Dhvani Sheth, Solution Engineer at Oracle, showed how Oracle Analytics Cloud and Oracle Autonomous Data Warehouse can provide useful data analysis and visualization to HR departments.
Hiring the right people requires a lot of time, money, and effort. However, hiring the wrong people can be just as costly, if not more so. Without using data analytics to make data-driven decisions, HR departments may be prone to hire based on a cognitive bias that they aren’t aware of. Instead of using traditional techniques, HR can use data analytics and visualization to find patterns within large volumes of data to make insightful hiring decisions for the company.
Oracle Analytics Cloud for HR
One of the tools that Oracle offers to customers to help them uncover these patterns and visualizations is the Oracle Analytics Cloud (OAC). By loading hiring profiling data into OAC, HR can find out which departments are getting the most applicants, where these applicants are coming from, and what major keyword trends are occurring in the received resumes. These are just a few examples of business analytics you can perform on data sets within OAC.
OAC + Oracle Autonomous Data Warehouse
The Oracle Analytics Cloud leverages the Oracle Autonomous Data Warehouse (ADW). The Autonomous Data Warehouse stores data sets from SaaS applications like HCM or Taleo. The connection between OAC and ADW enables users to perform data visualizations and predictive and semantic analysis.
Dhvani walked through one of the many ways to construct this solution, in which HCM and Taleo are connected to OAC and the data flow feature is utilized to extract and deliver data. The data is then pushed into ADW, and visualizations can be showcased on the OAC canvas.
In the first canvas that Dhvani showed, you can see an organization’s open positions in different job roles. This canvas also shows the most frequently used keywords in the positions’ requirements lists. You can also see which departments lack workflows and where attention is most needed in order to fill open positions.
The second canvas Dhvani walked through showed what geographic areas the applicants are coming from, whether they are internal or external applicants, what recruiter website applicants are using, what months had the highest number of applications, a comparison between the position’s starting salary and industry salary average for each position name.
The third canvas shows the top attrition reason, which department has the most terminations, the termination for each, and the trend status for when the most terminations occurred.
The fourth canvas showed reasons behind top performers leaving the company, which is a powerful insight for companies. It is a piece of data that companies can immediately begin addressing and acting upon.
The final canvas that Dhvani showed housed the number of terminations that come from each recruiting source and where the most high-risk employees come from.
Notional Architecture Behind OAC and ADW
The notional architecture behind Oracle Analytics Cloud shows how Taleo and HCM can be connected to OAC. The data flow feature allows users to extract data from HCM and Taleo and put it into the Oracle Autonomous Data Warehouse. ADW is then connected to OAC and used for data analysis and visualization.
Dhvani showed just one of the ways that different applications can be connected with the Oracle Autonomous Data Warehouse and Analytics Cloud. This helps customers detect patterns and anomalies within data sets. While this example shows how ADW and OAC can benefit HR, it can easily be extended to other lines of business as well.
To see the visuals of the canvases that Dhvani showed, check out the video below.
To learn more about Oracle Analytics Cloud and Oracle Autonomous Data Warehouse, check out the additional Quest resources attached below.
For Oracle HCM Cloud resources, case studies, best practices, etc., check out Quest’s Oracle HCM Cloud Content Center. There are resources and training available for all aspects of HCM Cloud, including payroll, analytics, recruiting, and more!