Big Data and Big Business

brain

Big Data can help big business gain new customer insights. It could also have a major impact on improving health, as several Israeli companies have started to demonstrate.

NeuroSteer, a young Tel Aviv company founded by Nathan Intrator, a professor at both Tel Aviv University’s Blavatnik School of Computer Science and Sagol School of Neuroscience, makes technology that enables the collection and interpretation of vast amounts of brain activity data. Mathematical algorithms, combined with machine learning and data mining, refine brain activity interpretation, for accurate continuous monitoring and more precise individualized medical and wellness applications.

FDNA, a New York City-based company founded by Israelis, has developed Face2Gene, a genetic search and reference mobile app, powered by Facial Dysmorphology Novel Analysis technology, that allows medical professionals to detect — by scanning someone’s face — whether he or she has certain genetic anomalies.

Detecting Cancer

Medial CS, founded by Israeli machine learning specialist Nir Kalkstein, has helped develop a new test, called “Colon Score,” which uses an algorithm that analyzes blood tests and demographic data to predict which patients are at risk of developing colorectal cancer. Israel’s Health Ministry recently approved the test, and it has been registered for U.S. Food and Drug Administration approval.

Just as machine learning and Big Data are helping extract more information from facial dysmorphology and the blood, NeuroSteer is trying to to extract more information from brain signals than was possible in the past in order to provide a more detailed “mirror” to the brain, says Intrator. To that end it has created a headband-like device that uses a mathematical formula to slice and dice brain waves in new ways. And because it only needs two electrodes instead of the traditional 32 the head hardware is much less intrusive and can be more easily worn for long periods of time, which is key to continuous data collection, Intrator says.

“The data is processed in real time and a high-level interpretation of brain activity can be provided in real time to a wide spectrum of applications,” says Intrator.

Easy To Do Data Collection

Applications include “Emochat,” which allows people to gauge the emotional response of someone reading an email or text message from a distance. Another application — which won first prize at a recent hackathon — used NeuroSteer’s technology combined with a Comigo TV box to recommend TV programs based on a viewer’s emotional response to previous programming.

“We can collect data continuously from a large number of people because our device makes it very easy to do the data collection,” says Intrator. “We are collecting huge amounts of data and that data can provide real-time high-level interpretation, but in addition it can potentially provide predictions or detect correlations via Big Data mining.”

That is where medical applications come in. “This is the era of sensors. Now we are obtaining sensory information that is related to health — everything from genomic analysis to physical monitoring by devices like Fitbit, to the intake of medications,” says Intrator. “Vast amounts of patient data is being collected. Of course the data has to be stored in a HIPAA [U.S. Health Insurance Portability and Accountability Act]-compliant way and most importantly data mined and analyzed so that deductions can be obtained.”

NeuroSteer says it intends to use technology from Russian search giant Yandex to crunch the data it collects.Yandex last year entered the big data mining space — in competition with companies like Amazon.

Mimic Human Bias

“Yandex provides computational power and storage — it provides a series of optimized libraries to do this kind of data mining in a cost-effective way,” says Intrator.

Analyzing algorithms can change the lives of millions of people but there are ethical and technical questions that businesses and government agencies should be aware of before incorporating Big Data analysis into their offerings. For example, businesses try to use insights to influence people, whether they be customers or employees, but there could be a backlash if they try to influence certain groups in one way and other groups in another.

Likewise, it could prove controversial if they charge different people different prices. There is the risk that “pattern recognition tools that mimic human expertise will also mimic human biases,” says Internet guru Esther Dyson. Then there are questions about what to do with too much knowledge — “either about things you can’t change or probabilities of, say, committing a crime when you can’t discriminate against people who have not (yet) done anything bad,” she says. And what about people at risk for diabetes. Should they be forbidden from buying sugar-laden drinks or be charged more for them?

An October 8th conference in Berlin sponsored by Yandex Data Factory will attempt to tackle some of these questions. The conference will be moderated by Dyson, a board member and investor in Yandex, and include speakers from aircraft manufacturer Boeing, insurance company AIG, German market research institute GfK, financial services company Allianz, rental car company Sixt and Deutsche Telekom, as well as NeuroSteer.

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