JUNE 2017CIOAPPLICATIONS.COM 19This is a missed opportunity when you consider that analytics has the potential to help answer questions such as, "How is employee turnover impacting productivity?"; "Are our investments in training actually leading to more sales?", and "What will it take to replace a key employee if she leaves?".Further, research from Deloitte shows that "companies that build capabilities in people analytics outperform their peers in quality of hire, retention, and leadership capabilities and are generally higher ranked in their employment brand."So how can companies embrace workforce analytics to gain workforce insight?Whether you're a tech company or in the manufacturing industry, you can apply workforce analytics to help manage your workforce and provide better business outcomes. The place to start is identifying core business metrics and looking for specific HR outcomes you can link to those metrics. For example, in retail, does associate tenure impact store revenue? Or in manufacturing, does an absence lead to product defects?If you find that safety events happen more in plants with high turnover, you can then focus your efforts on retention and measure how better retention impacts safety. By using turnover probability models, you can then identify organizational hot spots by manager, and even narrow it down to specific people who are at "high risk" of leaving. With this information, managers can then take action, whether that means having more frequent meetings with "high-risk" employees or offering them additional learning and growth opportunities. The Future Relies on Solid Data from the Past and Lots of itThe key to understanding what's going to happen in the future is really having enough solid data about what's happened in the past. Being able to identify workforce issues starts with pulling internal data together across business units and functions, and then normalizing or "cleansing" that data. That means knowing where the data originates and ensuring there's proper standardization in data entry so that you can ensure its reliability and compare it company-wide. Once your internal data's in good shape, then you can compare it against external Big Data to see how your organization compares to industry peers when it comes to things like average tenure, pay equity, and turnover.Many organizations are also using machine learning techniques to help advise and provide direction on where to focus. The key point to remember about machine learning is that it is only as good as the data it's trained with. Machine learning requires Big Data that can provide context and signals of what might come not just from one organization's experiences but many similar companies that have been through similar experiences. Having that context and training data from a large, Big Data set is critical to success when initially training machines to learn. The key takeaway: Machine learning and workforce analytics are directional rather than absolute. They provide insights so business leaders can then decide whether or not to take action. When you consider the cost of replacing key employees, sitting on data that can help improve retention and productivity can be kryptonite to your company's bottom line. Whether you're a tech company or in the manufacturing industry, you can apply workforce analytics to help manage your workforce and provide better business outcomes
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