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Predictive Analytics Goes from the Minority to the Majority

Advances in machine learning and big data are making possible the kind of predictive analysis chronicled in sci-fi classic Minority Report.

Steven Spielberg’s 2002 thriller, Minority Report, centers an entire universe of speculation around fate and predestination. Tom Cruise plays police chief John Anderton, who oversees a department called PreCrime, which uses gifted humans called precogs to predict — with chilling accuracy — who will commit crimes before they actually happen. Everything changes for Anderton when he receives a report that he will murder someone: Cracks in the precog system appear as Anderton goes rogue and tracks down an alternative prediction that he won’t kill anyone at all.

We’re now living in Anderton’s dystopian moment. Today corporations in every sector, from finance to education, are using a set of technologies called predictive analytics: Machine learning, big data and related forms of data analysis have given us the power to know more about the future — or, more precisely, the probability of certain future outcomes — than ever before. But the story doesn’t end there. As recently reported, Los Angeles County launched a pilot program “analyzing data about a child’s family, arrests, drug use, academic success, and abuse history” to determine which children were more likely to commit crimes. Although the program ended in 2014, PreCrime chief Anderton would be impressed.

Questions have already arisen about the legal use of predictive analytics. Especially in a justice system dependent on presumed innocence, no one can be arrested before they are guilty. But whatever else they suggest — and perhaps even because they are so important to the changing world in which we all live — these developments mean smart investors ought to watch the companies that make and use predictive analytics of any kind.

There are many entries here: the platforms that gather personal data, the storage companies that house such data, the start-ups and established players that provide analysis and related services like visualization and sentiment analysis, and their clients (those who hope to earn a competitive edge from its use).

In the first category — let’s call them the gatherers — we find just about every app for mobile, every consumer touchpoint that can be equipped with a computer or a sensor. The most obtrusive examples are Facebook, whose $5 billion–plus fourth-quarter earnings made news inside and outside of tech, and Twitter, though the latter company has been reported to stumble this year. Gatherers profit from the sale of data to marketing research firms and insurers — or from the secondary sale of those data as advertising space perfectly targeted based on location, demographic, political sentiment and so on.

In the second category — the storers — are providers like New York–based MongoDB, which provides flexible solutions for managing and accessing big data in real time. And in the third category — the analyzers — are entities as diverse as social services and financial technology. Consumers, too, will eventually make use of predictive analytics, likely through technology movements such as quantified self, which helps recommend diet and exercise based on perceived patterns.

The ramifications of the technological and societal shift promised by predictive analysis could open up opportunities almost anywhere, though the process might be fruitfully illustrated by pointing to a specific example. We might take the Boulder, Colorado–based start-up not coincidentally known as Precog. Before its sale, Precog built a programming environment for the radically simple analysis of big data, including for predictive purposes, and allowed for rapid, real-time combinations of various forms of predictive analysis involving machine learning and sentiment analysis.

In 2013 Precog was acquired by a San Francisco–based company called RichRelevance, which uses its data analysis engine to drive personalized experiences. As retailers go head to head over a dwindling number of in-store customers, those that excel at presenting the right products and experiences — tailored to each client — might have the best chance at retaining and deriving more lifetime value from each of those customers.

RichRelevance uses machine learning to understand what purchases are probable and which techniques are most likely to keep customers buying at every point in the sales funnel. Although RichRelevance’s bet on retail may or may not be a good one, companies that know how to increase engagement, recommendations and all the rest of the tactics predictive analysis unlocks will be better positioned to turn in strong profits.

By examining business-to-business connections involving predictive analysis, sharp-eyed observers might spot new opportunities. All along the way — from the data that organizations collect themselves or buy from other companies to the tools they use to understand those data — there are access points to a more and more exciting sector.

There are, too, those questions about what to do when we can predict what people will do much better than they can. It’s worth remembering that the precogs in Minority Report are people, and mutated people in a pool of goo to boot. But as machines get better at thinking — at achieving results like those we’ve seen in the movies — we’ll need to think about what we want them to be able to do and invest accordingly. That’s what investing does. It’s a tool we use to make the futures we want come to be.

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