The SAP Analytics Cloud R Visualization feature allows users to integrate their own R environment into SAP Analytics Cloud. R is an open-source programming language that includes packages for advanced visualizations, statistics, machine learning, and much more.

With this new integration in SAP Analytics Cloud, you can now:

  • Insert R-visualizations into your story.
  • Interact with R-visualizations using SAP Analytics Cloud-controls (such as filters).
  • Share these SAP Analytics Cloud stories, which include R-visualizations, with other users.

In Part One of this blog, I outlined the preparation steps you will need to take in order to apply the R visualization. Today, we’ll take a look at a use case of how an analyst in an international software company can use the R Visualization feature in SAP Analytics Cloud for their sales analysis and forecast. I’ll give you an overview of  two analyses:

  • Analysis of Sales/Cost Trend via time (where the analyst wants to explore the sales trend over time for software and service in order to leverage its data with R visualization.)
  • Customer Analysis (where the analyst wants to see the details of the company’s customer base and how it changes over time-based on different measures).

These examples help illustrate how to leverage the R-Visualization feature of SAP Analytics Cloud to do the real business analysis. As you will see, the feature is able to provide a lot more flexibility on visualization and business intelligence.

Analysis #1: Sales/Cost Trend via Time

As the technology industry moves from traditional on-premise software to the cloud, the analyst wants to take a look at how that trend affects the sales of the company.

First, the analyst wants to explore the Sales Trend over time for Software and Service, and so they follow the steps below in order to leverage its data with R visualization. (You can refer to this blog for screen shots of each of these steps.)

  1. Create a new story.
  2. Add a new Canvas Page.
  3. Create an R Visualization on the Story Canvas.
  4. On the Story page, click on “+ Input Data” and select the data model of your choice (in this case, it will be SoftwareSales_ABCCompany).
  5. Add Dimensions as shown below, Filters (e.g. from the Data Frame Panel, Story and Page Filters). Click on the “Fullscreen” button on the Data Frame Panel to see the Table Preview (view-only).
  6. Click “OK” or “Cancel” button to close the panel.
  7. Click on “Add Script” and expand the panel to full screen.
  8. Input R Script in the “Editor” section. Below is a sample R code you could use:
  9. Click “Execute” button to run the script and you will see the chart generated in the “Preview” section of the window.
  10. Click “Apply” to insert the chart in to your story.

From the chart above, we can see that in recent years, the Software sales have been relatively flat, whereas the Service sales have been increasing steadily over the last five years. This aligns with the typical trend in a cloud environment where revenue recognition is delayed, and it also shows the increasing demand in the market for professional software service.

Next, let’s take a deep dive into the software sales to see how each component of it has changed over time. When you follow the same steps in our previous section to generate a new chart by using the following R-Code, you end up with the following chart.

Here, we can easily see that the traditional on-premise software sales have been going down and the cloud software sales are taking the lead in sales during the last five years.

We can conclude that Cloud software, as well as services associated with it, are driving the revenue of the company (same as the industry trend).

Analysis #2: Customer Analysis

As an analyst, you may also want to see the details of the company’s customer base and how it changes over time based on different measures.

First, we can follow the same steps as mentioned previously to execute the following R-Code in SAP Analytics Cloud. This will  generate a chart like the one below.

Here, we can see that On-Premise Software Sales still account for a large chunk of sales for traditional value industries such as Industrial, where the growth industries such as High Tech are moving faster to the cloud.

Second, we can also take a look at customer size compared to customer number in terms of revenue contribution to the company.

Again, we can follow the same steps and R-code below to see the insight, and the generated chart will look like the screenshot below.

Here, we can see that even though the large-size customers (Fortune 500) are only a small portion of the entire customer base, they actually generate the majority of revenue for the company. It will give the higher management of the company some hints on how to better manage their customers and diversify their revenue sources.

Conclusion

It is a small story to tell regarding how to leverage the R-Visualization feature of SAP Analytics Cloud to do the real business analysis. As we can see, this feature is able to provide a lot more flexibility on visualization and business intelligence.

Learn More

  • In order to perform the analysis with R visualization, you will need to set up and configure your R Server to connect to SAP Analytics Cloud. You’ll also need to have a model created and data imported in SAP Analytics Cloud in order to apply the R visualization. Please refer to this blog for the detailed steps to set up your R server connection.
  • Read our other blogs on machine learning and predictive analytics subjects.
  • Learn more about SAP Analytics Cloud.

This post originally appeared on the SAP Community blog and has been republished with permission.