Welcome HR pro!
She immediately receives the results and tasks you with a mission to figure out:
- Who is leaving the company?
- Where are they located?
- Which department is most affected?
So that you can take action to reverse the trend.
Using Oracle Autonomous Data Warehouse and Oracle Analytics you will complete 3 objectives:
- Load your Autonomous Database and prepare data for analysis
- Analyze attrition and employee sentiment across multiple dimensions
- Analyze the impact on revenue and take action by identifying highest risk employees
You are now autonomous. Let’s go!
Start by loading a new dataset into your Autonomous Data Warehouse.
Objective 1 Complete
Nice work! You have successfully uploaded a new dataset to Autonomous Data Warehouse and joined it with two others to create a new multi-dimensional dataset for your analysis.
Now let’s analyze attrition and employee sentiment across multiple dimensions.
Let’s create your first visualization.
Now let’s add “Performance” to the canvas.
Select Performance Rating
Select Pick Visualization
Choose Bar Graph
Next add “Attrition” to the canvas.
Choose Create Best Visualization
Now let’s take a look at “Job Satisfaction”.
We already know that the sales department is the most impacted by the attrition, so let’s refine these results to sales department only.
Drag Department to the filter area
Repeating this process, you can make countless visualizations to tell the full story and answer questions.
Let’s analyze examples to shed light on attrition and employee sentiment within the company.
Performance ratings are distributed as expected, and we see no difference in satisfaction by gender, location or management hierarchy.
However, satisfaction and work-life balance scores are low for high-performing employees that have not been promoted nor had a salary increase in the past 3 years. This group is represented in 80% of those that have left the company voluntarily.
Thanks to the natural language processing capabilities within Oracle Analytics and Autonomous Data Warehouse, we now clearly understand overall employee sentiment, and have identified the most likely drivers of attrition:
- Career progression
- Work-life balance
Objective 2 Complete
Nice work! Using Autonomous Data Warehouse you analyzed attrition and employee sentiment across multiple dimensions.
Let’s move on to Objective 3 and analyze the impact on revenue and take action by identifying highest risk employees.
Now that we know who is leaving and why, it’s time to figure out the potential financial impact it could have on the organization.
We immediately see a sharp drop in sales revenue in France, correlated with lower satisfaction scores for the region.
By combining finance data and HR data, we can see the reasons for the attrition and the impact that it has had on the business. Now we can bring the France sales manager into the conversation and create a plan to reverse the trend.
Using predictive capabilities in Oracle Analytics and Autonomous Data Warehouse, let’s drill into a scatter plot of individual sales employees to visualize the top performers at risk of leaving.
Your Recommended Action
Now we know who is at risk, we can take action to retain them. Your suggestion to management is:
- An ad-hoc tactical campaign in France to identify and retain top performers
- A strategic initiative to improve work-life balance, career progression and compensation
- Development programs targeted at high performing employees with 2-3 years tenure
Thanks to the ease of use and power of Oracle Autonomous Data Warehouse you have successfully discovered who is leaving, from where, why they are leaving, and have put in place measures to reverse the attrition trend.
All without needing to involve IT.
You are autonomous.