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Internet Of Things Elnur Dreamstime
Internet Of Things Elnur Dreamstime
Internet Of Things Elnur Dreamstime
Internet Of Things Elnur Dreamstime
Internet Of Things Elnur Dreamstime

Data is messy and can be misleading

April 29, 2021
By using the right kind of data to measure the safety, efficiency, and general quality of operations, fleets can effectively make better business decisions.

Think about this quote for a minute. “When a measure becomes a target, it ceases to be a good measure.” It’s Goodhart’s Law, paraphrased by anthropologist Marilyn Strathern. And, it’s thought provoking.

Basically, it’s warning us to be careful with data and what they tells us. You may take steps to reduce one problem, but you could very well create another as a result. And, in the trucking industry, there are lots of opportunities for change based on data. The key is having processes well thought out. Otherwise, you might be playing whack-a-mole, where constant problems pop up.

Personally, I saw this play out with the first job I had in the tech industry. I was working in Apple’s tech support call center, and we were measured on the call volume and abandonment rate (the number of people who hung up before reaching an agent). The idea was to compensate based on the number of calls answered and incent people to answer those calls in a timely fashion. The unintended consequence was that agents got very good at quickly prescribing a set of potential resolution steps, then sending customers off to try them with the instruction to call back if it didn’t work. Lots of calls answered, low abandonment, but longer time to resolution, and not a great experience for the caller. One problem solved…another created.

Working with hundreds of fleets each year, I’ve seen similar scenarios play out. For example, some fleets use a single driver-related metric – driver productivity  – when doing performance reviews on their driver managers. Different companies use different measurements for driver productivity – mile production, total revenue, percent of available hours used – but it was all measuring driver productivity.

There’s certainly nothing wrong with incorporating productivity into the measurement of manager performance, but it only tells part of the story. If a manager has drivers with high productivity, but also high turnover, poor maintenance habits and more safety issues, is that manager really performing better than their peers?

Here’s another example. Benchmarking fuel performance, on the surface, looks like a fantastic idea since good fuel performance requires speed management and smooth driving, which helps with safety as well. However, when used on its own, it’s a great example of data that creates complacency. Time is a zero-sum game so if a driver is incented to spend more time in one area they’ll spend less somewhere else. What are they sacrificing in order to have more time to get where they need to go? Is it trip planning? Vehicle inspection? Customer service? This is particularly an issue in situations where drivers are bonused for on time performance -- they're incented to arrive on time, and to drive slowly to get there, but that can create dual pressures that lead to maintenance issues and higher turnover.

To be clear, I’m not saying that benchmarking fuel performance is a bad idea, and I’m certainly not saying that we shouldn’t incent drivers to slow down and drive more smoothly. But we do need to recognize that those benchmarking and incentive programs can’t operate alone or in isolation –- they need to be part of a larger package that balances all the places where performance can improve.

As we get more and more data points to measure things that are happening in the world, and as we attempt to use those data points to improve performance, it's important to understand the different ways that data can mislead us, and do what we can to prevent it.

The quote that opened this piece highlights the challenges that arise when you start trying to improve the numbers that you’re measuring, and the false sense of security that can be created when complex issues are reduced to simple numbers. Our experience in the Best Fleets to Drive For program has shown that the way to avoid that is to consider a broader set of metrics that identify and address the potential for unintended consequences.

Creating an effective safety management program for drivers means making sure that ALL the things that define an exceptional driver are measured, incented, and developed. That means not only tracking speed, hard brakes, lane changes, and other on-road performance metrics, but also measuring trip planning, inspections, cargo securement, general workplace safety, sharing of best practices, and continuous learning as well. We’ve seen some fleets go so far as tracking whether drivers keep the cab clean and sweep out the trailer when they’re done. 

Putting all those pieces together, there are lots of data points available now, and more coming all the time. What was previously “unknowable” is getting tracked, reported, and benchmarked. All that is fantastic, and it can be the foundation for making some dramatic improvements in safety, efficiency, and general quality of life for drivers. But, just remember: The world is complex, and data is messy. It’s easy to fall into complacency by relying on incomplete or overly simplified metrics.

Mark Murrell is co-founder of CarriersEdge, a leading provider of online driver training for the trucking industry, and co-creator of Best Fleets to Drive For, an annual evaluation of the best workplaces in the North American trucking industry produced in partnership with the Truckload Carriers Association.

About the Author

Mark Murrell

Mark Murrell is president of CarriersEdge, a leading provider of online driver training for the trucking industry, and co-creator of Best Fleets to Drive For, an annual evaluation of the best workplaces in the North American trucking industry. 

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