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Computer Grading The approach led him to his biggest triumph to date. The challenge was to write a program that could automatically and reliably evaluate student essays -- essentially a grading machine.

Two professors, computer scientist George Mohler and anthropologist Jeffrey Brantingham, who specializes in crime scenarios, were instrumental in developing the predictive method of fighting crime. Their program is based on models for predicting the aftershocks of earthquakes.

The two data experts, Mohler and Brantingham, have since started a company and are marketing their product, Predictive Policing, worldwide

"Security is one of the biggest growth areas for Big Data applications," says Schröder. In addition to crime and terrorism, Splunk focuses on the growing number of attacks in, and by means of, the Internet and its software can detect hacker attacks or other cyber attacks. "We are positioning ourselves for an expanding cyber war," Schröder says. But the data hunters' new war also has many civilian aspects.

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.

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. Credit card companies can quickly recognize unusual usage patterns, and hence automatically warn cardholders when large sums are suddenly being charged to their cards in places where they have never been. Energy companies use weather

According to official figures, since the Swedish capital Stockholm began using algorithms to manage traffic, drive times through the city's downtown area have been cut in half and emissions reduced by 10 percent. Online merchants have recently started using the analyses to optimize their selling strategies. The widespread phrase "Customers who bought this item also bought …" is only one example of the approach.

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.

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.

So far, many companies have tried to dispel such fears by noting that the data they gather, store and analyze remains "anonymous." But that, as it turns out, is not entirely accurate, in that it sells the power of data analysis radically short. Take the analysis of anonymous movement profiles, for example. According to a current study by the online journal Scientific Reports, our mobility patterns are so different that that they can be used to "uniquely identify 95 percent of the individuals." The more data is in circulation and available for analysis, the more likely it is that anonymity becomes "algorithmically impossible," says Princeton computer scientist Arvind Narayanan. In his blog, Narayanan writes that only 33 bits of information are sufficient to identify a person.

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.

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?