Big Data is a topic that is all the rage but at the same time isnt well defined. Authors Viktor Mayer-Schönberger and Kenneth Cukier give an overview of what is being done with the massive amount of data that is being generated from online interaction coupled with advances in practical statistics on the analysis of this data. The authors go through examples of how big data is being used today to give a flavour of it and then follow up the rest of the book with what is going on in the field, how it is useful, where aspects of it are going and some of the concerns we should have about our privacy.

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.

"Big Data: A Revolution..." was often engaging and included some interesting examples, but it was a disappointment. As others mention, the authors use repetition instead of evidence or proof, and ultimately I was not convinced by many of their claims.

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.

Authored by Economist writer Kenneth Cukier and Oxford Professor Viktor Mayer-Schonberger, Big Data is a new book that explores what the big fuss about the business buzzword "big data". Written to a general audience, the authors do an excellent job highlighting the fundamental changes that big data has made on society, law, and the very question of human nature. Most people think that big data is some technical idea best relegated to "data nerds and geeks". But doing so would be a huge mistake to an informed citizen...or the typical business school student. Big data's importance on humanity, particularly business people, cannot be understated as demonstrated by the authors. Let us count the ways.

First, according to the authors, big data changes the way we use statistics. Before big data, humanity was confined by taking only a sample of a population to conduct statistical analysis. But with today's technologies of storing and computing vast amounts of data, we don't have to limit ourselves to a sample. We can, as the authors note, do analysis on the population (i.e. N = all). And this ability allows us to solve problems at the granular level, which we couldn't do before. Being exact is no longer the goal. More data, though messy, gives us a general direction of what is happening...and for businesses, that's good enough.

Second, big data, for all its practical uses, shifts the goal of marketing from causality to correlation. In other words, the what matters more than the the why. Regression analysis is a classic and powerful tool taught in business schools, yet the goal is still to find out why customers like or do X. With big data, regression analysis is more robust because it contains the whole population not just a sample. And the insights couched in correlations are gold for decision-making.

Finally, the authors show the dark side of big data--a warning that business executives should heed. Since big data's power comes not just in divining insights that drive action but also in predicting behavior, a new set of rules governing the use of data by private businesses is needed. In particular, to prevent fraud or abuse of data, the authors recommend that the burden of privacy be shifted from users (consent forms) to holders of data (businesses). The power of big data is in its secondary uses (Google makes free email not only to help people but to track them and advertise to them) and only businesses are in the best position to know how to use data to its fullest potential. Therefore, businesses need to set limits on what can and can't be done with data...and governments must hold such companies accountable for data abuse.

More can be written about the many big ideas about big data in this provocative and thoughtfully-written book. But I must stop. The rich examples and stories of how big data has already and will continue to revolutionize how we live, work, and think are well worth your time to read this book. For the business school student, one important career advice from the book is notable, namely that big data gives us a hint of what are expected of future managers and what to study now--mathematics and statistics with a dash of programming.

This book addresses a hot topic for a large audience and provides a decent introduction. It outlines ways that big data has already impacted our lives and some of the history of big data, which not so shockingly, pre-dates our digital age.

I gave this book three stars for a few reasons. First, it's not meticulously edited and researched. The authors make simple factual errors, like perpetuating the common mistake that SQL actually means "structured query language" (page 45, go read O'Reilly Learning SQL) to more subtle mistakes like generalizing medical findings from Amalga (page 128), considered a big disappointment/failure in medical analytics and not substantiated by other research. Overall, the authors are trying to write a convincing story and theme for big data without the requisite science or background that would lend the best examples or credibility (i.e. no Brian Greene here).

I'm writing from the perspective of someone who has actually worked with big data, founded a company doing predictive medical analytics and who wants to learn more about the field. This book provides little in the way of helpful examples and its history is interesting but certainly not practical (i.e. Commander Maury "Pathfinder of the Seas" from the 19th century). It does a great job at synthesizing and thinking about the implications of big data. Their persistent theme of "what, not why" resonates and these guys are clearly good writers.

Overall, I would recommend Automate This (Steiner) as a better alternative both from the depth of examples and making big data more approachable.

An excellent book overall. This work goes into many relevant examples of Big Data applications and in contrasting correlation vs causation. We (the big We .....the human race) are now entering a world where the traditional methods of data analysis; such as sampling a subset of the available data, performing data cleanup and filtering, somewhat biased in a way that supports a hypothesis, and seeking to explain the why or how of a phenomena (creation of a data model) is being turned upside down by simply focusing on the what, making use of all the data available, and not attempting to explain the correlations that emerge, but using them nevertheless for predictions. Technology now permits this streamlined approach, and the applications and implications are profound.

The book also does a great job of covering numerous examples and benefits, along with the limitations and dangers of Big Data. Mayer-Schönberger addresses the need to control Big Data applications to protect individuals and institutions. I also particularly liked the discussion of how data itself is increasingly recognized to be an asset, and the need to quantify it's value in financial terms is a growing necessity for corporations. I could not agree with this more!

The book covers other dimensions of the subject that I have not addressed here. I would rate this book with 4.5 stars, as it is well written and contains an extensive set of useful references.

Bottom line, I highly recommend this book to anyone interested in learning about Big Data, and the impact Big Data is already having in many aspects of our lives.

John A. Serri, PhD

"At its core, big data is about predictions. Though it has been described as part of the branch of computer science called artificial intelligence, and more specifically, an area called machine learning, this characterization is misleading. Big data is not about trying to "teach" a computer to "think" like humans. Instead, it's about applying math to huge quantities of data in order to infer probabilities: the likelihood that an email message is spam; that the typed letters "teh" are supposed to be "the"; that the trajectory and velocity of a person jay-walking mean he'll make it across the street in time - the self-driven car need only slow slightly. The key is that these systems perform well because they are fed with lots of data on which to base their predictions. Moreover, the systems are built to improve themselves over time, by keeping a tab on what are the best signals and patterns to look for as more data is fed in.

