Can AI Help Insurers Leverage Big Data Better?

There’s been a yawning skills gap for many high-level IT and big data skills for some time. Insurers have found themselves scrambling to develop IT and data analytics competencies, finding themselves in competition with companies from many industries outside of insurance and financial services for the talent they need.

IBM, the company that brought you Watson, the artificial-intelligence platform and holder of the Jeopardy crown, thinks it has a solution to the skills gap, at least when it comes to data scientists and analysts: Bring in an AI platform such as Watson to do the heavy lifting.

As Bernard Marr, a Forbes contributor, explains it, “IBM believes that it can offer a solution to the skills shortage by cutting out the data scientists and replacing (or supplementing) them with its Watson natural language analytics platform. Watson and other natural language processing technologies can help to break down the barriers to widespread use of data analytics by making complex analytics possible to just about anyone, regardless of their technical ability.”

Most people lack the essential skills needed to work with analytics, Marr points out. Natural language processing “is teaching computers to accept that input in the natural, spoken language of humans – breaking down the communications barrier between man and machine.

Is it possible for natural language systems – connected to AI on the back end – to make it easy for non-technical business users to sift and sort through big data now as well? As Marr points out, IBM Watson is already doing the work of doctors and medical professionals in some settings, and has applicability in other areas as well.

Big data plays a key role in insurance systems, such as underwriting or fraud detection on the operational side, and customer analytics on the marketing and sales end. But it takes skilled, well-trained people to be able to be able to identify the right data and weave it into actionable intelligence.

In an article in Harvard Business Review a couple of years back, Thomas Davenport called data scientist as “the sexiest job of the 21st Century.” If automation and artificial intelligence systems do take on the work of data work, with job of data scientist also be extinct before the century even reaches its one-quarter mark? Marr observes that such “citizen data scientists” – a term coined by Gartner – are likely to proliferate as a faster rate than data scientists.

Gregory Piatetsky of KDNuggets even ran a poll on this very possibility a few months back, and speculates that data scientists’ jobs could flare out in the next decade. Based on the responses of 255 data professionals, a majority, 51%, feel automation will hit within the next 10 years. Five percent even believe it has already happened.

While enterprises are hungry for talent to bring them into the analytics age, skills shortages have been holding them back. Automation will help alleviate the need for this. However, it’s important to keep in mind that 10 years from now, there will be something else on the tech scene that needs experts to unravel.