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Big Data: A Revolution That Will Transform How We Live, Work and Think
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Big Data: A Revolution That Will Transform How We Live, Work and Think [Format Kindle]

Kenneth Cukier , Viktor Mayer-Schonberger
3.5 étoiles sur 5  Voir tous les commentaires (4 commentaires client)

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Descriptions du produit

Revue de presse

What I’m certain about is that BIG DATA will be the defining text in the discussion for some time to come."— "The authors make clear that ‘big data’ is much more than a Silicon Valley buzzword… No other book offers such an accessible and balanced tour of the many benefits and downsides of our continuing infatuation with data."—Wall Street Journal

" elegant and readable primer"—New Scientist

"'Big data' [is] one of the buzzwords of corporate executives, tech-savvy politicians, and worried civil libertarians. If you want to know what they’re all talking about, then BIG DATA is the book for you, a comprehensive and entertaining introduction to a very large topic....Mayer-Schönberger and Cukier offer up some sensible suggestions on how we can have the blessings of big data and our freedoms, too. Just as well; their lively book leaves no doubt that big data’s growth spurt is just beginning."—Boston Globe

"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,


"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

"Big Data breaks 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

"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,

"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. The authors 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

"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 Center

"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

"Plenty of books extol the technical marvels of our information society, but this is an original analysis of the information itself—trillions of searches, calls, clicks, queries and purchases....A fascinating, enthusiastic view of the possibilities of vast computer correlations and the entrepreneurs who are taking advantage of them." -- STARRED Kirkus Reviews

"This book offers important insights and information" -- Booklist

Présentation de l'éditeur

Since Aristotle, we have fought to understand the causes behind everything. But this ideology is fading. In the age of big data, we can crunch an incomprehensible amount of information, providing us with invaluable insights about the what rather than the why. We're just starting to reap the benefits: tracking vital signs to foresee deadly infections, predicting building fires, anticipating the best moment to buy a plane ticket, seeing inflation in real time and monitoring social media in order to identify trends. But there is a dark side to big data. Will it be machines, rather than people, that make the decisions? How do you regulate an algorithm? What will happen to privacy? Will individuals be punished for acts they have yet to commit?

In this groundbreaking and fascinating book, two of the world's most-respected data experts reveal the reality of a big data world and outline clear and actionable steps that will equip the reader with the tools needed for this next phase of human evolution.

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Commentaires en ligne 

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Commentaires client les plus utiles
4 internautes sur 4 ont trouvé ce commentaire utile 
Format:Relié|Achat authentifié par Amazon
Des exemples sympas mais c'est un livre très généraliste. Les concepts généraux sont très bien expliqués mais je suis resté sur ma faim. Il n'y a aucune explication technique ou scientifique.
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2 internautes sur 2 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Required reading 22 juin 2013
Format:Relié|Achat authentifié par Amazon
A good starter for anyone who wants to understand the big picture about Big Data.
However this book will not tell you how to write software to perform the analysis of Big Data.
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3.0 étoiles sur 5 Positif 9 avril 2014
Format:Relié|Achat authentifié par Amazon
je suis en train de lire le bouquin en ce moment. Donc impossible d'émettre un avis définitif.
je peux dire que les débuts sont très intéressant et faire l'hypothèse que c'est un bel ouvrage.
Assez content pour l'instant donc.
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1 internautes sur 3 ont trouvé ce commentaire utile 
3.0 étoiles sur 5 Livre de qualité mais trop exhaustif 25 septembre 2013
Livre généraliste de qualité sur les Big Data. D'esprit américain, parfaitement orienté business, mais sans oublier les nombreuses incidences sociales.
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Commentaires client les plus utiles sur (beta) 4.2 étoiles sur 5  247 commentaires
83 internautes sur 89 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Big data provides a lot of hope for the future, this is largely a mile-high view of the benefits 2 février 2013
Par Charles Ashbacher - Publié sur
Format:Relié|Commentaire du Club des Testeurs (De quoi s'agit-il?)
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. Rather than use a team of translators to develop the conversion rules, an enormous number of documents that have already been translated from one language to another are examined and used to build the models used in translation. This has been so effective that there is a joke that the efficiency of the translation software is greater when the linguists are not involved.
There is an enormous amount of potential value in the examination of big data; some of the successes so far have been surprising, yet understandable in retrospect. My favorite was Wal Mart discovering that when there are predictions of a hurricane, stores in the projected area to be hit sell a lot of Pop Tarts. As a teacher of statistics and occasional consultant, I was a bit surprised to learn that the big data revolution does not involve a lot of statistics; in fact statistical thinking is discouraged by many that work in big data. The goal of big data analysis is to find correlations, not causes.
If you have an interest in the mile-high view of what big data is and how it is being applied, this is an excellent book to satisfy that curiosity. In a world where the demographics and economic trends seem to doom us to a cycle of extreme austerity, big data has the potential to provide a great deal of relief. Learning how it is being used can help make you more optimistic about the future.
145 internautes sur 164 ont trouvé ce commentaire utile 
3.0 étoiles sur 5 Should Have Been Condensed to An Article - 7 mars 2013
Par Loyd E. Eskildson - Publié sur
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. Potential sources include that approximately 7 billion shares change hands every day in finance, two-thirds via computer model direction, Google processing over 24 petabytes of data/day, Facebook getting over 10 million new photos uploaded every hour.