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."

This book provided me with an excellent overall beginners course in the area of Big Data. Without suffocating the reader in complicated mathematics driving big data technology, Mayer-Schonberger and Cukier, use well researched and timely examples of the overarching implications of this movement.

You realize very quickly that there are indeed certain career skillsets that are rapidly becoming obsolete, and other skillsets, like data scientist, that are rapidly becoming more and more valuable. I keep asking myself, should I have studied computer science? Reading this book as an entrepreneur, I feel like I've stumbled into uncharted territory. I also kept saying: "Oh my god, this field of study could change my whole business!"

The authors make the complex world of "big data" accessible. Reading this book, you get the sense they truly understand the full spectrum of issues well enough to captivate, without complicating.

There are just so many interesting angles to this phenomena, all of which were brilliantly illuminated. Privacy, security, public health, commerce, crime, travel, law, and in some cases even morality itself, Big Data forces you to consider the broader implications of a datafied future.

For anyone looking for a great primer on Big Data and the concerns that surround it, this is the book for you. I would highly recommend for business analysts and managers, including c-level execs. Mayer-Schonberger does a great job on identifying the key issues around Big Data and offering his opinion and insights on how we should move forward.

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)

Very interesting book. It is one of the books that I would recommend to be used as reference book, as it contains lots of examples and quotations about individuals, who woke up to the reality of the Big data and how it could be utilised for the good and, perhaps, the more challenging way of profiling innocent people according to their names, culture, religion, political thoughts etc.

I would also recommend this book to the anyone interested in studying or curious about "the concept machine learning and what role the big data can play." Sometimes, you may wonder how Cortana finds out when it is the time to leave for work or home; or it predicts how the traffic would be, while you are on your way to work or home. If you do wonder about this, then you must read this book.

Author's acknowledgement of the role of "algorithmists" in Big data is also plausible. Imagine the day the nutters become part of the law society. I think this would inject honesty into the "how most lawyers handle cases that they are working on."

You can skip this paragraph: If you ever wondered how Neural Network proponents will ever succeed to teach a basic Times table to algorithm that requires two input numbers, like 8 time 7, then after reading this book, you will note Big Data will may help. Note that when we are young and attending elementary schools, most of us learn the Times table by memorising. As we grow, we simply identify a strategy where we, for example, think 7 Times table goes up by 7 and 8 Times table goes up by 8. Hence, no need to memories. In this instance, Big Data can be used to bridge the gap between the Neural Network and those, like me, who very much believe that we should focusing on mimicking how our neocortext works and complement it with Algorithms that make our machines perform better than our neocortext. In this paradigm, the Big Data will be used for playing the role of the memory and experience, while still we will be able to create strategies that can be serialised into and de-serialised from the Big Data repository.

The author does also go on about privacy and the challenges Big Data faces. I think the question to ask is: if we accepted to use the cloud, have we not sleepwalked into sharing our data with those, who are there to analyse data? Is it the machine that should only have an access to our private data; or also those, who own this smart machines? Would the combination of Big Data and Intelligent machines bring about the creation of all-knowing being that cannot only know our past, but can also predict our future activities. And imagine what impact this would have on currency/stock traders? Do not even think politics here, as this will get more scarier.

If you have ever watched the Movie "Her" and reasoned with the poor man, who fallen for OS that knows him very well, then think about the consequence of intelligent machines, powered by Big Data! And this is another reason to read this book.

However, we should never fear exploring what we are capable of doing for the good of this world and its inhabitants; but should also be prepare to ensure that the all-knowing thing, which we are in the process of creating, is not one dictator, but one that lives and functions within democratic system.

Excellente présentation du sujet - de quoi s'agit-il, avantages, risques, ... J'ai été véritablement stupéfait de lire comment les données étaient analysées aujourd'hui et l'impact que cela pouvait avoir sur le monde. Pas sûr que beaucoup de monde réalise ce qui se passe, à son insu; il m'a permis de comprendre beaucoup de choses que je constate au quotidien, bien pratiques, mais dont j'ignorais le fonctionnement (il ne rentre pas dans les détails techniques mais donne quelques info). En tout cas ça m'a réveillé et rendu encore plus méfiant, ou plutôt conscient, vis-à-vis d'internet, même si tout n'est pas noir, loin s'en faut et même si le domaine des big data va bien au-delà de l'internet. Le point de vue est business et sociétal, pas du tout technique, mais ça m'a donné fort envie d'en savoir beaucoup plus, aussi point de vue technique.

Je le recommande sans hésiter. Pas besoin d'être expert pour le lire.

Consegna rapida.

Contenuti del libro: buoni, forse un pò generici, ma è pur sempre un buon inizio per iniziare a capire i BigData.

Well written introduction to an emerging discipline that will affect every aspect of humanity's future. The book treats the reader as intelligent but does not require prior knowledge in any aspect of statistics, computer science, or philosophy. Its many examples make the book's points crystal clear. This is one of those few books that explain how decisions will be made in the future. At the same time, it shows humanity's role in creativity and free will that may escape the casual, uncritical reader otherwise. The author is a master of the discipline and blessed with wisdom that goes beyond the promise of the book's title.

Un libro que ayuda a comprender el transito a la era digital, muy bien explicado, con muchos ejemplos, accesible pero con u. Contenido muy interesante.