With the recent publication of Novarica’s Analytics and Big Data at Insurers report, it’s time to take an honest look at the state of big data in the industry. One of the most telling–and disappointing–charts showed that of the insurers working with big data, seventy percent were using traditional computing, storage, database, and analytics technology, meaning they’re working with SQL databases in their existing environment. Of all the other technology options (Hadoop, NoSQL, columnar databases, etc) only a small percentage of insurers use them and almost no insurer uses them extensively.

Compare that to the percentage of insurers who say they are using big data sources, which is significantly higher than the percentage of insurers using big data technology. This includes third-party consumer or business data, geospatial data, weather data at the top the list, with audio/video data, telematics, social-media content, and internet clickstreams lagging behind. But what’s really happening when those big data sources are loaded into a traditional structured database? Most likely, a few key elements are pulled from the data and the rest is ignored, allowing the so-called big data to be treated like “small data,” processed along with all the policy, claims, and customer data already being stored. For example, while a weather feed might be coming with minute-by-minute updates, most insurers are probably just pulling region condition along with daily temperature highs and lows, filtering it down to a subset that stores easily. While I’m not saying such reduced data doesn’t augment an insurer’s understanding of incoming claims (for example), it’s far from what we think about when we imagine how a big data analysis of the weather might impact our understanding of insurance claim patterns.

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