Machine learning manages complex data while location intelligence gives that data the crucial context of where. Here we explore real-world examples.
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While executives, already working through digital transformation, grapple with pandemic-related recovery issues, they’re leaning on technologies—both proven and innovative—to stay on track.
Most leaders see as crucial and describe a “sense of urgency at the top” to implement it. Yet, they struggle to integrate company-wide initiatives. Seventy-five percent of executives surveyed believe that if they fail to do so, their companies will be gone in five years.
in the business world will likely grow at a steady pace over the next five years, then shoot skyward . By 2030, it’s expected that a majority of companies will use to support and accelerate upper-level guidance and decision-making. Most use cases will involve a powerful form of called , in which computers analyze enormous datasets to help answer questions. But,
Location intelligence, achieved with a geographic information system (GIS), gives businesses the power to map, analyze, and share data in the context of location.
1. Market Analysis, Growth Planning, Advanced Analytics
Machine learning programs find clusters and hotspots in complex datasets. Applying that capability to customer data,
The question of where to site a store, for example, involves a determination of how reachable it is from various parts of the community. Meanwhile, demographic information can reveal hotspots of certain consumer behavior. By analyzing both datasets—potential site reachability and nearby demographics—retailers can better understand which customers will favor certain locations.
Using GIS technology, people have always been able to answer questions quickly, displaying reachability and demographics on maps and dashboards. Now, they can add
This reveals consumer patterns previously unseen and answers important questions. How will the average age of customers vary? How many will come with families? What are the mobility patterns influencing store visit patterns? How many will take public transportation? Should a store open for extended hours on certain days?
As companies increasingly tailor products and services for specific geographic areas, this kind of information helps anticipate and meet customer needs. Machine learning programs can perform advanced analytics, often in real time, to identify patterns in sales data, linking those patterns to location. A program might, for example, help a company discover commonalities among regional demographics such as urban versus suburban, or areas with young families—spotting patterns that might otherwise be missed.
2. Monitoring and Tracking Assets
Machine learning algorithms can be taught to recognize objects and to sort them accordingly. When there is a location component—as there is with most objects—this capability can pay enormous dividends in time and money, especially in an age of drones and satellite imagery.
All businesses have assets that must be tracked and accounted for. Consider the way a utility company used
This concept works for even larger geographic areas. The operator of an oil pipeline uses
Location intelligence with
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