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on May 10, 2013
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)
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on May 8, 2016
This is a great book for those of us that are not data engineers. My only complaint with the information is that I believe the author overstates the risks associated with big data. Still, overall, this book is well written and doesn't require a degree in Information Technology to understand, which I appreciated. I would recommend it for anyone who wants to understand the power of big data, and the true meaning behind the statement, "In God we trust, all others must bring data!"
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on April 3, 2014
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.
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on September 16, 2014
The main problem with this book is how it treats the relationship between theory and empirical research. The authors argue that the use of big data is different from that of small data (?) or sampled data in that there is no need for theories or hypotheses since one is operating on a universe instead of a sample. Drilling down is easier and more informative. Etc. But clearly the only way to interpret any kind of data is against a theory about what matters and why. The authors point out the differences between correlation and causation, but this comes near the end of the book after much foolishness about the advantages of big data. The best part of the book is about what companies and governments are doing with big data and how that might be a bit of a problem from the perspective of privacy and civil liberties. One factoid that I found interesting was that Google used text data from the scanning of library books to improve its translation software. There was a good discussion of the use of online search behavior for tracking potential epidemics. Also an interesting story about how the city of New York was able to use big data to predict manhole cover explosions. In the end, though, this book is really just a part of the general hype about big data and not a serious contribution to the larger discussion that is still emerging.
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on April 21, 2014
"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."
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on August 4, 2013
The book provides a very good overview of the "what" of big data: what it is, how people are using it, and what value and benefit it provides to companies and even to individuals (e.g., better service). If that's the kind of thing you're looking for, this is your book.

Oddly, the book drives careful readers to ask others kinds of questions, such as "Whose data is that?" and "What about privacy?". The authors take a stab at those important nascent problems, but their answers seem foggy and uncertain. The chapter on risks seemed to focus on an odd comparison to the culture of prejudgement seen in the film Minority Report. Unaddressed is the actual ownership of the data comprising Big Data. The use of the "little data" generated by individuals - not by the companies that collect, process, and sell it as Big Data - seems a minor concern in the authors' thinking. Potential impacts on the U.S. 4th Amendment and the general weakness of privacy laws did not seem to be relevant to the thesis of the book, although these could evolve into devastating problems for the Big Data movement (and the companies that join it).

In any case the book is interesting and highly readable despite those shortcomings. It will at the very least inform readers about the reasons for all the Big Data hoopla. Others may wonder how we're going to tame the Big Data monster, and the authors' suggestions about that seem to miss the point.
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on May 7, 2013
Big Data should be read by anyone who clicks on a screen, moves a mouse or carries a smartphone. Yes, that means basically everyone. Why? Because our lives are being dissected and managed with a level of intrusion and manipulation that even an old corporate DB data vet like myself ( I was online to IBM's central systems in NY from my PC at home in Arlington, TX, in 1984 ) finds frightening.

The book offers a scary overview into how your data and my data gets gobbled up, warehoused, sold and resold in a way that it is very unlikely each of us will be allowed any privacy that can be honestly protected. There are good and bad elements of this tidal wave. It could help put out fires in many areas of our lives - medical treatments, urban planning, marketing effectiveness, quality of life, etc. - while at the same time drowning us all at the bottom of a world where the root purpose of our lives is not as important as what is "trending" on Twitter. Unfortunately, the solutions proposed by the authors to protect us as a countervailing force ( "human agency") against this threat in response to the data tsunami fall far short of a reliable answer. When you read about their self-policing recommendation to be ushered in by the data accumulators and industry experts with integrity, please go ahead and super impose over that hype the previously glossy image of firms like Arthur Anderson (mother ship to Accenture) that were supposed to be watchdogs responsible for detecting and exposing accounting malfeasance but now are dead and gone at the bottom of the cesspool of professional crooks.

The most troubling aspect of the book's scan of the global data landscape lies in giving credence to "correlation" over "causation." What this boils down to saying to the general public, "We're throwing away the compass used by Western Civilization for a couple of thousand years and forgetting true North. The way of the future is better decided by being able to predict which way the wind blows." ( My quote ). This path of expediency arises boldly as the final recommendation with a naive promise that society will then use the predictions to correct problems that bedevil us now because we mere humans are too silly (pandering to dull-witted conclusions characterized in examples from old BB coaches in the movie Moneyball ) and wasting time trying to determine ultimate root causes. The data will show us what we cannot figure out.

Let me contrast with that viewpoint an example of why causation still reigns supreme over the latent power of "N=all" data bases. If numbers were the ultimate answer to solving problems rather than root cause analysis why is it that the USA has not solved the "benign neglect" collateral damage of our national welfare system that Sen. Daniel Patrick Moynihan warned the country about in 1969 as tech wonk in DC? We have more than enough data to verify his warning being prescient in the over 40 year backwash of statistics chronicling the break down of black family structures. Still, the data is not correcting outcomes like the 70% of black children born without a father in the family. BIG DATA won't slow down the wave of problems eroding our cultural shores unless we, as people, deal with the root causes honestly and forcefully.

We should all be aware of how BIG DATA could end up being a surfer's guide to staying ahead of the waves until everything crashes into the rocky shore awaiting us. Understand and confront it now, if we can.
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on April 5, 2013
I once had a manager whose mantra was, "Take away the matches, don't just put out the fire!", his approach to problem solving. In other words, find the cause of the problem and correct it, don't simply snuff out the flames. When firemen race into a building burning, their first goal is to rescue anyone trapped inside the building; determining the cause of the fire is left to the investigators.
In a "Big Data" world, the building may never have caught on fire in the first place since data from multiple and seemingly unrelated sources could have been analyzed to identify buildings likely to catch on fire. Prediction through correlation, not causation. Once the inhabitants are rescued and the building burns down to the ground, the big data expert would look for data relationships that might help identify other buildings that the fire inspectors should visit sooner rather than later.
Through the use of (better) examples, the authors hammer home the point that Big Data, N=ALL, holds the future as the hidden relationships of data hitherto unseen are slowly being revealed by applying complicated mathematical algorithms by a new breed of analysts. Rather than search for reasons WHY something happened, the freshly minted data analyst will try to understand the WHAT and look for scenarios in which correlated data points to additional potential occurrences.
I have been a data analyst for years and I can vouch for the authors; big data holds a treasure trove of related information that can save lives. The problem is in convincing others that correlation, not causation, is where it's at.
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on December 15, 2013
I purchased this book in order to gain a basic understanding of big data. I had read a few articles on the topic and was curious as to the hype and the implications of big data. This is exactly what Big Data provides and more. Having read this book I feel as if I have a solid understanding of the concept, understand how it has changed, and will continue to change the world, and why it should it matter to me and everyone else in the global economy. The authors do an outstanding job of explaining all of this in terms that even the most unversed individual could understand, all without getting bogged down in technical details. Big Data is perfect for anyone looking for a basic overview of big data, interested in examples as to how big data has improved individual lives and added value to businesses, or just looking for an interesting read.
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on January 21, 2018
I certainly wouldn't say that this book was a waste of time, but it would've served an equal purpose at half the length. I felt like it was written in several parts and then glued together without much review. The examples are incredibly repetitive, and I'm not sure the authors collaborated on topics as several sections repeat in their entirety. I learned a few useful nuggets from the book, but it felt largely muddled together and disjoint.
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