Did you ever throw a dart at a map on the wall as a random way to pick your next travel destination? Then you already get the concept of geo-mapping.
Simply put, geo-mapping is a type of data visualization tool that lets you make location-dependent decisions based on business intelligence and geospatial data. It enables you to visualize any locale in the world with assets, regions, and KPIs all shown together to help answer the “where” questions – even drilling down from global territory to street level. Where are the best hot spots for outdoor advertising based on drive time to your branch locations? Where is the highest concentration of risk in the supply chain? Where should a new franchise be located in a certain town based on demographics, competitors, and vehicular and pedestrian traffic patterns? Essentially, geo-mapping enables you to recognize spatial patterns, trends, and potential. Let’s take a look at how that works.
Essentially, geo-mapping – also known as “location intelligence” – takes relevant data from an integrated information source, typically business warehouse software, and overlays it with cartographic tools for spatial representations and geographic analysis and forecasting. (The technical term for this is “geo-coding.”) If the information source is, for example, customer-related factors like customer addresses, branch locations, sales areas, and marketing targets, this information can essentially be viewed and analyzed in a map format. The map might be presented together with a bubble chart or a graph in an interactive dashboard; you can pan, zoom, select, and spatially search and interpret reports.
And as with all modern data visualization software, you can use simple point-and-click to organize and consolidate information however you want it, and create clear, colorful graphics to convey and share knowledge. It’s all plug and play, with no coding required – as simple as throwing a dart at a map, and a whole lot more sophisticated.
Geo-mapping in action
But how can you use it for analysis? Let’s say you want to understand why sales on the Upper West Side are lower than those in Lower Manhattan, despite a higher number of customers and branches uptown. Try layering in competitive sites. Or look at the proximity of your own locations to detect the possibility of cannibalization. View it according to one-way streets or intersections known for traffic congestion. You can easily change up the factors right on your dashboard. Eureka! Now you can see how these factors might be affecting sales.
How can you use it for forecasts and preventive action? Here, deductive models describe the relationship between an event and spatial variables. For example, car break-ins occur more frequently at a certain distance from a highway exit, and in a certain city, home invasions correlate to the distance from police and fire stations. Armed with this data, you can pinpoint on the map where to install street lights, for instance, or additional call boxes or public safety satellites.
And how can you derive immediate, feet-on-the-street benefits from location analytics? Imagine a salesperson, a claims adjuster, or a service rep with a geo-mapping app on a mobile device. Finding herself with 30 minutes to spare, the rep can quickly check on a map of the neighborhood to see if other customers are within the vicinity, thus making better use of time and transportation. Or the adjuster can draw a circle on the dashboard and zoom in to see driving distances between them, for making quick decisions about where he should go next.
Honestly, the applications are endless. What about an insurance company using geo-mapping in a team meeting to visualize risk factors to set auto insurance rates? Or a restaurant supplier working remotely with geo-mapping to help redirect delivery personnel due to an impending storm? Or an oil and gas company evaluating expansion locations relative to business intelligence on its workforce, paired with a geographic analysis of transportation networks?
Recent studies suggest that some 75% of a typical enterprise’s data is location identifiable: a sales territory, a delivery route, location of installed products, addresses, and so on and so forth. If you’re a visual person, or if you have visual learners on your team – and who doesn’t? – think about the ways geo-mapping can simplify your life.
The post “Map-ify” Your Data To Make Location-Dependent Business Decisions appeared first on SAP Digital.
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