BIG DATA VIKTOR MAYER-SCHONBERGER PDF

Google a. Google Flu Trends Let us begin with the information giant Google. As mentioned in the introduction, Google was able apply big data to search terms run through its search engine to track the spread of the H1N1 pandemic in in real time. First, researchers at Google obtained data from the Center for Disease Control and Prevention CDC regarding the spread of the seasonal flu between and loc. Next, the researchers took the 50 million most common search terms punched into their search engine, and traced where and when they were punched in during the flu seasons between and loc.

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Google a. Google Flu Trends Let us begin with the information giant Google. As mentioned in the introduction, Google was able apply big data to search terms run through its search engine to track the spread of the H1N1 pandemic in in real time. First, researchers at Google obtained data from the Center for Disease Control and Prevention CDC regarding the spread of the seasonal flu between and loc.

Next, the researchers took the 50 million most common search terms punched into their search engine, and traced where and when they were punched in during the flu seasons between and loc. The researchers then plugged both data sets into their computers to look for correlations. Then, in , when the H1N1 flu pandemic struck, Google used its predictive model, together with the search terms that were being registered in its search engine in real time, to track the spread of the virus loc.

It worked like a charm. Now, the H1N1 virus did not turn out to be as deadly as many predicted. Nevertheless, there is no guarantee that we will be so fortunate with future flu pandemics. Google Translate Another area wherein Google has made use of bid data is in its language translation service. In order to build its translation service called Google Translate , Google tracked down billions of pages of translations into or from English [loc. In went corporate websites in multiple languages, identical translations of official documents, and reports from intergovernmental bodies like the United Nations and the European Union.

Google then crunched all the data to find out which words were being translated into which other words taking into account which words were nearby , to come up with a statistical formula that would allow uploaded text to be translated in the most appropriate way loc. The result? It worked! Indeed, Google Translate represents a vast improvement over previous efforts to translate documents algorithmically loc.

And it is far, far richer. It works well because its creators… fed in more data—and not just of high quality. However, the project deserves some attention on its own. To begin with, the project, which began in , ambitiously aims to scan pretty well every book ever written not under copyright law , and put them all up on the internet so that they may be accessed by anyone loc.

The project has already made a good amount of progress. However, Google is not only scanning these books. In other words, Google is not just digitizing the books, it is datafying them loc.

Altogether, these services are of great use to many of us—and society as a whole. But it is not the case that Google exists entirely for altruistic purposes.

Indeed, much of the information that Google collects to fuel its informational services and much of the information that it collects in our use of these services is also being used by Google for profit-making endeavors. For example, the information that Google is collecting with its Street View cars to help perfect its Google Maps service is also been used by them to help design their driverless car technology loc.

While driverless cars may still be some way off in the future, they have already gained some acceptance, and are almost certain to one day be an important part of everyday traffic. The potential profits in this industry are enormous, and no one will be better positioned than Google to reap the rewards. And this brings us to how Google is making the bulk of its profits in the here and now.

Actually, using big data for advertising and marketing purposes is currently one of its most prominent and profitable uses loc. Now, the authors do not touch on this topic much with regards to Google; however, they do address it via other companies such as Amazon.

It is to this topic that we will turn to now. Funnily enough, Amazon did not start out as a big data user. When Amazon. Specifically, rather than recommending books to customers based on their similarity to other customers, Linden and a few colleagues designed some software that could identify associations between the books themselves loc. The modification turned out to make a big difference, and improved the system greatly loc. The data-derived material generated vastly more sales.

The computer may not have known why a customer who read Ernest Hemingway might also like to buy F. Scott Fitzgerald. The cash register was ringing. Eventually the editors were presented with the precise percentage of sales Amazon had to forgo when it featured their reviews online and the group was disbanded. For Netflix, an online film rental company, three-fourths of new orders come from recommendations. Facebook is a unique case in point here, as it has access to rather unique information.

Target As you will have noticed, some types of targeted advertising are a little sneakier and creepier than others. They noticed that these women bought lots of unscented lotion at around the third month of pregnancy, and that a few weeks later they tended to purchase supplements like magnesium, calcium, and zinc. The correlations even let the retailer estimate the due date within a narrow range, so it could send relevant coupons for each stage of the pregnancy.

Indeed, to begin with, big data is also being used by businesses to help increase efficiency, as well as safety.

In Factories and Refineries Beginning with manufacturing, many companies have begun using bid data in their factories and refineries in order to streamline their operations, and improve safety. A major part of this process begins with the application of sensors to machinery and infrastructure. When the information from these sensors is crunched and analyzed, it helps pick up on trouble spots early, before they lead to full on break-downs.

Once the pattern has been recognized something that computers themselves can be designed to do , maintenance crews can then step in to fix the imminent break-down before it occurs loc. This practice not only improves efficiency, of course, but can also increase safety. Outside of the Factory Big data can also be used to help increase efficiency and safety outside of the factory. A good case in point here comes from UPS.

