Oracle Analytics gives you the power to enable any user to ask any data-driven questions,
so you can get the value you expect from your analytics.
Oracle Analytics provides smart analytics for everyone.
Analysts and Data Stewards can easily access and transform all necessary datasets in preparation
for analysis, then share those datasets with other users.
Business Users, such as Executives, can go beyond just consuming other users’ findings
by proactively investigating datasets to discover and share answers to key business questions.
…and Developers can enhance the out-of-the-box capabilities with customer visualizations,
data connectors, and functions…
…and embed analytics in any context.
Suppose your organization is experiencing employee attrition, and you need to know why.
Oracle Analytics can help you find the answers!
First, your operations team initiates data collaboration from Taleo to create a dataset.
The dataset includes current and past employee details such as salary, education level, and
job satisfaction.
Your HR analyst then uses that dataset to create a data flow containing a Machine Learning
algorithm to predict employee attrition.
She creates the data flow from the Home page by clicking Create and then Data Flow.
She double clicks Attrition Predict to add the employee attrition dataset to the data
flow.
She clicks Add a step (+) and selects Apply Model to create the first step in the data
flow.
A member of her team already trained the model based on a Naïve Bayes algorithm that comes
with Oracle Analytics.
Next, she names the column outputs that are returned by the model.
This includes the predicted values and confidence predictions linked to employee attrition.
She adds the final step in the data flow that specifies the results of the data flow when
its run. The resulting dataset name will be Attrition
Predicted Data.
She clicks Save to save her changes and then…
… names the data flow as Attrition Predicted Data so she can easily find it again.
She clicks OK.
She remembers that she has to run the data flow to generate a new dataset for use later
in visualizations and reports….
…and it looks good so far.
Finally, the Human Resources VP uses the Attrition Predicted Data output to begin working on
pinpointing exactly why the organization is losing employees.
He begins by creating a workbook from the Home page by clicking Create and then Workbook…
He double-clicks Attrition Predicted Data to select the dataset that the HR analyst
just created.
He drags and drops the Employee Count data element to the canvas.
It looks like there is data for 470 employees in the dataset.
He wants to see how many employees are predicted to leave, so he drags the Predicted Attrition
data element to the Filters bar to create a filter on predicted attrition…
...and double clicks Yes to only include employees with a predicted attrition value equal to
Yes.
Many employees are predicted to leave the organization, so he investigates further to
find out why.
He uses the control key to select the Predicted Attrition, Predicted Confidence, First Name,
Last Name, and Employee Number data elements….
…and right-clicks one of the data elements and selects Create Best Visualization to tell
the system to select the best visualization type for the set of data elements he picked.
The pivot table shows him a more detailed view of attrition predictions by employee.
So far, so good.
He wants to see which departments are likely to experience the most employee attrition,
so he uses the control key to select the Employee Count and Department data elements and drags
them to the canvas to create a new visual.
It looks like the organization is losing the most employees from the R & D department at
about 49 percent.
The VP still wants to know more, so he right-clicks Predicted Attrition and selects Explain Predicted
Attrition to launch the Explain feature to automatically generate basic facts, key drivers,
segments, and anomalies discovered for Predicted Attrition.
He scrolls down the Basic Facts About Predicted Attrition page to make further discoveries.
The Age chart which shows him that employees tend to leave if they are younger than 30
years old. He clicks Select For Canvas to select it.
The Daily Rate chart shows him that employees tend to leave if their daily pay rate is less
than 586 dollars. He clicks Select For Canvas to select it.
…and the Stock Option Level chart shows him that employees tend to leave if they are
15 percent or less vested in their stock options. He clicks Select For Canvas to select it.
He clicks Add Selected to add the Age, Daily Rate, and Stock Option charts to the workbook…
This automatically creates a second canvas with those charts.
He clicks Add Notes and enters text to add a not at the top of the canvas about his discoveries.
Finally, the VP begins creating a storyline by clicking Present….
…and double-clicking the Explain Predicted Attrition canvas to add it to the storyline.
He’ll share this information with his team during the next planning session to slow employee
attrition.
Thanks to Oracle Analytics, the VP has all the data he needs to build a focused strategy
to slow attrition.
Check us out at docs.oracle.com