Many organizations already rely on spatial data processing and use specialist applications alongside, but separate from their business process applications. For example, a gas storage tank is leaking and an emergency repair order is raised in the SAP ERP system by a coordinator. Next, the coordinator uses a separate geo-based application and manually enters the asset number of the tank. The tank shows up on a map, and the coordinator then uses yet another application to identify the nearest available, qualified engineer, who is then dispatched. Multiple, unconnected applications are needed in this business process.
Missed Opportunities using Different Applications
Beyond business applications, there are more exciting use cases for spatial analysis in the sports environment. SAP has developed a series of applications that provide deep analysis of player performance. For example, in golf, by adding a sensor to the ball and pin, we can create a graphical history to illustrate the improvements in accuracy of the shot. These types of applications are already in use by major sports organizations around the world.
There are many applications that could be dramatically enhanced with the integration of spatial data.
SAP Spatial provides new data types for storing geometrical data such as points, lines, and polygons. These can be used to represent precise locations, roads, and regions. SAP HANA Cloud Spatial uses open standards and so can easily be integrated with well-known, leading geospatial providers such as ESRI, OGC, OpenStreepMap, GoogleMap.
As well as storing spatial data, SAP HANA Cloud also provides spatial query functions that can easily be included in SQL Script. Here are some examples of the functions:
Within — which customers are in my region?
Distance — what is the longest distance a high-value customer has to travel to reach my sales outlet?
Crosses — where does truck route A cross truck route B?
SAP HANA Spatial also provides algorithms that can determine clusters. This helps an organization to locate precise locations that might be lucrative based on income data and other interesting attributes associated with consumers.