The Truth and Myths about Staffing Trends

Depending on who you talk to, the staffing trend today is toward more temporary staffing. Or less. Providing more perks and benefits. Or less. Favoring more experienced workers. Or not. And so on.

Clearly, planning the future of your business using staffing predictions can be an “iffy” proposition. Which is why it’s important to be aware of the strengths and limitations of prediction models. 

Because staffing is based on business growth, or lack thereof, it can be instructive to examine the techniques that financial institutions use to evaluate stock market trends. Though, as you can infer from the lack of absolute accuracy surrounding those predictions, this task is less of a science than some would hope. 

Before a predictive model can be created, a large quantity of data must be gathered. Modelers not only employ reams of government statistics, but proprietary business and industry statistics as well. Supplementary information, which may not be directly connected to the question at hand, is also included. Once collected, the data may be weighted, with the most trustworthy data receiving a higher score. This helps ensure that the final results are based on the best available statistical data. 

 In the world of economics, statistics can be grouped into three headings: leading, lagging and coincident. Leading is the view forward, as through a windshield, predicting where we’re going. Lagging is the view backward, as through the rearview mirror, analyzing where we’ve been. Coincident is the view through the side window, determining where we are. Typically, the lagging and coincident statistics help determine the leading economic indicators. 

Once the statistical data has been compiled and weighted, the actual trend analysis and predictions are made. There are as many mathematical algorithms for analyzing these statistics as there are firms engaged in the process. Each has varying degrees of success, but most experts believe that it’s the variety, precision and weighting of the underlying data that best determines the difference between an accurate prediction and one that is less accurate.  Or, more succinctly, the better the data, the more likely a prediction will be correct. 

At that point, a trend solution is created. However, such trend predictions normally need to be translated from a mathematical notation to a quantifiable concept that can easily be understood by a particular audience, whether they be stock investors or staffing business owners. And, just as often, that conclusion is accompanied by a wide variety of footnotes that may get lost in the media’s reporting of the results. 

Despite the possibility of misinterpretation or inaccurate predictions, the scientific method of trend prediction is preferred over guessing and instinct, especially where long-term business planning is concerned. The best method to obtain a more accurate vision of the future is to consider the reports in aggregate, as each individual report is likely to have its own flaws and shortcomings. And to keep in mind the old stock market adage, “Past performance is no indication of future results.”