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Predictive analytics is quickly becoming one of the most powerful tech tools we have at our disposal, and in the short-term future, it seems obvious how it will work its way into our existing systems. But where does it go from there? How could predictive analytics evolve over the next decade and beyond, and what should we be doing to prepare for that transformation?

Applications

For starters, we’ll start to see predictive analytics applied to more industries, and in more innovative ways:

  • Medical. We’ll see a major breakthrough in predictive systems applied to the medical industry, with data from twin studies and large-scale studies fed into complex algorithms that can account for variables like age, demographics, family history, and current systems. Ultimately, these algorithms may be able to diagnose and/or treat patients better than human doctors.
  • Entertainment. Have you ever been borderline insulted by the movies that Netflix recommended to you, supposedly based on your past tastes? Algorithms of the future will be far more accurate, recommending movies, TV shows, and music you may never have considered, but have a good chance of falling in love with.
  • Social. Speaking of falling in love, brands like OKCupid have made a name for themselves by supposedly coming up with a formula for love—but in the future, that formula could become shockingly accurate. We may even see algorithms that can predict the value, health, and life expectancy of friendships based on how we interact online.
  • Financial. We can’t forget the financial industry. Smart algorithms may be able to predict financial mistakes before they happen, and recommend the perfect financial plan for almost any individual.
  • Marketing. And of course, we’ll see tons of development for predictive analytics in marketing and advertising—that’s where the money is. In the future, we may be able to serve personalized content to people no matter where they are, including customized billboards and magazine ads in addition to online video ads.

Reliability

It’s also all but certain that the reliability of our algorithms is going to improve. Our modern machine learning algorithms are somewhat limited—though they may progress iteratively for the next few years, we’ll be in need of a fundamentally new path of development to see a real breakthrough. When that happens, we should gain access not only to better, more accurate algorithms, but systems that can quickly learn from their mistakes and update themselves on the fly.

Add to that the reality that our data are going to improve, both in quantity and in quality. More than 90 percent of the current data in existence has been created in the past few years, and our rate of production is only going to grow; with all that data available to factor into predictive analytics equations, we’re going to get a clearer picture of what’s going on in any scenario. It also helps that these data will also be a higher quality, with more accurate measurements and more serious quality control efforts within businesses.

Accessibility

Finally, we’ll see a revolution in accessibility. Today, the best predictive analytics algorithms are proprietary and held by major corporations. It won’t be long until those algorithms become open source, and more programmers become skilled in creating their own predictive systems. When that happens, it will be cheaper and more efficient for businesses of all shapes and sizes to start using predictive analytics, and its use will become even more widespread.

Until then, we already have some impressive predictive analytics tools to work with. If you’re interested in trying SAP’s predictive analytics software, you can learn more about it here.

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