Machine learning manages complex data while location intelligence gives that data the crucial context of where. Here we explore real-world examples.
copyright by www.forbes.com
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 artificial intelligence as crucial and describe a “sense of urgency at the top” to implement it. Yet, they struggle to integrate company-wide AI initiatives. Seventy-five percent of executives surveyed believe that if they fail to do so, their companies will be gone in five years.
AI 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 AI to support and accelerate upper-level guidance and decision-making. Most use cases will involve a powerful form of AI called machine learning, in which computers analyze enormous datasets to help answer questions. But, AI alone is not enough. Executives are already seeing that machine learning programs require real-world context to connect AI with the physical world, and that bridge is location intelligence (LI).
Location intelligence, achieved with a geographic information system (GIS), gives businesses the power to map, analyze, and share data in the context of location. AI machine learning with LI enables trend-spotting and prediction to support market assessment, site selection, risk management, asset tracking, and other core business needs. In short, machine learning manages complex data while location intelligence gives that data the crucial context of where. Here we explore real-world examples.
1. Market Analysis, Growth Planning, Advanced Analytics
Machine learning programs find clusters and hotspots in complex datasets. Applying that capability to customer data, AI and LI can unlock patterns and trends that help businesses understand their markets.
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 AI workflows by tapping into data sources like consumer mobility and purchase patterns.
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 AI and LI to maintain the health of its network. Drones were trained to take thousands of geotagged photos of utility lines. These points became a layer on a smart map. A computer, using deep learning—a complex form of machine learning—analyzed the images and highlighted the assets in need of repair. In the case of one utility company, the system quickly analyzed 17,000 miles of wire, a task that would otherwise take workers an estimated 50,000 hours.
This concept works for even larger geographic areas. The operator of an oil pipeline uses deep learning to detect any structures built too close to it.
Location intelligence with machine learning can provide the kind of visualization that helps businessess better understand their markets. A program that can count the number of vehicles in a parking lot—and classify them by model—could help a retailer gather demographic intelligence about a competitor’s customer base. An oil-and-gas company could quickly discover where in the world others are drilling. An insurance company might use a a combination of LI and AI to better understand liability in a neighborhood by quickly determining how many homes have swimming pools or other notable features.[…]
read more www.forbes.com
Machine learning manages complex data while location intelligence gives that data the crucial context of where. Here we explore real-world examples.
copyright by www.forbes.com
Most leaders see artificial intelligence as crucial and describe a “sense of urgency at the top” to implement it. Yet, they struggle to integrate company-wide AI initiatives. Seventy-five percent of executives surveyed believe that if they fail to do so, their companies will be gone in five years.
AI 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 AI to support and accelerate upper-level guidance and decision-making. Most use cases will involve a powerful form of AI called machine learning, in which computers analyze enormous datasets to help answer questions. But, AI alone is not enough. Executives are already seeing that machine learning programs require real-world context to connect AI with the physical world, and that bridge is location intelligence (LI).
Location intelligence, achieved with a geographic information system (GIS), gives businesses the power to map, analyze, and share data in the context of location. AI machine learning with LI enables trend-spotting and prediction to support market assessment, site selection, risk management, asset tracking, and other core business needs. In short, machine learning manages complex data while location intelligence gives that data the crucial context of where. Here we explore real-world examples.
1. Market Analysis, Growth Planning, Advanced Analytics
Machine learning programs find clusters and hotspots in complex datasets. Applying that capability to customer data, AI and LI can unlock patterns and trends that help businesses understand their markets.
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 AI workflows by tapping into data sources like consumer mobility and purchase patterns.
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 AI and LI to maintain the health of its network. Drones were trained to take thousands of geotagged photos of utility lines. These points became a layer on a smart map. A computer, using deep learning—a complex form of machine learning—analyzed the images and highlighted the assets in need of repair. In the case of one utility company, the system quickly analyzed 17,000 miles of wire, a task that would otherwise take workers an estimated 50,000 hours.
This concept works for even larger geographic areas. The operator of an oil pipeline uses deep learning to detect any structures built too close to it.
Location intelligence with machine learning can provide the kind of visualization that helps businessess better understand their markets. A program that can count the number of vehicles in a parking lot—and classify them by model—could help a retailer gather demographic intelligence about a competitor’s customer base. An oil-and-gas company could quickly discover where in the world others are drilling. An insurance company might use a a combination of LI and AI to better understand liability in a neighborhood by quickly determining how many homes have swimming pools or other notable features.[…]
read more www.forbes.com
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