BIG. DATA. A Revolution That. Will Transform How We. Live, Work and Think. VIKTOR MAYER-SCHÖNBERGER. AND KENNETH CUKIER. In Big Data: A Revolution That Will Transform How We Live, Work and Think, two of the world's most-respected data experts reveal the reality of. In today's digital age, there is an explosion of data everywhere. Google processes more than. 24 petabytes of data per day. Data bytes are being generated.
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Ian M Dunham. To cite this article: Ian M. Dunham () Big Data: A Revolution That Will Transform How We Live, Work, and Think, The AAG. Viktor Mayer-Schönberger and. PDF | Book review of "Big Data: A Revolution That Will Transform How We Live, Work and Think", by Kenneth Cukier and Viktor Mayer-Schonberger, John. Book Review. Big Data: A Revolution That Will Transform How We Live, Work, and Think. By Viktor Mayer-Schönberger and Kenneth Cukier.
Very interesting book. In the world of big data, the mindset with which researchers approach a dataset can make all of the difference. How can officials identify the most dangerous New York City manholes before they explode? Read more Read less Length: Because big data allows us to analyze far more data, we will move beyond expecting exactness and can no longer be fixated on causation. UPS, the authors report, has fitted its trucks with sensors and GPS so that it can monitor employees, optimize route itineraries and know when to perform preventive vehicle maintenance.
While a missed prediction does not cause much damage if it is about book recommendations on Amazon, a similar error when doing policy making through big data is potentially more serious.
Crawf ord reminds us that Google Flu Trends f ailed because of measurement error. In big data, data are proxies of events, not the events themselves.
Google Flu Trends cannot distinguish with certainty people who have the f lu f rom people who are just searching about it. What matters is the nature of the data points and Google has apples mixed with oranges.
T he traditional image of science the authors discuss f ixated with causality, paranoid about exactitude conf lates principles with practices. Correlational thinking has been driving a lot of processes and institutional behaviours in the real world.
T he authors cannot dismiss causation so cavalierly. However, it appears that they do. T he new algorithmists will be experts in the areas of computer science, mathematics, and statistics; and they would act as reviewers of big data analyses and predictions.
T his book is a shining example that big data speaks the narrative experts give it.
What close observers know is that even at the most granular level of practice, analytic understanding is necessary when managers attempt to implement these systems in the world.
T he book is blinded by its strongest assumption: It is hard to see how imagination and practical context will suddenly cease to play a f undamental role in innovation.
But innovation could def initely be jeopardised if big data systems are not recognized f or what they are — tools f or optimising resource management. Big data may not be an instrument of discovery; while certainly it is a way of managing entities that are already known. Big data promises to be f inancially valuable — because it is primarily a managerial resource e.
However, data scientists have found that even massive error-prone datasets are more reliable than pristine but tiny samples. As we make inroads into big data, we also make an important shift from results that focus on causation to results concerned only with correlation.
Nowadays, that needs to be enough — and it often is for e-commerce companies looking for profit, and doctors looking to save lives — but it also represents a radically different approach to problem-solving than many of us are used to.
Rather than adhering strictly to the traditional scientific method, big data allows us to work backward, first starting with data collection, then analysis and finally drawing conclusions from whatever patterns may appear. This shift away from trying to support or disprove a theory cancels out the possibility of researcher bias, but also lends itself to a directionless investigation, with results subject to the interests of the analysts exploring the data.
Essentially, the only answers that will be found are the ones a researcher chooses to look for. With their Kindle e-book readers, for example, Amazon.
Are You Ready? PART 2: Big Data Analytics: PART 3: Data Analytics and Machine Learning: Tools Get online access For authors.
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