{"id":3777,"date":"2016-11-23T10:06:00","date_gmt":"2016-11-23T10:06:00","guid":{"rendered":"http:\/\/localhost\/projects\/horsesforsources\/predictive-analytics-in-hcm-systems-20160722\/"},"modified":"2016-11-23T10:06:00","modified_gmt":"2016-11-23T10:06:00","slug":"predictive-analytics-in-hcm-systems-20160722","status":"publish","type":"post","link":"https:\/\/www.horsesforsources.com\/predictive-analytics-in-hcm-systems-20160722\/","title":{"rendered":"First-of-its-Kind HR Technology Research Launched by HfS"},"content":{"rendered":"

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A former colleague had a penchant for using phrases that stuck with me… One of them was – “I have questions for all your answers.”<\/strong>  It took me years of working with Charles Edward “Skip” Odell to learn that his middle name was Edward, thereby explaining why the letters “CEO” on his cuffed shirts were not just aspirational.<\/p>\n

During those same years, the HR technology domain was very much growing up, and the topic of  predictive capabilities wasn’t generating many questions or answers in most customer or solution vendor circles.<\/p>\n

While HR technology solutions have clearly matured in many ways (e.g., engaging user experiences leading to broader usage outside HR Departments, mobile computing’s dominance and increasing cognitive capabilities), the use of science within HCM platforms is arguably still at the adolescent stage. Lots of promise, seemingly random growth spurts, daunting challenges and some really pleasant surprises along the way.<\/p>\n

 Pulling Back the Curtain on Predictive HCM Analytics Capabilities<\/strong><\/span><\/p>\n

What are some of the pleasant surprises?  Well for starters, literally — as these were in-fact the first predictive capabilities introduced in the HR tech arena — more customers are now using tools that highlight employee retention risks, or future star performers among job candidates.  Both of these predictive capabilities, and most others, are of course generally based on validated algorithms adapted to the customer’s business context and data relationships; and either the customer’s data scientists or the system itself (via machine learning) does the adapting and periodic re-calibrating.<\/p>\n

But as Skip astutely pointed out, interesting answers often beget more good questions.   So relative to predicting retention risk or future star employees — or any other situation or outcome that is attracting predictive HR tech capabilities, here is a small sampling of questions that arise<\/strong>:<\/p>\n