Both HR and C-suite teams look to finance experts as their best resource for emerging workforce trends and budgetary implications. But accurately predicting market forces and staff requirements can be a daunting, complex task. The evolution of predictive analytics tools now empowers finance teams to analyze workforce data and deliver actionable, evidence-based recommendations for hiring, retention and overall budget. The challenge? Accurate predictions don't happen without the right organizational strategy. Here's a look at four best practices for forecasting.
1. Identify the Purpose
Start with the basics. Why are you adopting predictive modeling tools in HR and finance? As noted by TechRepublic, the most common benefit is reduced cost. For example, predictive systems used by Duke Energy saved the organization more than $4 million when the software triggered an early warning after regular power plant maintenance. While staff aren't as predictable as industrial equipment, the right analysis tools can help identify key trends in behavior that may lead to retention issues or performance problems. For finance leaders, it's critical to describe why predictive analytics makes sense and identify the key outcomes expected. This specificity helps set the stage for the next step in improving end results — identifying your pain points.
2. Target Pain Points
Where does your business struggle? What's frustrating your workforce or making them consider the possibility of another job? As noted by ADP's The Workforce Analytics Workbook: How to Turn People Data Into Business Insights, 63 percent of employees may be open to leaving their jobs, even if they display few outward signs of dissatisfaction. This is the value of agile analytics tools — the ability to dig down and identify pain points that may not be immediately obvious. For example, how long is the average commute for employees? Are there outliers? What do annual performance reviews look like? Have specific employees experienced a sudden decline? Knowing what hurts can inform your ability to prescribe the right solution.
3. Implement the Process
Effective process implementation can be key to predictive analytics. As noted by Zawya, finance has been historically backward-looking when it comes to leveraging data but new sources allow firms to "use this huge quantity of data and turn it into insights by identifying patterns and predicting future outcomes." But what does this look like in practice? How do finance teams implement predictive processes? Start by pinpointing key data sources both inside and outside your organization. This means using internal data sets such as employee attendance data, feedback surveys, performance reviews and incorporating external sources such as wage comparison, benefits analysis and market trend data to create a comprehensive workforce picture.
4. Integrate Your People
Organizations must make their predictive people analytics more people-friendly. Harvard Business Review reports that although businesses talk big, just 5 percent of all big-data investments are in HR. Part of the problem stems from a kind of data-blindness, with organizations too focused on pulling out information without recognizing the people attached to this data. Done recklessly, attempts at integrating predictive tools can actually become the cause of workplace issues, which in turn could increase the chance of turnover. Instead, firms need to focus on the user experience: Is data collection appropriate, seamless and straightforward? Is it transparent for users and actionable for finance staff? Without effective integration, any predictive initiative will end up going backward instead of driving future decisions.
HR leaders, along with the C-suite, are wondering what's next for their workforce and how they can create reasonable budgets. Finance leaders can improve their predictive power by identifying purpose, targeting pain points, implementing effective processes and integrating the right people.
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