Big data for financial leaders is more important than ever. When it comes to your organization’s most valuable asset — people — big data can tell you what you need to know to engage talent and get the most out of it. Employee turnover, for example, may be among your highest costs. But by investing in big data and then using that intelligence to predict which employees are most likely to jump ship, finance leaders can enable their HR department to keep their best and brightest and stave off the high costs that come with talent attrition.
Whether you are using it to its full potential or not, your organization likely collects a wide array of actionable HCM data on your employees, from performance to pay to promotion history and more. With predictive analytics, you can use this information to detect flight risks and take action to keep talent. Underlying reasons for churn include dissatisfaction with the organization, geographic location (commute time), compensation, performance standards and relationships with managers and teammates. The difficulty for most businesses is collecting data on these drivers and then measuring which are most connected to turnover.
In the case of leveraging predictive analytics, a number of algorithms can be utilized to anticipate churn. Your HCM data — when used to its full capabilities — can be your best tool against turnover. HCM data can be used to construct a model that identifies turnover drivers, and these models can then help you determine the likelihood of an employee leaving your business and indicate what factors led to the decision to go. Once these factors are identified, businesses can work to forestall churn. This can lead to substantial savings, engaged employees (who recognize efforts to retain them) and increased productivity.
Age and retirement data collected from HCM systems offer insight about hiring needs, benefits administration and more.