HRMS Predictive Analytics: A Step to HR Clairvoyance
Do you ever wish you could have a crystal ball for HR? How often have you heard this from an HR colleague: “if only we knew then what we know now!” Fortunately, there’s a way to improve visibility of future HR expectations and demands, and this secret weapon can be found in the form of HRMS predictive analytics.
Establish Your Baselines for Trends
The first step to identifying trends is to understand your starting point. You need to track basic measurements such as hires and terminations per month, sources of hires, performance review ratings, etc. All of this basic data needs to exist in your HRMS in a clear and concise manner.
Once you have begun feeding HRMS predictive analytics with foundation data, the next step is to have monthly figures for metrics of interest, usually dating back a year at least. Do you normally have a 10% increase in hiring in the summer months due to your business needs? Do more people resign on January 2nd immediately after bonuses are paid out? Often, these numbers are cyclical, so comparing month to month does not give us a true picture of the next month. Rather, we need to look ahead a year and compare to this month’s counts.
Identify Statistical Anomalies
Most companies will have reporting gurus who can spot trends based on what they know about how the data normally looks. A spike in counts or decrease at the wrong time of year will need to be investigated. Some companies may even go a step further and import HRMS data into statistical analysis tools such as IBM’s SPSS. Applications such as these will specifically guide you to ‘statistical anomalies’ where the numbers just don’t make sense. From there, HRMS predictive analytics will often be able to pinpoint a data trend well in advance of seeing it via ‘normal’ reporting methods.
If your sales systems shows an upcoming sustained increase of orders, you may have to hire new employees to support the increased business. How often as HR do we only hear about this increase late in the game, when resources are needed urgently on-site to fulfill the orders? If you can bring in other forms of data and management intelligence and overlay it on your HRMS data, you will be able to spot that sales spike. You can then assess your current and forecasted headcount, thus enabling you to inform the business of its hiring needs, well before it notices itself.
While HRMS predictive analytics won’t be able to predict the winning lottery numbers for you, it will be able to guide you through unknown territory, as long as you know the secrets of using it to its maximum potential.
Featured white papers
5 ways HRMS helps you get more out of your HR data
A comprehensive guide to the data analytics capabilities of your HRMSDownload
Three unexpected benefits of real-time HRMS data analytics
How a market focus on real-time HRMS data analytics has transformed the way HR departments do bus...
A beginners' guide to recruitment analytics
Discover what recruitment analytics can do for you with this guide to key HRMS metrics and what t...
Where is HRMS going in 2017?
What will 2017 have in store for HR technology? We analyze some of last year’s biggest HRMS trend...