Businesses are changing at a rapid pace and having a dynamic decision making model facilitated by analytics has become inevitable
Fremont, CA: HR analytics tools can offer evidence-based insights to help make better hiring decisions, reduce employee attrition, and increase employee engagement.
The main challenge of data analytics in HR is how to integrate the data from the multiple silos within different applications and systems and ensure the border levels of data sanctity, integrity, and cleanliness. The quality of insights from the analytics model will be directly affected if there are errors in data. Therefore, it is crucial to carefully carry out this step so that users can trust the quality of HR metrics delivered by the model.
Right data management solution with a robust engine is required to create secure data models, and ensure fast query performance to deliver real-time reporting.
Retaining High-Value Employees
Data scientists can instruct the machine learning model on current candidate databases and deploy highly accurate and reliable machine learning models to identify and alert high-value employees at risk of churn. Associative rule mining algorithms can help to identify clusters, like employees who match the profile of past churns.
Examine the reasons such as expanding capabilities, workforce churn, lack of training, etc. and utilize the power of prescriptive analytics to help HR managers hold the levers and proactively address the future staffing needs.
Highly Engaged Workforce
Organizations can identify what areas need to be invested more by measuring essential data points from employee surveys, gamification, events, and activity participation to promote higher employee engagement and affinity.
Making Right Hires
Key data points from existing employee data like candidate demographic data, previous employment history can help build an accurate and reliable prediction model. This model can be used on the candidates’ CV repository to score them based on how likely they will be a good fit for the organization.
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