If Big Data is going to remake an industry, insurance is certainly a great candidate. With massive amounts of historical data as well as emerging IoT-based data sources, insurance is well positioned to take advantage of new analytical methods and techniques. Recently, I had a chance to speak to a very large insurance company about their Big Data opportunities and challenges. What made the day unique was my session was followed by a predictive analytics demonstration created by the company’s IT department.
Once the gathered executives saw what was possible with predictive analytics, even in a small scale, they wanted to do more: new cuts of data, additional facets, different questions. But the requests from the invigorated audience reinforced fundamental challenges with Big Data.
First, the data must be correct. This isn’t limited to data quality, but also metadata. Almost any organization is going to have multiple data sources, often for the same data. In the case of this insurer, it has several claims systems, each with different attributes. For example, one claim system has five different categories for marriage status, while another had seven. Inconsistent dates of birth also complicated analysis.
Second, IT and the business must be partners. For the purposes of the demonstration, IT simply picked what they thought was an interesting problem and they picked correctly. After that, the executives started asking for more things from the IT team – without any consideration for the work that must happen from the business side. The executives believed they could simply request predictive insights from IT in the same way they ask for new descriptive analytics reports.
Without meaningful collaboration and investment from the business side, in the form of people, process and data, Big Data initiatives will fail. And they will fail quite spectacularly.
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