This article was updated on July 27, 2018.
Predictive analytics should be coming to the forefront for many organizations. According to Tech Target, analytics rank among the top 10 trends in HR, as businesses look for ways to enhance recruiting efforts, retain great talent and optimize engagement.
But that's just the tip of the big data iceberg — analytics are now reaching the surface as technology advances to meet expectations, with 32 percent of businesses ready to maximize predictive potential, according to Tech Target. The key to upping the numbers and getting great results? Metrics.
What Analytics Can Do for You
While predictive analytics can't make critical business decisions on behalf of HR executives, ideal analytics solutions offer a high-probability look at what's coming next. Are employees likely to seek out new jobs or stay with your firm? Where are new recruits coming from? Who's your biggest competition? The right data-driven answers can help guide both short- and long-term strategy to maximum effect.
But as noted by HR.com, big data solutions are only as good as the information they use. If you've got messy, outdated resources or try to shoehorn in HCM tools that don't play well with cloud-based data sets, your results may never match C-suite expectations. Addressing this issue requires a two-pronged effort — you need to spend the time and money necessary to clean up and streamline your data, and invest in an HCM analytics suite that can handle internal and external information on the cloud.
Find Out What to Measure
Next up? Define what you need to measure. Given the sheer amount of information created by both employees and online interactions every single day, it's easy to get bogged down in metrics that don't matter and answers that don't advance your agenda. Start with the obvious, like average earnings, average tenure, new hire turnover rate and retention rates. Combine the information with external factors such as weather reports or state health data to help predict and manage absenteeism. It's also important to incorporate emerging metrics such as employee engagement. Are staff members happy at their jobs? Do they feel valued and appreciated for the work they do? Think of this as the critical move away from simply structured data into the realm of unstructured insights — a necessity for HR given the flexible and changeable nature of people. Good pay and great benefits aren't enough to predict future HR needs if staff aren't satisfied.
Thanks to the ubiquity and reliability of cloud-based services and solutions, nearly any business can adopt a global worldview. For existing multinational corporations, this presents a unique problem. How do you adopt predictive analytics tools that account for global trends and local eccentricities while staying ahead of more flexible, cloud-startup organizations? As noted by Computerworld, the new face of HR isn't just about keeping staff happy and recruitment steady but "tying staffing decisions directly to specific business problems, strategies and goals."
On a global scale, however, this could seem like a daunting task. Consider the thriving Chinese market. Twenty years ago, international corporations had no problem attracting and keeping top talent. According to China Business Review, however, there's now a significant uptick in Chinese organizations, leaving outside multinational corporations struggling to keep up. As a result, predictive analytics tools can quickly lose their impact overseas if you don't account for local culture, customs and expectations.
In China, for example, a renewed focus on corporate culture and clear leadership opportunities drives new talent acquisition. Big data tools must take these metrics into account if businesses want to succeed outside the U.S. — relying on stateside metrics will yield predictive results that don't match real-world developments.
Maximizing the impact of HCM tools is about more than managing current talent — predictive modeling and hiring tools are now critical to long-term success. The reliability of these tools, however, depends on both the quality of your data and your metrics — accurate measurement delivers actionable prediction.
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