Big data and predictive analytics have been around in numerous industries for some years, yet they’ve only recently been applied to HR functions like talent management. The availability of data and the means to harness it can be a game-changer for talent acquisition and retention, but the only way it can work is if the data processing is followed up and acted on.
Predictive analytics includes any statistical technique that identifies and qualifies large quantities of past and present data to make predictions about future events. Its use in business is based on finding patterns to help identify risks and opportunities. Another popular use is credit scoring – like the FICO credit score that is assigned to each person based on his or her credit history, loan applications, etc. and is analyzed to determine the person’s likelihood to make future credit payments on time.
In HR, predictive analytics can help synthesize data on employment rates, job trends, business patterns, and more, to help you develop a more well-rounded recruiting strategy. As HR departments and recruiting, agencies begin to look to predictive analytics, the most important thing to consider is what information will be used and how it will be presented. In other words, what are your company’s goals and how can you build actionable predictive analytics to reach those goals?
When determining whether to implement actionable predictive analytics in your HR strategies, consider including some of these metrics:
Job openings. Which positions will you need to fill in the company—and when—based on the business’s growth plan?
Skills and performance. Again, based on the company’s business strategy and needs, what kind of future skills will be needed from employees, and what performance level is required?
Competition. Which competitors will be hiring from the same talent pool as you, and how will this affect talent availability? Alternatively, this can also show when there may be a talent surplus among job seekers.
Hiring success. What are the general characteristics of candidates who will perform well on the job?
Candidate priorities. How will candidate-job preferences and expectations shift in the future, so your company can adapt?
Turnover. Which jobs are likely to turnover and when, and which employees are likely to leave or be poached by other companies?
Retirement. Which employees will be leaving the company in the foreseeable future to retire, and what openings will then need to be filled?
Mobility. Which employees will be ready for a promotion and when, and who may be able to move around internally based on departmental needs, etc.?
These are only a sampling of the metrics that can be measured through predictive analytics. Once you have determined the kind of data that will most effectively help your company’s talent acquisition and retention strategies, you can develop an action plan for using the data.
Develop a strategy. The main purpose of using actionable predictive analytics is to help form a strategy that will help your HR managers and your company make better decisions when hiring candidates.
Look for patterns. Through actionable predictive analytics, you can discover existing patterns that can help you prepare for what’s coming, by predicting whether historical patterns will repeat or change in the future.
Develop a competitive advantage. Being aware and preparing for what’s coming can give you an advantage over your competitors when it comes to finding the right talent for your company at any given time.
Cover all of the bases. You can use predictive analytic metrics to measure the important aspects of your HR strategies, such as recruiting, retention, performance management, leadership development, etc.
Get to the root causes. When analyzed properly, predictive analytics can not only show what’s changing at your company but also why it’s changing, so you can implement solutions that address the real roots of the problems.
Like any other strategy, actionable predictive analytics also has some challenges, including the following:
Data volume. Big data is called that for a reason. Some estimates state that business data for a company doubles every 1.2 years. This means that the data used one year to predict a pattern might become obsolete in the next few years. Predictive analytic models must be updated regularly to make sure that the data they’re using is still relevant.
Data complexity. The results from any predictive analysis can get pretty complex. Without proper filtering and simplification, it can become useless, since the HR managers who will ultimately use the information are not expert analysts. Any information produced through predictive analytic models should include actionable items that are easy to understand.
Data quality. Sometimes data is just data. To be effective, predictive analytics need to produce data that is accurate, stable, and most importantly, actionable. Without producing an actionable pattern, these models can become expensive time-wasters.
Data usability. Unless your HR department is ready to apply solutions and strategies to patterns predicted by these models, predictive analytics will be useless. If you’re planning to put the time and resources behind it, you have to be ready to turn the predictions into actionable strategies.
Gathering more of the right information can help companies acquire and retain top talent and improve workforce performance by identifying gaps and developing strategies to meet upcoming changes in staffing and skill requirements.
Do you think it’s time for HR to get on board with big data and predictive analytics? What’s been your experience?