Oracle launched Oracle Analytics for Cloud HCM, a self-service application designed to provide more detailed workforce intelligence by gathering data from across the organization.
Oracle Analytics SVP T.K. Anand said the package can be used out-of-the-box and reduces the organization’s reliance on IT. “This allows for closer collaboration between HR and other key business units—such as finance,” he said..@Oracle launched a self-service application to provide workforce intelligence using data from across the organization. #HR #HRTech Click To Tweet
Pre-built features included in Oracle Analytics for Cloud HCM include:
- Cross-functional data model that accelerates analysis of large volumes of operational data. The model resides within Oracle Autonomous Data Warehouse, enabling alignment across business functions.
- KPI and dashboard library delivers more than 50 HR KPIs, dashboards and reports for HR metrics such as workforce composition, turnover and retention, and team effectiveness. Pre-built KPIs include workforce demographics, turnover, diversity statistics and trends, and compensation trends.
- Advanced analytics for key areas including top talent retention, turnover, team effectiveness, span of control and diversity.
- Extensibility allows customers to use Oracle Cloud HCM data with information from other applications and external sources.
Fusion With Cloud HCM
Oracle Analytics for Cloud HCM is the latest offering in the Oracle Analytics for Fusion Applications suite. The company said it offers the same breadth of capabilities added to Oracle Cloud ERP in September 2019.
In March, Oracle said its Fusion HCM business grew 27 percent during its fiscal 2020 third quarter. In addition, its Cloud HCM products saw strong growth as part of the company’s cloud ERP suite.
Oracle calls Analytics for Fusion Applications “a core pillar” of Oracle Analytics, a single brand that connects data, analytics and applications.
In February, the company rolled out a wide-ranging data science platform for Oracle Cloud with a service called Infrastructure Data Science at its core. The system is meant to help data scientists collaborate on and deploy machine learning models.
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