The Beginner’s Guide to Predictive Workforce Analytics
Today’s business executives are increasingly applying pressure to their human resources departments to “use predictive analytics.” This pressure isn’t unique to human resources, as these same business leaders are similarly pressuring sales, customer service, IT, finance, and every other department head to use predictive or analytical tools.
Every department needs to uncover predictive analytics projects that affect their bottom line (increase sales, increase customer service, decrease mistakes, increase calls per day, and the like).
When human resources analysts begin a predictive analytics initiative, it appears to mirror what other lines of business do. However, instead of having a great outcome, it can be potentially devastating for HR.
Unless the unique challenge HR faces is understood, it can cause an HR department to falter, lose analytics project resources and funding, and leave them mystified, with no idea how they missed the goal of the predictive initiative so badly.
Like all other lines of business, Talent Analytics’ experience has been that when HR focuses on predictive analytics projects, they look around for interesting HR problems to solve; that is, problems inside of HR departments. They’d like to know if employee engagement predicts anything, if they can use predictive work with their diversity challenges, predict a flight risk score that is tied to the amount of training or promotions an employee has received, or see if employee onboarding relates to how long an individual lasts in a role. Though these projects have tentative ties to other lines of business, they are driven from an HR need or curiosity.
Our firm is often asked if we can “explore the data in the HR systems” to see if we can find anything useful. We recommend avoiding this approach. It is exactly the same as beginning to read Wikipedia from the beginning (like a book) hoping to find something useful.
When exploring HR data (or any data) without posing a question, what you’ll find are factoids that will be “interesting but not actionable.” People may comment, “really, I never knew that,” but nothing will result. You’ll pay an external consultant a lot of money to do this, or have a needed internal resource do this—only to gain little value without any strategic impact. Avoid using the Wikipedia approach—at least at first. Start with a question to solve. Don’t start with a data set.
Like all other lines of business, HR wants to show results of their HR-focused predictive projects. However, there is an important disconnect: HR shows results that are meaningful to HR only.
Perhaps there is a prediction that ties the number of training classes to attrition, or correlates performance review ratings with how long someone would last in their role. This is interesting information to HR but not to the business.
The HR department can learn from the marketing department, which came before them on the predictive analytics journey.