Random sampling was an earlier data analysis breakthrough, but it doesn't work well if users want to make smaller and smaller subgroup analyses. It no longer makes much sense when we can harness large amounts of data. Detection of credit card fraud works by looking for anomalies, and the best way to find them is to crunch all the data, even at an individual level - definitely a big data problem.

Xoom specializes in the analysis of international money transfers - it raised an alarm in 2011 after noticing a slight increase in Discover Card transactions from N.J. - they came from a criminal group.

Teaching computers to translate requires not only teaching them rules, but the exceptions as well. IBM researchers in 1980 improved the current state of the art using statistical probabilities derived from 3 million sentences pairs, but the results still weren't good enough. Google tried again in 2006 using every translation it could find - eg. corporate websites in multiple languages, translations of official documents, and translations of books from Google's book-scanning project. They compiled 95 billion English sentences and translations, some of dubious quality. It offers translations of over 60 languages and the results are more accurate than others, though still highly imperfect. The improvement came from using more data.

PriceStats uses a software crawler to collect a half-million prices of products sold in the U.S., daily. Analysis then allows immediate detection of price change trends, vs. BLS processes that take about two weeks to complete and cost about $250 million/year.

A third of Amazon's sales reportedly result from its correlation-based recommendation system - putting local booksellers at a decided disadvantage. Netflix has a similar system to boost its rental volume.

Bottom-Line: 'Big Data' was more up-to-date than prior works on the subject, but provided very little in explanation of the specifics of new uses. I was especially disappointed that it had failed to report on new applications in health care that would improve the quality and cost-effectiveness of care.
42 internautes sur 54 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 An Excellent Overview of Big Data & Machine Learning 1 février 2013
Par Ira Laefsky - Publié sur
Format:Relié|Commentaire du Club des Testeurs (De quoi s'agit-il?)
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
7 internautes sur 7 ont trouvé ce commentaire utile 
2.0 étoiles sur 5 Big Data 6 mai 2013
Par Sinankt - Publié sur
Format:Format Kindle|Achat authentifié par Amazon
Although the book contains a lot of examples, i felt bored most of the times by the extensive repetition of the concepts and the ideas. The book could be written in a 100 page and still give the message.
9 internautes sur 10 ont trouvé ce commentaire utile 
1.0 étoiles sur 5 Sensational pseudo-statistical slop for the numerically challenged 6 décembre 2013
Par Teresa Neeman - Publié sur
This is sloppy journalism at its best, and irresponsible and dangerous at other times. One would have to conclude that their target audience suffer from math anxiety, but are secretly turned-on by super-geeks who seem to be able to do magical things with numbers. The main messages of the book are given away in the first chapter, and once read, you’ll feel little reason to read on.

A few of their puzzling assertions:

“More trumps better”. The authors should read up on the history of sampling, with particular attention to the forecasting debacle of the “” in the 1936 presidential election. Their prediction based upon 10 million data point was dead wrong, and in this case, sampling trumped big. The omission of this very celebrated story makes one wonder if the authors have any understanding of sampling.

Because Steve Jobs had access to his complete genome, he was able to live years longer than he would have without this information. Really? How do they know? Maybe this is a case of misplaced causation, a subject they deal with in another chapter. After all Steve Jobs is a single individual.

“Reaching for a random sample in the age of big data is like clutching at a horse whip in the age of the motor car.” They seem ignorant of the vast number of studies where data are sparse or unavailable.

“Big data saves lives.” This remark was based upon a story that when vital signs in preemies in hospitals appear stable, than one needs to be concerned. But what puzzles the reader is what are signs that assure the doctor that the preemie is okay – unstable vital signs?

“A strong correlation means that when one of the data values changes, the other is highly likely to change as well. Conversely, a weak correlation means that when one data value changes little happens to the other.” I hope none of the authors teaches statistics.
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