As the Internet of Things (IoT) expands toward 21 billion devices in 2020, the volume of location data, along with all other data types, will only increase. The move toward a world of connected cars, smart devices, and intelligent sensors will create high-value markets focusing on spatial analysis and geo-analytics. After all, this is the key premise of IoT: linking devices, humans, and networks all together to give consumers more knowledge and control.
Let’s remember that geo-analytics is not just the holy grail of our “Internet of everything” future. In the past couple of years, the insight value of location data has been extremely important to many other business verticals. Take, for example, location-based targeting in advertising: Juniper Research predicts that revenue generated by location-based services will reach $43.3 billion by 2019. This will drive the introduction of more intelligent devices, vehicles, and sensors that produce data at a much higher rate than we have ever seen; nearly all of that data generated will have a spatial element to it.
Today’s market leaders are using geospatial analytics to draw location-based insights and optimize workflows, reduce costs, manage risk, and optimize the customer experience. Geospatial data is providing organizations with opportunities to utilize fast data processing and analytics to make highly differentiated, insight-driven decisions. Let’s consider some of the key use cases in which the combination of geospatial data and real-time analytics can provide significant value:
Financial services. Fraud detection is a billion-dollar problem in finance, affecting consumers and banks alike. Geospatial data can help financial institutions detect and prevent fraud by correlating spatial, temporal, and transactional data altogether using predictive analytics and anomaly-detection techniques.
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Retail. In the age of omni-channel retailing and hyper-personalization, geospatial is the missing link that provides customers with a seamless and converged experience between offline channels (in-store purchases, smart tags) and online channels (web, mobile). Adding geospatial analytics to the personalization across channel interactions can help retailers optimize their promotional activity based on customer locations.
Insurance. High quality geospatial data is critical to the insurance industry, as risk is often tied to location. Insurers can perform risk simulations against vast amounts of data to come up with the right risk model. This helps insurers better estimate potential losses and help customers to purchase the correct amount of cover at the right price.
While the above applications of geospatial intelligence seem diverse by virtue of business vertical, they all share one common trait: the value of their generated data is perishable within seconds, if not less. It goes without saying that, for location information to be valuable, it must be processed immediately.