- Capa comum: 252 páginas
- Editora: Eamon Dolan/Houghton Mifflin Harcourt; Edição: Reprint (4 de março de 2014)
- Idioma: Inglês
- ISBN-10: 0544227751
- ISBN-13: 978-0544227750
- Dimensões do produto: 13,5 x 2 x 20,3 cm
- Peso de envio: 227 g
- Avaliação média: 1 avaliação de cliente
- Lista de mais vendidos da Amazon: no. 59,801 em Livros (Conheça o Top 100 na categoria Livros)
Big Data: A Revolution That Will Transform How We Live, Work, and Think (Inglês) Capa Comum – 3 mar 2014
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Illuminating and very timely . . . a fascinating and sometimes alarming survey of big data s growing effect on just about everything: business, government, science and medicine, privacy, and even on the way we think. New York Times
It seems like big data is in the news every day, as we read the latest examples of how powerful algorithms are teasing out the hidden connections between seemingly unrelated things. Whether it is used by the NSA to fight terrorism or by online retailers to predict customers buying patterns, big data is a revolution occurring around us, in the process of forever changing economics, science, culture, and the very way we think. But it also poses new threats, from the end of privacy as we know it to the prospect of being penalized for things we haven t even done yet, based on big data s ability to predict our future behavior. What we have already seen is just the tip of the iceberg.
Big Data is the first major book about this earthshaking subject, with two leading experts explaining what big data is, how it will change our lives, and what we can do to protect ourselves from its hazards.
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, boingboing.com
[AU PHOTO] VIKTOR MAYER-SCHONBERGER is Professor of Internet Governance and Regulation at the Oxford Internet Institute, Oxford University. A widely recognized authority on big data, he is the author of over a hundred articles and eight books, of which the most recent is Delete: The Virtue of Forgetting in the Digital Age.
[AU PHOTO] KENNETH CUKIER is the Data Editor of the Economist and a prominent commentator on developments in big data. His writings on business and economics have appeared in Foreign Affairs, the New York Times, the Financial Times, and elsewhere.
Sobre o Autor
VIKTOR MAYER-SCHÖNBERGER is Professor of Internet Governance and Regulation at the Oxford Internet Institute, Oxford University. The co-author of Big Data: A Revolution That Will Transform How We, Live, Work, and Think, he has published over a hundred articles and eight other books, including Delete: The Virtue of Forgetting in the Digital Age. He is on the advisory boards of corporations and organizations around the world, including Microsoft and the World Economic Forum.
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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.
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.