SAP Analytics Cloud (SAC) is a powerful and versatile analytical platform that can be leveraged for various use-cases in different organizational settings. Within the modern defense environment, in which data-driven decision making is more important than ever, SAC can help defense organizations handle the increasing volume of data generated from various sources, ranging from intelligence information to logistics. By leveraging SAC, defense organizations can improve their overall situational awareness, analyze material readiness and support capability-based planning, leveraging the full potential of the platform.

Situational Awareness

As a crucial cornerstone of operational oversight and planning, situational awareness allows defense leaders to understand and interpret the environment a given organization operates in. Enabled by the geospatial capabilities of the analytical platform, the user of SAC has the possibility to gain an overview of facilities, garrisons, infrastructure and any other geospatial data or points of interest they might deem important for their internal use. This data can be displayed and consumed on its own, or, as is the case in the image above, combined with other data sources for a more complete operational picture. 

Combined with data on readiness and capabilities, also relating to other use-case examples we will touch on below, the inclusion of spatial information (together with the possibility to add open data from various sources to the mix) provides a powerful and effective way to demonstrate real-time developments, together with the possibilities to focus, filter and combine data in an interactive manner through SAC's “Linked Analysis” feature.

Material Readiness

On the more granular level, when dealing with a given technical object or a group of technical objects, SAC provides the possibility to aggregate various indicators relating to these objects in its local calculations to be translated into KPIs and provide an operational overview when it comes to, e.g., material readiness, as seen on the picture above. In this specific case, we are dealing with a fleet of fighter jets grouped by tranche and weapon system they are equipped with, whose overall material readiness is calculated based on their total operational hours as well as their individual hours since last maintenance. This calculation can, however, be made to include more variables, as well as updating data based on live connections to an S/4HANA system which can be combined to calculate these key indicators directly in SAC.

Analytics Cloud allows the user to choose which underlying data they deem as important and then, subsequently, provides them with a possibility to combine them with different weights and parameters in the story itself to develop calculations that are then mirrored in graphs and charts to provide not only the user, but also a possibly wider audience with quite a complete and customized picture of what the overall readiness picture for, say, a group of fighters currently is, possibly also grouped by their tranche (see radar chart above) or weapon system (see heatmap above).

Mission Planning

Speaking of weights and parameters, this feature can also be carried over from the more granular analytic levels to high-level mission planning, such as in this third example of SAC for defense analytics. This dashboard focuses on the so-called Army Tactical Tasks (ARTs), which are all attributed with a preparation time and task seriousness, two indicators which are later combined with user-customizable weights and, based on these weights, grouped into categories of success/uncertainty/failure in real time.

This SAC story is a powerful example of the possibilities of data aggregation from the lowest organizational levels (the calculation of, e.g., preparation time, can start at the Force Element level already) to the highest planning levels that have the possibility to influence decision makers, but also provide those decision makers to have a say in how this aggregation impacts the data analysis, as we have seen with the example of weighted variables above.

Third party integration

One a more technical level, SAC also allows for various integrations of custom applications, such as webapps (as seen on the right side of the image above in a HANA-based chat interface, which is a part of a larger use-case related to disaster management: Smart Drone Logistics – SAP Media Share), as well as R visualizations and pop-ups that can be added to dashboards to enhance the analytical products of the given user.

Another important technical feature of SAP Analytics Cloud is the customizable scripting feature which utilizes built-in JavaScript capabilities to allow analytics users to create more complex versions of their stories, enable Role-Based Access Control and more complex data filtering. For example, below is a piece of JavaScript code that ensures switching between a GeoMap and a Table by clicking a radio button toggle on a given page of an SAC story:

var selectedValue = RadioButtonGroup_1.getSelectedKey();  // Get the selected value from the Radio Button widget

// Check the selected value to determine which widget to show
if (selectedValue === "Table") {
	// Show Table and Hide Map
	Table_1.setVisible(true);
	GeoMap_1.setVisible(false);
	console.log("Showing Table_1 and hiding Map_1.");
} else if (selectedValue === "Map") {
	// Show Map and Hide Table
	Table_1.setVisible(false);
	GeoMap_1.setVisible(true);
	console.log("Showing Map_1 and hiding Table_1.");
} else {
	// Handle unexpected values (optional)
	console.log(selectedValue);
}

Scripting allows users to create stories that are dynamic, well-optimized and up-to-date, while maintaining an already high level of analytical capabilities that are available out of the box.

Additional details

What we have not touched on in this specific blog is the different connection options that SAC utilizes in order to connect to an S/4 system, a Datasphere tenant or other possible data sources, as well as the different possible formats in which this data can be carried over into the platform (e.g., live connections or replication flows), as well as the details of how 3rd party data can be added to SAC (be it through simple Excel files, points of interest or the Data Marketplace feature on Datasphere). More information on these technical details of SAC can be expected in subsequent future blogs to come.