As mentioned in the introduction, UPS has managed to use big data algorithms to help it identify more efficient routes for its trucks. These routes have also proved to be safer. Similar to the approach mentioned above, UPS has placed sensors on many of its truck parts in order to help them identify potential break-downs before they occur. So to be cautious, UPS used to replace certain parts after two or three years.

But that was inefficient, as some of the parts were fine. Big Data and Start-Ups a. Farecast Aside from helping already established businesses improve their efficiency and safety or target advertisements to potential customers , big data is also being used to launch businesses that never would have been possible without it.

Take Farecast, for example, a business that predicts the cost of airfare tickets, saving its customers a bundle, and earning itself a tidy profit in the process. He had assumed that the earlier you bought, the more you would save; but, after a few informal surveys, he discovered that this was not at all the case much to his chagrin—Etzioni had long based his purchases of airfare tickets on this assumption [loc.

Sensing a business opportunity, Etzioni—a man already well familiar with big data loc. Specifically, Etzioni began collecting data on the price of airfare tickets which he obtained from travel websites [loc. He then had his computer analyze how the prices changed over time on the run up to the actual flight. Subsequently, he used this information to build a model that could predict the ever-changing prices of future flights. And lo and behold, it worked! The model had no understanding of why, only what.

It based its predictions on what it did know: probabilities gleaned from the data about other flights. Having proved to himself that his idea could work, Etzioni decided to take his idea which he now called Farecast to the market. This time, though, Etzioni upped the ante. Rather than limiting himself to airline prices lifted off of travel websites, Etzioni went to the source. With that information, the system could make predictions based on every seat on every flight for most routes in American commercial aviation over the course of a year.

Farecast was now crunching nearly billion flight-price records to make its predictions. Nevertheless, Etzioni recognized that his idea could be used for far more than just airfare tickets. So Etzioni let Microsoft have Farecast, but he took his idea with him, and immediately started another business that was designed to do for all manner of consumer goods what Farecast had done for airfare tickets. So confident is the company, that in cases where its predictions prove incorrect, Decide.

As we discover more and more ways to datify the world, and as the data continues to stream in, the business opportunities will only multiply. Here is a brief segment about the uses and potential of big data in business: 7. Health Care a. Big Data in the Hospital One of the more promising uses of big data is in the area of health care. We have already mentioned above how Google was able to use search engine information to help track flu pandemics in real time.

However, the uses of big data in health care go far beyond this. To begin with, our ability to measure anything and everything in the human body including the health of its systems is increasing rapidly, and the amount of data coming out of these measurements is enormous. In the past, most of this information was simply thrown out. Today, though, efforts are underway to keep the information and mine it for insights. For example, IBM recently teamed up with researchers at the University of Ontario Institute of Technology to design software that can crunch and analyze physiological readings from premature babies.

While the project is working specifically with premature babies, there is no reason to think that the strategy cannot be extended to other patients as well loc. Big data has also been used in a similar way to help reduce hospital readmission rates a very costly phenomenon.

Somewhat unexpectedly, the crunched data revealed that one of the most prominent predictors for hospital readmission was mental distress even when the specific ailment being treated had been strictly physiological in nature loc. Big Data in Genomics At an even deeper level, big data is also being applied to our genomes.

This process began when the human genome was fully sequenced in Despite the success of 23andMe, the company has, until now, operated under a significant limitation. Meanwhile, billions of base pairs of DNA remain unsequenced. A person of means can already have this done. For example, many are aware that Steve Jobs endured a long and arduous battle with cancer.

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The key intuition that this book is highlighting is a shift towards greatly increased production of data and greatly increased use of large nearly complete population levels data sets in the management and control of a range of industries. This change is fueled by the wide adoption of broadband internet services and significant increases in data processing capacities in pcs, laptops, and tablets. The key point of distinction is that when you can access nearly all of the data in a situation in real time, analysis approaches and techniques change and the uses of data analysis greatly increase. The authors are from Oxford and "The Economist" magazine and appear to be experts, even though they claim in the book that experts will decline in importance in the big data sector. This really is a change that is being discussed and business and engineering schools across the US are hurrying to catch up with and capitalize on it. Having said that

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Big Data: A Revolution That Will Transform How We Live, Work, and Think

By Michiko Kakutani June 10, Google does it. Amazon does it. Walmart does it. And, as news reports last week made clear, the United States government does it. Does what? Amazon uses customer data to give us recommendations based on our previous purchases.

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Knowing sensitive facts about one person, or a dozen, may be trivially useful. But analyze the same facts about million people, and you can cure diseases, win elections, or earn billions of dollars, because unpredictable insights emerge when you turn computers loose on vast storehouses of information. My favorite involves the giant retailer Walmart and the notorious breakfast snack Pop-Tarts. Walmart records every purchase by every customer for future analysis. Company analysts noticed that when the National Weather Service warned of a hurricane, Walmart stores in the affected area would see a surge in sales of Pop-Tarts. So store managers were told to put their Pop-Tarts near the entrance during hurricane season, and sales soared. This is big data at its coolest.

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