3 Steps to Big Data Success
Insurance Experts' Forum, March 20, 2013
Progressive Insurance was in the spotlight at a recent IDC presentation. As reported by CIO's Chris Kanaracus, Progressive's telematics program for customers was held up as a successful example of big data analytics.
What is it about Progressive's telematics program that makes it stand out as a premier big data initiative, setting it apart from the rest? First and foremost, the program has a direct business connection—big data is being used to create an unique value proposition, and draw in new customers. Unlike many big data initiatives, it isn’t a mysterious quant exercise taking place in a back room somewhere.
“Using detailed information about customers' driving habits, the insurer has created a usage-based model that defines a policy's price down to the individual,” says IDC analyst Michael Versace. “If the data shows a customer is driving safely, they can get significant discounts on their insurance.”
For Progressive, big data means more business, simple as that.
The analysts also outlined the key steps needed to take full advantage of opportunities in the big data realm:
Explore with an open mind: First, noodle around with all the components that go into the big data space. “Identify opportunities to use their existing technology and data in new ways, evaluate public cloud and open-source options, and start experimenting with proof-of-concept exercises and prototypes,” the analysts say.
Start small and work incrementally: Then, pick out smaller projects that will deliver quick, early successes. This will lay the groundwork and enthusiasm for larger analytics projects.
Build a skills base: Along the way, it's critical to start either training current staff in big data tools, methodology and thinking, or hiring those that are familiar with it. However, this is a challenge that many dampen big data efforts both inside and outside the insurance industry, Versace cautions. There's a looming shortage of IT and data professionals with the skills to develop and maintain big data analytics applications.
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