Analytics can be a valuable tool in helping an organization increase productivity, reduce costs, and increase the effectiveness of the resources designated to helping constituents. The Deputy Commissioner of Finance for West Virginia Bureau for Medical Services, Tony Atkins, states that “If you don’t have the numbers right, then all the other policy, political and cost issues that come into play in the decision-making process become guesses.”
In my previous role as a strategic consultant on Kronos’ data science practice team, I helped customers use their data to tell compelling stories and uncover some interesting trends. For this blog, I’m going to focus on a few simple examples that I worked on that many cities and agencies could relate to. That’s not to say the data science team isn’t working on some advanced things, though! The good news about the trends I uncovered is that our customers usually have the data required to fix them. The purpose of reviewing the following examples is to help inspire new ways of looking at your workforce data. With new ways of looking at your data, you’re able to confidently make decisions around the complicated problems related to your workforce. Products like Kronos’ Analytics are the perfect way to visualize the workforce management data you should already be capturing.
Optimizing Schedules for Better Coverage:
The maintenance department in a mid-sized county was having issues. They had recently switched to an experimental 4/10 workweek, which means they worked 10 hours a day for just 4 days a week. It appeared to be going well until maintenance requests on Fridays and Saturdays started rolling in and they didn’t have enough employees to take care of them. When the 4/10 schedule was introduced, employees were supposed to stagger their workweeks. It wasn’t obvious that this wasn’t happening until the county analyzed worked hours and the graph spiked on Mo/Tu/We/Th. There were 80% less hours-worked on Fridays compared to Tuesdays or Wednesdays. This fairly-simple analysis prompted the department to switch back to a 5x8 workweek, and the problem was instantly resolved. Afterwards, there was less than a 10% variance in worked-hours for any given weekday and constituents were able to benefit from even, consistent coverage. The trial wasn’t a failure, however. The county also benefited from knowing a 4/10 would work if employees were willing to stagger the days that could to take off.
Reducing Unplanned Absences by Finding Abuses
Unplanned absences are top-of-mind for most employers. In 2018, the U.S. Department of Labor (DOL) estimated that over 3.2 percent of a government’s workforce was absent on any given day. That’s higher than almost every private-sector industry listed. This can cause a ripple-effect for teams as well as gaps in coverage and unplanned overtime. A mid-sized county that I worked with had a department struggling with both issues of overtime as well as coverage gaps. If unplanned sick days are truly unplanned, one would expect the rate to be level month-to-month, with few exceptions.
When the whole county was analyzed, the unplanned absence rate was a near-constant 5%, which is in line with many comparable counties. If they stopped analyzing there, they wouldn’t see an issue. However, we used some basic analytics to dive deeper and view unplanned absence rates by specific departments by month. What they found was alarming: One department was ~5% every month, except for July, October, and December. Rates didn’t just double in these months, they saw them tripling and quadrupling! This gave supervisors the data that they needed to take immediate action and drill down even deeper to figure out which employees were driving these high rates.
Issues like this aren’t hard to resolve, but the hard part is finding where they exist. Analyzing workforce data can be a valuable tool for agencies looking to assess their unplanned absence and leave policies.
How to Start Creating Your Own Analytics Team
Gartner estimated that roughly sixty-percent of data projects failed in 2016. Only a year later, Gartner analyst Nick Heudecker said the original estimate was too conservative and believed that it was probably closer to 85 percent. Those are some scary numbers but aren’t cause for concern if these projects are well-researched and planned. By utilizing the right software and involving the correct stakeholders, any agency can utilize their data to solve problems.
Tod Newcomb, a technology contributor for Governing.com, states that an analytics project “...isn’t a technology project that should be run by the IT department, though it will need input from CIOs and their staff to manage the databases and networks that underpin it. It’s also not about data. Rather, it’s a way to predict future strategies and support decision-making. That’s why the right stakeholders need to be involved.”