opensap Intelligent Decisions with SAP Analytics Cloud Unit 3.7 - HANDS-ON SCRIPT on Use in BI Story HR Flight Risk SAC BI & SAC Smart Predict PUBLIC
Table of Contents 1 DEMO SCRIPT OVERVIEW... 3 1.1 Highlights... 3 2 DEMO SCRIPT / STEP-BY STEP GUIDE... 4 2.1 Create a BI model... 4 2.2 Enhance your HR Dashboard... 5 2
DEMO SCRIPT OVERVIEW Highlights In today s digital age, the competition for high performing employees is greater than ever, and the costs associated with losing and replacing talent can be quite expensive. Organizations are looking to improve employee satisfaction to maintain loyalty, and reduce costs spent on recruiting and training new employees. To achieve this, organizations must take a proactive approach to human resources, instead of a reactive one. In this hands on script, you will review how an organization would typically review HR related KPIs, and understand how SAP Analytics Cloud Predictive features could influence proactive employee retention. 3
DEMO SCRIPT / STEP-BY STEP GUIDE We will start from the output dataset HR_Flight_Risk_Output_XX where XX is your initial. Create a BI model Steps Navigate to the menu and select Create followed by Model Select Get data from a datasource then Dataset. Select the folder where you saved your output dataset and click your dataset. Description We will create our model using the existing output dataset. Make the following changes by clicking on the column header, and changing the Type on the far right: To navigate in the list of columns, select the grid view: KXIndex change Type to Dimension. This is an internal index created by Smart Predict. Age change Type to Measure. quantile_rr_flight_risk_10 change Type to Dimension. Click Create Model in the bottom right and click Create. Confirm creation and provide a name for the BI model 4
Steps HR_Flight_Risk_Output_BIM odel_xx Description Now you are brought to the modelling screen in SAP Analytics Cloud. We need to identify measures and dimensions for BI purposes. Once your model has been built, navigate back to the story (the one created with the Smart Discovery feature + the new panel) Navigate to the menu and select Browse followed by Files Navigate to your story Enhance your HR Dashboard Open BI story HR_Dashboard_Work Click Edit in the top right. Hover over Overview and click the downward pointing arrow to Duplicate. Hover over Overview (1) and click the downward pointing arrow to Rename the page to Flight Risk. Delete all the visualizations. Click on the visualization and select the trash can. 5
Insert a chart via the toolbar at the top of the page. Insert a chart. Move the blank chart if necessary to reorganize the page Edit your Data Source. Change the Data Source to your output data model titled HR_Flight_Risk_Output_BIM odel_xx. Under Measures, click the drop-down menu and select Create Calculation Before we start visualizing, let s create a new measure that counts the total number of employees. Select Aggregation as your type, and name your calculated measure Count_Employees For Operation, select COUNT DIMENSIONS and select Employee_ID as the aggregation dimension 6
Under Dimensions add the dimension decision_rr_flight_risk. The new graph shows the number of employees detected as potentially risky. Create a new chart by copying this chart from the right-hand sidebar. Ctrl+V to paste. You can move the chart to align with the previous. Let s take a look at the number of employees who were marked as flight risks by our model. It looks like we have 245 employees who are flight risks For the new duplicated chart, under Dimensions remove the selected option and add quantile_rr_flight_risk_10. Move chart below. Now let s visualize our employees into groups that may leave by range of risk. As you may recall, we generated the approximate quantile with our predictive model, so let s build a visualization to show this. Let s move this chart to sit below the 1 st chart. 7
Insert a table from the top toolbar. Under Rows, select Employee_ID. Under Columns, hover on Account and move your mouse to the right. You should see a filter button appear. Select Age, Organization_Tenure_Mon, Salary, and proba_rr_flight_risk. Click OK. Next, let s create a list of individual employees. Let s create a table that shows the employee s age, tenure in months, salary, and probability of being a flight risk. From the right-side toolbar, click the arrows to sort the table. Select Value Sorting. For type, select Descending and for Related Dimension Account, select proba_rr_flight_risk. Click OK. Sort the employees by highest probability to leave. Right Click on the column header proba_rr_flight_risk from within the table and click on the bullseye icon, selecting New threshold. 8
And let s add some color. Click on the column header proba_rr_flight_risk from within the table. Set the thresholds as per the screenshot. Click Apply. Now you can see the list of employees sorted by risk to flight, with the level of importance of this risk. Select Linked Analysis from the top toolbar and click on your 2 nd chart with quantile information. Under the section chart interactions apply to select Linked item set + This chart Now that we have multiple charts and components together, let s link them so that they are interactive. Then check the box for Filter on data point selection. Once your visualizaitons are linked, you should see some changes when you click on quantile 2. Repeat this step with the table. Click on quantile 2 from the chart. 9
Insert Input Control from the top toolbar. Edit the data source to reflect your output model. For the dimension, select Job_Family and select All Members. Click OK. Resize the input control length-wise to show the all options. Let s add our filter by job family, so that we can further investigate. From the right-side toolbar, click the 3 dots for more options, and select Edit Styling. Set the background to white Save the story And edit the styling so that the options are clearly visible. Adjust the size of your charts and input controls so that they fit nicely on the page. And your dashboard should look something like this: Under the Job_Family input control, uncheck All, and check Information Supervisors/Leaders and Telemarketing Sales Lastly, let s go back to investigating our Information System Profs and Telemarketing Sales employees, who we believed were at risk of leaving the organization. Filter down to those job families. What do you see? There are only 13 employees who are classified as flight risks. Notice they have their probability of leaving, and the number of them who fall into our 1 st quantile are at risk employees. For the rest of the employees, they seem to have a lower probability of flight risk. Our gut feeling, that these employees were likely to leave due to low salaries, is only relevant for 13 employees. We are now able to 10
execute employee retention programs very precisely for these employees, instead of generalizing and speaking to all employees from those job families. This concludes our HR Flight Risk demo. The objective was to show you quickly how you can use the BI features of SAC to explore the output of the classification model. 11
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