Moving past H.R. to Human Capital Management (HCM), where people are assets and their future value should be invested in.
The changing workforce, ability to attract the best talent, new technology, and availability of data, have added to the pressure companies feel to make HCM more data-driven. In a highly competitive job market, it’s also important to focus on reducing employee turnover which can have a negative effect on culture, morale, production, and company expenses. People-related data is the key to better understanding an organization’s human capital, workforce capacity, risk, and business performance.
Aggregating and visualizing HCM data to optimize workforce analytics and planning; unlocking HCM intelligence.
Today, there are many specialized third-party applications that deliver functions like payroll management, learning and development, and benefits administration. Aggregating this data is essential to ensure ROI across the HCM data landscape. Our goal is to understand your organization’s human capital, workforce capacity, risk, and business performance. We can help you gain better insight from a single database or multiple databases and applications to produce analytics that reveal insights.
Bringing data together is just the first step, discover HCM insights through data science
At CE Strategy we believe that there are many applications for data science to assist tackling problems facing HCM. These include:
Managing employee attrition and measuring loyalty through machine learning techniques like Random Forest
- One of the best features of the Random forest model is that it provides the importance of variables within the data. For attrition, we are most interested in knowing which factors contribute the most as leading indicators and this technique can provide that insight.
Forecasting capacity and recruitment requirements through econometric modeling
- An econometric model consists of multiple regressions that describe specific business results. The parameters of the regressions are estimated simultaneously which allows them to better express causality which in this case is target workforce capacity based on accurately projected sales and other variables added to the model.
Employee sentiment analysis
- Understanding the voice of your employees can be a major challenge. Through machine learning we can cluster sentiment at scale to develop relevant themes. Those themes can then be contextualized through natural language processing to report sentiment of varying levels. This insight provides the ability to recognize and react to shifts in employee satisfaction or reaction to policy shifts in an accurate, scalable, and timely manner.