Everything happens somewhere: Why combining AI and Location Intelligence is essential for business success

Location intelligence provides another vital tool to organize and analyze vast and complex datasets, using geographic location as the unifying factor

November 4, 2020

4 Min Read

As businesses worldwide have woken up to the potential of data to make their operations smarter, many teams have been left drowning in it – often turning to new technologies, such as AI, to help unlock the insights contained within complex datasets.

While AI has been subject to hype and speculation for many years, recent leaps in computing power, increasingly complex algorithms and a vast array of available data have pushed AI from theory to everyday reality.

In fact, a recent survey by New Vantage Partners found that C-level executives singled out AI as the most disruptive technology —far outranking technologies such as cloud computing and blockchain. And nearly 80 percent of those executives fear that competitors will harness AI to outflank their business.

As well as AI, location intelligence provides another vital tool to organize and analyze vast and complex datasets, using geographic location as the unifying factor. The combination of artificial intelligence and location intelligence can be truly potent – with the ability to uncover business insight and support digital transformation in a rapidly evolving world.

The combination of these technologies is being increasingly applied across multiple sectors, they are being used for everything from drug discovery, to fraud detection, risk assessment, and manufacturing to help with a variety of needs. More broadly, executives are finding insight and the competitive edge from data describing where things happen, why they happen there, and how they can be improved.

Massive amounts of business and customer data are linked to physical locations and times, and thousands of organizations already analyze this location data to uncover hidden intelligence. A combination of machine learning and a geographic information system (GIS)—the backbone of location intelligence—is helping organizations capture, store, and manage vast amounts of data; run robust analysis; and then visualize insight embedded in that data. This enables analysis to be run on location data to automate the prediction, classification, and clustering of data. As a result, machine learning is making location intelligence a powerful force behind critical business decisions and operations.

This technology is helping retailers turn customer data— shared with the user’s permission—into insight that drives personalized experiences with their brand. In one scenario, customers who order a meal using a retailer’s app have their food handed to them at the exact moment they walk into the store. How is that level of precision possible? It is, in part, due to machine learning ’s predictive capabilities combined with location intelligence. By analyzing millions of data points based on customer behavior and location data tracked through the app, ML algorithms can make accurate predictions about when that customer will arrive— without the privacy-infringing practice of individual location tracking.

In addition, forward-thinking retailers are finding ways to tap into the data they need to be able to predict—with high levels of accuracy—what customers are going to demand; when they want it; by what channel; and most importantly, where they want it available. By combining location intelligence and artificial intelligence, companies can bridge the traditional gap between supply chain forecasting and actual consumer demand.

Insurance companies are also combining machine learning and location intelligence to tailor premiums much more accurately than just using basic demographics. One US-based insurance company is experimenting with analyzing data from onboard accelerometers alongside GIS data as cars move through space and time, identifying patterns in driver behavior including safety concerns like texting while driving.

If, for instance, a driver consistently makes small, quick corrections on curved roads, the algorithms may identify that the driver is texting. That behavior identification is based on the analysis of millions of data points from drivers across the country. No texting? That driver will pay a lower premium. The insurance company can calculate individual, behavior-based premiums, rewarding drivers who operate vehicles safely.

It’s clear that AI is already revolutionizing a multitude of business operations, but it hasn’t reached its full potential just yet – and business leaders today still have the ability to make strategic investments in emerging facts of artificial intelligence. The powerful combination of AI and location intelligence can help businesses meet their goals at a fundamental level, and increasingly, leveraging this powerful combination will be fundamental to success.

Charles Kennelly is Chief Technology Officer at Esri UK

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