Mitigating employee flight risk is critical for strong organizational performance because there are hard costs involved in recruiting, hiring and training staff to replace lost resources. In addition, new hires require varying ramp-up times to achieve full productivity, so just filling the job doesn't eliminate the drag on productivity.

TLNT.com reports that, even for entry-level employees, the cost of replacement is anywhere between 30 and 50 percent of the annualized salary. On the highly specialized or executive end of the spectrum, that cost can run to as much as 400 percent of someone's salary. Knowing which employees are going to stick around, and which are likely to depart, is valuable information for finance leaders to have.

Collect Diverse Data

According to Lighthouse Research & Advisory, more than half of talent leaders are using manual processes to gather, report and analyze data. This can present a problem, especially when it comes to flight risk. Data signals that can predict flight risk are varied, but they can include anything from performance review scores and manager satisfaction to engagement levels and salary. Because of the number of signals, analysis can quickly become incredibly complex, requiring the use of technology to surface relevant insights and trends that aren't visible to the naked eye.

According to ERE.net, flight risk can happen due to specific employee populations, such as those with traditionally high turnover or those with a high-pressure manager. Another key element is examining events in the employee experience that might trigger discontent, such as when a colleague departs or when the person completes an advanced degree. While managers can watch for these signs in their individual teams, it's challenging to look at these concepts across the board without technology to support the approach.

Drive Results With Quality Data

The common saying around computers and systems is "garbage in, garbage out," and it's more true now than ever. As more employers start to rely on technology and analytics to solve problems across finance, marketing, sales and other functions, focusing on data quality is paramount. In addition to having enough of the right data and harmony among the various systems in use, the recency of the data matters as well.

Imagine if your firm could not take advantage of the skills available in its workforce because the only data you've collected on skills is from employee resumes, some of which have not been updated in 10-plus years. If you're making a decision based on information, it needs to be recent, relevant and complete in order to generate confidence in the approach.

The concept of employee flight risk is nothing new, but today we have the ability to measure and understand this phenomenon through the use of data analytics. While it may seem like significant work to gather, analyze and understand this data, having the foresight into who is likely to leave can help organizations mitigate risk, prepare for talent gaps and target the key employees that they need to keep.

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Tags: predictive analytics big data