For our participation in the SAC Analytics Designer Hackathon contest, we set out the challenge to create a Netflix-like experience to help our users navigate through their content in a personal and familiar manner. This idea allowed us to explore a variety of Analytics Designer’s capabilities in a playful manner, while simultaneously creating real added value for our reporting community. Obviously, our application was named “SACFlix“.
The application is fed by the four auditing models that are part of SAC’s Usage Tracking Content package. These models are built by SAP and directly consume SAC’s underlying log tables, providing real-time insights on the tenant’s activities, users, files and other objects.
The application’s homepage:
The consumer’s user ID is automatically applied as a filter on the underlying data models, causing the tiles to be populated dynamically based on who is consuming.
The first section is titled “Recommended For Me” and consists of three subsections:
- Viewed most recently: showing the stories the user consumed most recently,
- Modified most recently: showing the stories the user edited most recently, and
- Most popular stories: showing the most popular stories based on number of views.
Clicking on any of the tiles brings up a preview panel with more detailed information on the selected story as well as an embedded preview:
Clicking the “Open Story” button opens the story in a new browser tab.
The second section is titled “My Teams” and shows the teams this user is a member of. Clicking on one of this section’s tiles will bring up more details:
The third major section is titled “My Activities” and provides a visual overview on this user’s SAC activities. The user can toggle between his activities in the current month or overall as well as add additional filters using the filter line on top. This section also includes an R visualization depicting which colleagues this user has been most actively collaborating with.
By navigating to the Preferences icon on the top left the users can determine which (sub)sections they are interested in. These settings can also be controlled using URL parameters. When clicking “Save” a bookmark for this user is automatically created to store the user’s preferences.
Finally, the search bar allows users to search for any specific story or model using free text. The underlying algorithm, written in R, will search through the files’ names and descriptions looking for a match, using regular expressions to achieve the pattern matching:
Under the hood
Summary of the technical highlights:
- bi-directional communication with R server to give the application its intelligence,
- usage of recently released panel widget and layout-related API to control tile positions and visibility,
- usage of embedding functionality to achieve the in-app preview functionality
Building this application allowed us to explore SAC Analytics Designer’s capabilities in detail. It proved to be a very pleasant experience. The product facilitates the creation of complex, powerful analytical applications and has an ambitious road map. We are really excited to see this product evolve!