Written by BSM, a Geotab Company
on May 29, 2019

Big data is increasingly impacting every aspect of fleet management, and it’s empowering companies to both manage current operations and predict future trends and events. This week in our big data series, we’re zeroing in on how to convert big data into actionable insights that lower costs and increase fleet safety.

For this article, we’re leaning on an approach to big data called the “insight factory,” coined by McKinsey in 2013. There are four steps to getting value from the data, so we’ll summarize and then provide examples of how this would work for ABC Deliveries, a fictitious company created to illustrate each step. 

Step 1: Decide What to Produce

For the first step, you need to have a clear understanding of what you want to achieve, such as reducing delivery windows, reducing idle times, etc. Give priority to what leads to the most significant economic opportunities. Next, you will want to build a customized factory, collecting the data necessary to produce actionable insights.

Over the past three years, ABC Deliveries have seen a steady decline in their net profits. Their main objective is to increase the company’s bottom line by lowering fleet expenses. To this end, they wanted to reduce fuel and maintenance costs and decrease vehicle wear-and-tear. To establish a customized big data telematics factory, they implemented the fleet management solution from BSM, a Geotab Company. To ensure that they were collecting the right data to meet their goals, they worked with BSM’s project management team to create tailored reports and dashboards.

 

Step 2: Source the Raw Materials

Building a data “warehouse” is extremely valuable, but it takes time. The idea behind this step is to segment the data based on what can be actioned immediately and which will be more reliable with longer collection.

ABC Deliveries determined that individual driver habits could be actioned immediately with a small data set, but they would need a more extensive data set to create a benchmark for overall driver performance, and accurately benchmark fuel and vehicle repair costs against industry averages. Thankfully ABC’s data is warehoused by Geotab, which provides access to historical data, so the benchmarking data was very reliable.

 

Step 3: Produce Insights with Speed

This step is essential because so often we have a “test until perfect” mentality that can be paralyzing to progress. The lesson here is to focus on using good, immediately-available data to produce real-time insights that fleet managers can implement quickly.

ABC Deliveries gave their fleet managers a mandate to follow up with their drivers the same day when major infractions occurred. By actioning the data immediately, they were able to reduce incidents of harsh breaking by 80%, excessive speeding by 90%, and idle time by 55% within the first four weeks.

By reviewing the data and benchmarking them against the industry average, they discovered that they were overspending on vehicle repairs by 16%. They immediately established a preventative maintenance protocol on all of their vehicles and retired the vehicles whose total cost of ownership exceeded the replacement cost.

 

Step 4: Deliver the Goods and Act

Companies can spend months, even years, accumulating data sets to create the perfect benchmark, but why wait? If data yields immediate insights, they should be actioned immediately.

“’Good enough’ information available now 

can be used now to inform specific actions.”

 

What does it take to action the data insights? Insights that drive action must be practical, clear, and trusted by those on the front line: fleet managers, operational managers, etc. They must be empowered, and communication must be two-way.

**

The concept of big data can seem overwhelming when all we hear about are the billions of data points collected, but when we break it down to these four steps, benchmarking with big data is very approachable and compelling. As long as you have a clear understanding of the areas your business needs to focus on, you can build your big data machine to capture and analyze the information to grow your bottom line.

If you haven’t checked out our previous posts on big data, be sure you do! If you’re new to the big data conversation, we wrote a primer post on this topic which you can find here. And for a one-on-one deep dive conversation on the benefits of big data analysis, check out our expert insights post from last week with in-house expert, Joel Waithman.

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