grinding inconsistency between the approach of the book and the message it is trying to impart. A book about the benefits of using data to make decisions needs to show-not-tell and this book contains virtually no data or quantitative analysis.

One problem with the narrative approach is that sooner or later any given reader encounters a story they have some familiarity with and realises how it has been simplified and spun to suit the purposes of the book. That moment came for me with the story of Steve Jobs' management of his terminal cancer referenced from Isaacson's authorised biography, pp. 550-551. But any rounded account of this story also has to engage with the very different impression given by pp. 452-456 of the same book. You need to take an 'N = all' approach to your sources, guys!

I really parted company with this simplistic narrative at the account of 'The-Numbers.com' which uses big data to predict income from movie proposals (pp. 144-145). I challenge any movie fan to read this section and not be thinking: "ha! that explains a lot about Hollywood's output over the last decade!". But the book never even acknowledges there might be any problem with this approach. For the rest of the book, I was expecting the authors to return to this piece of low-hanging fruit, but they never did. What a missed opportunity to introduce the problem of "causal pollution" of big data sets. As soon as big data gets used extensively to drive decisions, feedback from those decisions begins to pollute the data set reducing its predictive value and constricting the solution space.
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Format: Hardcover
According to Viktor Mayer-Schönberger and Kenneth Cukier, "There is no rigorous definition of big data. Initially the idea was that the volume of information had grown so large that the quantity being examined no longer fit into the memory that computers use for processing, so engineers needed to revamp the tools they used for analyzing it all...One way to think about the issue today -- and the way we do in the book -- is this: big data refers to things one can do at a large scale that cannot be done at a smaller one, to extract new insights or create new forms of value, in ways that change markets, organizations, the relationship between citizens and governments, and more." Much more.

Mayer-Schönberger and Cukier identify and examine several "shifts" in the way information is analyzed that transform how we understand and organize society. Understanding these shifts helps us to understand the nature and extent of big data's possibilities as well as its limitations. For example, more data can be processed and evaluated. Also, Looking at vastly more data reduces our preoccupation with exactitude. Moreover, "these two shifts lead to a third change, which we explain in Chapter Four: a move away from the age-old search for causality." They devote a separate chapter to each of these shifts, then shift their and their reader's attention to a term, indeed a process that helps frame the changes: datafication, a concept they discuss in Chapter Five.

Then in Chapters Six and Seven, they explain how big data changes the nature of business, markets, and society as what they characterize as a multi-dimensional "treasure hunt" continues to extract insights from data and unleash dormant value by a shift from causation to correlation.
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