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Top five mistakes in analyzing fleet data

Friday, July 8, 2016

By: Doug Peters, Advanced Analytics Product Leader, Element Fleet Management

When it comes to your company's fleet, evaluating performance is one of the most important aspects of effective management. Business intelligence tools facilitate the process of analyzing fleet data, but their ability to sort through it quickly and easily can lead to fleet managers taking mental shortcuts. This increases the likelihood of making an analytical mistake.

Here are five typical mistakes to avoid:

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Over-generalizing: Drawing conclusions on little data

Often in fleet, we don't have a lot of data to answer a particular question, so be cautious about drawing quick and clear conclusions. Data is often very specific and literal, so overgeneralizing could easily lead to the wrong conclusions. Small data sets run a greater risk of representing anomalies just by chance. Benchmarking against other fleets is one way to increase the data set. Element conducts a proprietary biannual examination of the major cost categories affecting fleets, bringing greater clarity to company vehicle expense for businesses. View the infographic here.

Ignoring or misunderstanding variance

Point estimates are not always useful. A value could vary quite a bit from the average and still be within a normal and expected range. Average doesn't tell you the full story, so reporting an expected range of values can be more informative.

Analysis paralysis: Tracking things that are not important

Trying to track all data points will hinder more than it will help. Instead, align what you track with your company's strategic objectives. For example, if fuel efficiency is one of your top goals, track things like how often an asset is refueled, what kind of fuel is being used and distance traveled. This will save you time, money and increase your efficiency.

Confusing correlation with causation

There are so many variables to track that sometimes two completely unrelated variables will correlate by purely random chance. Be careful about drawing inferences when two trends move together.

Over complication: Occam's razor concept

Occam's razor is a problem-solving principle stating that when faced with more than one hypothesis explaining the data, choose the simpler explanation. More variables and factors and assumptions do not necessarily result in better analysis.

Do you have any mistakes to add to the list? Tell us, @ElementFleet.

Learn more in our whitepaper on the top five mistakes people make when analyzing data – and tips for avoiding these pitfalls.

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