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on 25 June 2016
The term "Big Data" is constantly being thrown around today by businesses and the technology world. Leveraging big data to gain competitive advantages is an organisational panacea. Given current compute power and storage capabilities we are now able to truly leverage big data in ways one could only previously dream.

This book however lays an important theme: big data is about knowing what and not why. Said differently, it is more about correlations than causations and making that mind shift is at the core of leveraging big data. Organisations that can combine mathematics and statistics along with programming and network science will be at the forefront of big data literacy.

Said best by the authors: "… when we say that humans see the world through causalities, we’re referring to two fundamental ways humans explain and understand the world: through quick, illusory causality; and via slow, methodical causal experiments. Big data will transform the roles of both."

Three key takeaways from the book
1. Some staggering "Big Data" statistics at the time this book was written:
○ About seven billion shares change hands every day on U.S. equity markets, of which around two-thirds is traded by computer algorithms based on mathematical models that crunch mountains of data to predict gains while trying to reduce risk.
○ Google processes more than 24 petabytes of data per day, a volume that is thousands of times the quantity of all printed material in the U.S. Library of Congress.
○ Facebook, a company that didn’t exist a decade ago, gets more than 10 million new photos uploaded every hour. Facebook members click a “like” button or leave a comment nearly three billion times per day, creating a digital trail that the company can mine to learn about users’ preferences.
○ The 800 million monthly users of Google’s YouTube service upload over an hour of video every second.
○ The number of messages on Twitter grows at around 200 percent a year and by 2012 had exceeded 400 million tweets a day.
○ More than 300 exabytes of stored data existed in 2007. To understand what this means in slightly more human terms, think of it like this. A full-length feature film in digital form can be compressed into a one gigabyte file. An exabyte is one billion gigabytes. In short, it’s a lot. Interestingly, in 2007 only about 7 percent of the data was analog (paper, books, photographic prints, and so on).

2. The amount of stored information grows four times faster than the world economy, while the processing power of computers grows nine times faster.

3. Big data’s ascendancy represents three shifts in the way we analyze information that transform how we understand and organize society:
i. We can analyze far more data
ii. Loosen up our desire for exactitude

A move away from the age-old search for causality. Instead we can discover patterns and correlations in the data that offer us novel and invaluable insights. The correlations may not tell us precisely why something is happening, but they alert us that it is happening. Fundamentally, big data is about 'what', not 'why'.
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on 14 July 2014
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. When a movie is predicted to be a flop, it never gets made so the prediction never gets tested. Meanwhile the same old movies are getting made again and again and with the same stars, their ageing making their performance increasingly ridiculous. So the whole industry is locked in a spiral of decline having poisoned the well of creativity. We can probably live with the suicide-by-data of Hollywood, but the same processes are behind many of the problems with the world financial system.

This book was given to me as a gift, but I've learned to steer clear of any book that has notes and bibliography which consist mainly of media articles and journalistic interviews rather than academic research.
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on 20 October 2013
This is a book of missed opportunities.
One of the central themes in this book is the conversion from looking for causality to correlations. Showing that the authors knowledge of physics ends with Newton, that they have never studied genetics or developmental biology, and totally missed the artificial neural networks. From that I infer that they also missed many more parallels in all fields of science that I am less familiar with. That is a pity.

The section on ethics and privacy is especially weak. Lots of questions are unanswered and most are not even posed. If you have an Android phone and you cheat on your spouse, Google has enough information to deduce that with a high level of certainty. Are they harvesting that information? Can someone else create a startup to sell that information?
There is also no discussion on political use. Independent of the type of smart-phone the NSA will also know if someone is cheating. Is that information used to influence the lawmaking process by the government or by big data companies?
Is knowledge on the political preference of citizens used to 'improve' the borders of electoral districts? Are certain groups of voters and individuals actively encouraged or discouraged to vote based on their political preference by lawmakers or political parties? And for all of these questions (and many more): would that be ethically acceptable?
Of course these are just examples of some issues that are missing. I don't expect the authors to address exactly these, but what is discussed is too little to make this book relevant. That is not good.

