Free PDF 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
Free PDF Big Data: A Revolution That Will Transform How We Live, Work, and Think
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Audible Audiobook
Listening Length: 8 hours and 36 minutes
Program Type: Audiobook
Version: Unabridged
Publisher: Recorded Books
Audible.com Release Date: May 30, 2013
Whispersync for Voice: Ready
Language: English, English
ASIN: B00D3MZDDQ
Amazon Best Sellers Rank:
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)
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.
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!"
"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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