Examples Of Strategic Analytics

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HR professionals and their customers were struggling to explain the “so what” of the HR scorecards and training programs. With the rise of predictive analytics of Amazon, Netflix, and Google, CEOs are asking all functional departments to deliver better business outcomes (Roberts, 2015). HR is no exception.
Strategic analytics in HR falls under many different names: Human Capital Management, Human Capital Analytics, Workforce Analytics, Talent Management, People Management, Talent Supply Chain, and most recently, People Analytics or People Science. Regardless of its name, several examples of strategic analytics occur in each of the major HR responsibilities.
Attract Talented Workforce
One of the primary roles of the HR function is
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Extending a job offer is a major decision that generally creates a long-term relationship between the company and the employee. Companies begin to invest in employees right away with salary, benefits, and training so securing the right person for the position is essential (Roberts, 2015). This will avoid buyer’s remorse. Without a doubt, having the wrong people stay at a company reduces the productivity of the entire team (Sutton, 2011). Given the importance, no other area in the HR strategic analytics has as much attention as this one.
The most notable example is the work Google accomplished in its selection criteria process. Google processes three million resumes each year and hires 0.25% out of the pool (Rafter, 2015). Through rigorous analytics of its processes, Google learned that four interviews were adequate, down from the norm of 15 – 25 interviews. Recruiting administrative time decreased from 10 hours to 1.5 hours. Google uncovered what questions were most effective, created an elaborate on-line questionnaire, and then standardized the process with rigorous interviewee training (Hansell, 2007). It also learned that education was a poor predictor and stopped requesting SAT scores and
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Corporate learning is moving from how many people took a training session to the impact the training had on the employee and the company. Companies have moved towards online and self-directed training making it employee-owned. By analyzing the metadata on which sessions were watched and where videos were stopped, analysts could adapt the videos on an on-going basis (Gale, Chasing the analytics dream, 2015). Employee feedback, with ratings and text, are transparent so others in the organization can adjust their training plans to align with the highly desirable content. All the training and learning data is then matched to employee data to see how it contributes to personal performance and company performance (Bersin, Predictions for 2016: A bold new world of talent, learning, leadership, and HR technology ahead, 2016). One unnamed company determined that employees were leaving within six months because the training was too difficult, and then adjusted the program (Ring & McKeon, HR analytics adds new dimension to the human touch,

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