The recent English online edition of Der Spiegel has published an interesting elaborate article on a very relevant theme: Big Data. Here are a few excerpts.
Forget Big Brother. Companies and countries are discovering that algorithms programmed to scour vast quantities of data can be much more powerful. They can predict your next purchase, forecast car thefts and maybe even help cure cancer. But there is a down side.
The expression “Big Brother” has become dated. Experts would seem to have reached consensus on the term “Big Data” to describe the new favorite topic of discussion in boardrooms, at conventions like Berlin’s re:publica last week, and in a number of new books. Big Data promises both total control and the logical management of our future in all aspects of life. Authors like Oxford Professor Victor Mayer-Schönberger are calling it a “revolution.” According to Mayer-Schönberger, Big Data, which is also the title of his current book on the subject, will change our working environment and even the way we think.
The most important factor is not the sheer volume of data, even though it is currently growing faster than ever. An estimated 2.8 zettabytes of data were created in 2012. One zettabyte is 1,000,000,000,000,000,000 kilobytes. Experts predict that the volume of new data could increase to 40 zettabytes by 2020. It would take about 250 million DVDs to store the amount of data being transmitted on the Internet in a single day. This volume doubles about once every two years.
New is the way companies, government agencies and scientists are now beginning to interpret and analyze their data resources. Because storage space costs almost nothing nowadays, computers, which are getting faster and faster, can link and correlate a wide variety of data around the clock. Algorithms are what create order from this chaos. They dig through, discovering previously unknown patterns and promptly revealing new relationships, insights and business models.
Though the term Big Data means very little to most people, the power of algorithms is already everywhere.
Google and Facebook are pure, unadulterated Big Data. Their business models are based on collecting, analyzing and marketing information about their users, through advertising tailored as closely as possible to the individual. This gigantic database and the notion of what can be done with more than a billion individual profiles in the age of Big Data was worth at least $100 billion (€78 billion) to Facebook investors.
The prospect of turning their treasure troves of data into dollars is now fueling the fantasies of businesses in many industries, from supermarkets to the automobile industry, and from aviation to banks and insurance companies. According to figures published by industry association Bitkom, global sales related to Big Data applications amounted to €4.6 billion in 2012. That number is expected to increase to about €16 billion by 2016.
Some cities even predict the probability of crimes in certain neighborhoods. The method, known as “predictive policing,” seems like something straight out of a Hollywood film, and in fact it is. In Steven Spielberg’s “Minority Report,” perpetrators were arrested for crimes they hadn’t even committed yet.
Finding the presumed delinquents also doesn’t seem to present a problem. Scientists have figured out that, with the help of our mobile phone geolocation and address book data, they can predict with some certainty where we will be tomorrow or at a certain time a year from now.
The increasing accuracy of such forecasts have led American tech guru Chris Anderson to proclaim that we are arriving at the “end of theory.” Austrian media executive Rudi Klausnitzer, who has just written a book on the subject called “Das Ende des Zufalls” (“The End of Chance”), has reached a similar conclusion.
It is a prospect that is not altogether appealing to some. But many already rely on the prognostic ability of soulless algorithms in the most intimate spheres of life. The extensive questionnaires used by online dating agencies are fed into algorithms designed to increase the probability of finding a compatible partner.
The self-confident founders of Kreditech lend money through the Internet: short-term mini-loans of up to €500, with the average customer receiving €109. Instead of requiring credit information from their customers, they determine the probability of default on their own, using a social scoring method that consists of high-speed data analysis. “Ideally, the money should be in customers’ accounts within 15 minutes of approval. This is already working in Poland,” says Diemer. In return, Kreditech wants as much data as possible from its users. The more information the company gets, the more precise are its predictions and the higher a customer’s potential credit line.
The evaluation profiles of EBay accounts are already publicly accessible. Kreditech also requires access to Facebook profiles, so that it can verify whether a user’s photo and location match information on other social networking sites, like Xing and LinkedIn — and whether his or her friends include many with similar education levels or many colleagues working in the same company.
All of this increases the likelihood that Kreditech is dealing with a real person. Even the question of whether the loan request was submitted from an expensive iPad or a cheap Aldi computer goes into the evaluation.
Business models like Kreditech’s illustrate the sensitivity of the issues that many new Big Data applications raise. Users, of course, “voluntarily” relinquish their data step by step, just as we voluntarily and sometimes revealingly post private photos on Facebook or air our political views through Twitter. Everyone is ultimately a supplier of this large, new data resource, even in the analog world, where we use loyalty cards, earn miles and rent cars.
Perhaps many people do so with so little inhibition because what happens to our data often remains ambiguous. To whom and how often is our data sold? Are these buyers of data also subject to rules for deleting the data and preserving anonymity? And what will happen, for example, with Kreditech’s credit profiles if the small business is ever acquired by a larger company or goes bankrupt?
A study by New York advertising agency Ogilvy One concludes that 75 percent of respondents don’t want companies to store their personal data, while almost 90 percent were opposed to companies tracking their surfing behavior on the Internet.
This conflict explains the heated nature of the current controversy over the proposed new European data protection directive. If the European Commission’s plans, which also include a “right to be forgotten” on the web, become a reality, many providers could see their Big Data growth fantasies in jeopardy. This is one of the reasons Brussels currently faces a barrage of lobbying from the likes of Amazon, Google and Facebook.
But for a modern society, an even more pressing question is whether it wishes to accept everything that becomes possible in a data-driven economy. Do we want to live in a world in which algorithms predict how well a child will do in school, how suitable he or she is for a specific job — or whether that person is at risk of becoming a criminal or developing cancer?
Internet philosopher Evgeny Morozov warns of an impending “tyranny of algorithms” and is fundamentally critical of the ideology behind many current Big Data applications. Morozov argues that because formulas are increasingly being used in finance and, as in the case of Predictive Policing, in police work, they should be regularly reviewed by independent, qualified auditors — if only to prevent discrimination and abuses of power.
Read HERE the entrie article.