In the book the authors say that it is useful information to know when readers stops reading a book. To answer that for this book: it is probably already somewhere before half of it, when the authors keep repeating themselves in every chapter without any new insights. The reason I finished it was that I was wondering if you can write a book on big data without discussing identity theft. Apparently you can. That is surprising.

This book may have some useful insights, but (ironically for a book on big data) you have to consult many other sources to get a more useful picture of reality.
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TOP 100 REVIEWERon 5 March 2013
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. That is to say, big data "marks an important step in humankind's quest to quantify and understand the world" in ways and to an extent once thought impossible.

These are among the dozens of passages that caught my eye, also listed to suggest the scope of Mayer-Schönberger and Cukier's coverage.

o Letting the data speak (Pages 6-12)
o More, messy, good enough (12-18)
o More trumps better (39-49)
o Illusions and illuminations (61-68)
o Quantifying the world, and, When words become data (79-86)
o The "option value" of data, and, The reuse of data (102-107)
o The value of open data (116-118)
o The big-data value chain (126-134)
o The demise of the expert (139-145)
o Paralyzing piracy (152-157)
o The dictatorship of data, and, The dark side of big data (163-170)
o Governing the data barons (182-184)
o When data speaks, and, Even bigger data (189-197)

On Page 197, Mayer-Schönberger and Cukier observe, "What we are able to collect and process will always be just a tiny fraction of the information that exists in the world. It can only be a simulacrum of reality, like the shadows on the wall of Plato's cave. Because we can never have perfect information, our predictions are inherently fallible. That doesn't mean they're wrong, only that hey are always incomplete. It doesn't negate the insights that big data offers, but it puts big data in its place -- as 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. It also suggests that we must use this tool with a generous degree of humility.....and humanity."

I realize that no brief commentary such as mine can do full justice to the material that Viktor Mayer-Schönberger and Kenneth Cukier provide in this volume but I hope that I have at least suggested why I think so highly of it. Also, I hope that those who read this commentary will be better prepared to determine whether or not they wish to read the book and, in that event, will have at least some idea of how to leverage Big Data applications and capabilities to transform how they live, work, and think.
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on 20 October 2014
This book feels extremely empty, even though it is less than 200 pages long. It is extremely shallow, misses in-depth analysis, is annoyingly repetitive (some bits of the book are the same word for word), made of 80% of fillers, not insightful at all, and sometimes very naive (whether this was on purpose or not I cannot say).

I think the authors tried to be the first to write about big data, but they failed to deliver something that is worth reading. That is a shame, because there are a lot of things to say about it, and they had the skills and network (I think) to write a reference book on the subject. I would definitely not recommend buying this book.
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on 10 December 2013
I agree with other reviewers that the topic is superficially treated, and certainly for anyone that works in the field or has academic interest in Big Data the book will fall short. However for the uninitiated like me this is a great conceptual introduction to the subject and if read right after Who owns the Future by Jared Lanier the two books come together to form a very interesting and thought provoking package dealing with the future, the role which large companies such as Google, Facebook and Amazon play and how these companies have profited from data freely given, or otherwise by the public. It is certainly worth reading both in tandem.
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on 13 June 2016
Luckily, the first book I have read on Big Data. The exposition is clear,concise, and intellectually satisfying and the illustrations of Big Data in action are very illuminating. The section on future risks has an odour of the academic - but this does not detract from its importance as a key text.
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on 5 October 2017
Nice overview of Big Data. If you want to peer a bit deeper into what the topics actually mean.
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on 9 January 2014
I've been aware of the rise of big data and some of its implications, but this book helped to spell them out in an engaging and quite fascinating manner. The idea that decisions can be driven purely from analysis of sufficient data has a huge number of intriguing implications. This is a must read for anyone interested in how society engages with our digital future.
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on 29 June 2016
Learned so much how the internet works. It will blow your mind how we have access to the type of information as what we have now!
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