APU Business Original

Workforce Analytics: Critical to Good Workforce Management

By Dr. Gary L. Deel, Ph.D., J.D.
Faculty Member, Wallace E. Boston School of Business

For better or worse, many large businesses today are far too complex for mere intuition to drive workforce decision making. The historic “gold standard” for running companies was to put a talented manager behind the wheel who would use his “gut” and what felt right to make good choices.

But as we’ve learned over time, relying on gut feeling alone is rarely a good method for deciding anything. Take the 2011 movie “Moneyball,” for example. The plot of this movie is based on a real story involving the Oakland Athletics baseball team and their then-controversial shift in strategy. They moved away from using the intuition of talent scouts for organizing teams and toward using mathematics and statistics to govern team dynamics.

Essentially, this shift took an emphasis off of age-old baseball metrics such as runs batted in (RBI). Instead, the team’s strategy was redirected it to newer variables like on base percentages (OBP), as those variables were shown to be more closely correlated with game wins.

What were the results? The “Moneyball” method brought about one of the most successful team streaks in baseball history. Instead of the Oakland Athletics suffering mockery from the entire league for their change in strategy, it is now one of the most widely used tools for management in professional baseball.

Similarly, businesses with complex organizational structures and production dynamics must embrace careful data collection and analysis strategies if they want to maximize performance over the long term. This area is where workforce analytics become valuable in many ways.

The Various Ways Workforce Analytics Are Helpful for Organizations

One way that workforce analytics can be helpful to a business is in detecting and correcting employee performance gaps. In a particular organization, how much should each worker in each division be expected to produce? A lot of this comes down to understanding workflows and the capability of the average worker. But this understanding is only accessible to an organization if they collect good data on what employees produce on a daily, weekly and monthly basis.

Another way workforce analytics is useful is in identifying and resolving bottlenecks. Imagine that there are delays in production or processing caused by issues in a particular area or department, which could be related to staffing, training, supplies, support, or other issues. These problems and their full scope might only become visible through a surgical look at throughput across an organization.

On a related note, a third way workforce analytics can be helpful is through determining optimal staffing levels, notwithstanding the presence or absence of bottlenecks. For example, a business could, in theory, accomplish the same amount of work with two employees working 60 hours each, three employees working 40 hours each or even six part-time employees working 20 hours each.

But as I discussed in a previous article, there are important considerations in each business situation, related to factors such as overtime, mandatory health insurance costs, employee burnout, work-life balance, training and supervision, scheduling and availability, turnover projections, and a host of other factors. Consequently, a business must carefully assess and calibrate the proper balance of employee volumes versus hour allocations to arrive at the most efficient, effective model for their business. This outcome is only possible through collecting good data and analyzing it competently.

Similarly, some businesses have tremendously complex, real-time staffing dynamics that require careful planning and adjustment on an almost minute-by-minute basis. For example, when I worked in theme park operations for Disney, we had to adjust labor allocations several times an hour based upon changing variables such as park attendance (inflow and outflow of guests), park schedules (such as showtimes and ride operating hours), queue line waiting times, operational hourly ride capacities (OHRCs), and other guest considerations.

To accommodate this need for constantly shifting labor allocation, we employed a very sophisticated workforce management software that analyzed these rapidly changing variables every minute and made changes based on guest movements throughout the day. The software also scheduled park employees, rotated their posts as needed and accounted for their union-mandated breaks and lunches during each work shift.

With tens of thousands of employees working onsite at any one time, this task would be near impossible to do manually, even with an army of managers. So in this type of situation, workforce analytics were not only convenient, but also vital to the continued operation of the parks and customer satisfaction.

Lastly, workforce analytics tools can help tremendously with understanding and improving worker morale and job satisfaction dynamics. It’s no secret that unhappy employees tend not to stay in their jobs for too long, so every business should invest in understanding the feelings of their workers, lest they suffer significant unnecessary losses through increased turnover.

Data on these variables is obtainable. However, there are important factors to consider in determining how best to collect this information to maximize its validity and reliability.

For example, should employee attitude surveys be anonymous or personally identifiable. There are pros and cons to both choices. For example, anonymous surveys tend to be more honest since employees don’t worry about retaliation for critical opinions. But if we don’t know where the dissatisfaction is coming from (i.e. which employees or departments), it’s much more difficult to solve internal problems, so these choices must be weighed carefully.

Also, expert researchers and social scientists know that questions must be carefully engineered to solicit optimally honest and valid answers. For example, a survey could ask a yes/no question such as “Are you satisfied with your job?” This approach would be simplest, but obviously the data feedback would be binary and consequently limited in its usefulness.

Another option would be to ask a scaled question: “How happy are you with your job?” followed by choices from 1 through 5. But notice the bias in the wording of the question here. What if an employee is not happy at all with the job – what number should then be picked?

The word “happy” here could create a potential bias in answers, even if only a subconscious one. A better way to word a scaled prompt like this one might be “Rate your feelings about your job on the following scale.”

On the other hand, the survey could ask an open-ended, freeform question such as “How do you feel about your job?,” followed by a few lines for an answer in the respondent’s own words. This strategy might serve to solicit more honest and detailed responses than we would expect on a closed-ended question that constrains answers to “yes/no” or a numerical rating scale, which is a more useful approach for business owners seeking honest employee feedback.

However, how would a business owner draw conclusions from the larger population of employees with these types of qualitative survey responses? How could business owners determine an “average” or a “median” in employee opinions, or conduct assessments for other data trends? They probably couldn’t.

Companies That Ignore Workforce Analytics Are More Likely to Suffer Problems

In the end, the takeaway is that workforce analytics efforts can be tremendously useful to 21st-century businesses for a multitude of reasons. However, any organizations that ignore the prospects of workforce analytics do so at their own peril.

Dr. Gary Deel is an Associate Professor with the Wallace E. Boston School of Business. He holds a J.D. in Law and a Ph.D. in Hospitality/Business Management. Gary teaches human resources and employment law classes for the University, the University of Central Florida, Colorado State University and others.

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