The benefits of the Internet of Things (IoT) are usually illustrated by isolated, one-off examples. For instance, if a farmer has a sensor telling them the moisture level in the soil, the data stream can be linked to the irrigation system to enable optimum watering.
But it's not so simple. An interesting article by Matteo Luccio explores how multiple sensor streams create complexity – and confusion. He says: “This is a critical problem for many users, most notably war fighters, who need to make rapid and correct tactical decisions. Increasingly, sensor fusion algorithms are merging and analyzing geospatial data to help extract actionable intelligence.”
Luccio cites a company using fusion algorithms to calculate the best sites for solar panels in the US: “Using a single data source for the entire United States – say a digital elevation model from which one can extract slope and aspect – one might find that locations which are both flat to mildly sloped and face south are the best for solar panels. One could refine the results by adding weather data, such as rainfall, humidity and temperature, and by calculating the mean incident radiation across the country to find the regions most exposed to solar energy.”
Now, this looks to me an awful lot like what insurers do when they model risk. (It's also a different situation from that of the soldier, because it's not real time.) Insurers have the skills and capabilities to become “fusion engineers”. As the world about us becomes ever more intelligent, with data arriving from multiple points in real time, risk assessment and coverage must become more adaptive. Insurers will want to look carefully at the techniques and tools developed by teams working with fusion algorithms in different industries. Exogenesis