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By Marc Rind, VP of Product Development & Chief Data Scientist, ADP
Marc Rind, VP of Product Development & Chief Data Scientist, ADP
We’ve all heard of Big Data, but leveraging and finding value in it is no small task. Today, technology uses Big Data to learn our patterns and make recommendations on everything from where to shop to the best route to take home. Your smart phone has learned that you and many people “like you” travel home at 5 PM every day., so it makes recommendations on the shortest route as soon as you approach your car.
Similarly, with advances in technology organizations are getting smarter about collecting, storing, and cleaning their data to help drive better informed business decisions. However, the value internal data can provide is limited unless companies also can access external data to provide context and a wider lens to help suggest what is to come.
“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”
Think about it: What good is tons of internal information if business leaders can’t compare it against industry benchmarks to see how they measure up, and analyze it to identify patterns and take action?
Workforce Data Comes Last
Unfortunately, workforce data is lagging behind other business functions when it comes to providing business insights. While most business leaders will tell you that the most important part of their company is the people, measuring and optimizing those resources from a data perspective still trails the rest of the business from a data availability perspective.
According to the Mercer 2017 Global Talent Trends study, few senior executives and HR professionals are able to translate human capital management data into predictive insights, and nearly one in five are generating only basic descriptive reporting and historical trend analyses.
This 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 it
The 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.
By Kim Tracy, CIO, Northeastern Illinois University
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