Written by: Shiva Bhardwaj

As part of the fleet management team, you are inundated with data from many sources, such as driver ELD data, routing and scheduling data, vehicle profile logistics, and so on. If you are within the maintenance organization, you have your work order data as well as vehicle component health data. Your diagnostic service tools are a must, and you might see several daily dashboards. As your vehicles get more populated with electronics – especially the ‘smart trailers,’ it will only grow exponentially in the near term. At any given time, you have access to more information than anyone can humanly digest. All the while having to make quick decisions that positively impact your fleet’s driver and vehicle productivity (and profitability). This is the definition of DATA OVERLOAD!


You might consider telematics as a cost-cutting, and safety tool that satisfies compliance, improves routing and modifies driving behavior to improve safety or fuel efficiency. However, there is so much more under the hood, literally. There are thousands of diagnostic and component health fault codes, hundreds of sensor-reporting fluids, temperatures, and flow rates every second. This data review process can be overwhelming! Taking advantage of this data is perceived as a tall task without hiring a team of data analysts.


We can all agree that data is powerful and offers new possibilities to manage your fleet. But sifting through the data to find what is useful to your unique fleet without using up precious resources is challenging. In addition, receiving loads of real-time data may not always be actionable, refraining you from making informed decisions. So how can we use the data to make our fleet more efficient and cost-effective without losing sight of the bottom line?


The challenge of data overload begins with thousands of fault codes and millions of sensor data points generated from your vehicle components and emerging through your fleet’s telematics/ELD provider. How can any fleet afford to process all of this raw data in a manner that guarantees reduced downtime and operational costs? 


Attempting to make sense of this data has many fleet managers like yourself answering reactive questions such as  “Is this fault code severe or not?”, “Does this fault code correlate to other fault codes?”  “What is the frequency of this fault code?” “Should I pull the truck off its route, or can it keep driving?” “Should I hire someone to review fault codes and telematics data manually?”


However, what many don’t realize, is that it is actually more important that you can answer these questions: 


  1. How do I avoid unscheduled repairs, unexpected breakdowns and the associated repair & tow costs? 
  2. With my aging fleet (we all have them), which vehicles should I prioritize getting rid of? 
  3. Are our fleet maintenance costs justified? What regions are performing well and which are not?
  4. Is the cost per day per truck of operation growing for vehicles in my fleet? (hint: unless you had the tools to predict the parts and vehicle shortage a few years back, the answer for every fleet is that the costs are growing!”)


A fleet cannot survive solely on preventive maintenance strategies. Instead, it must increase its focus on predictive maintenance and be able to create data visualizations that tell a clear story of effectiveness and efficiency. Predictive fleet maintenance, like Pitstop, enables you to receive insights on your vehicle’s health, predicting equipment failure and overall cost savings.


The difference between raw data and receiving insights is that those insights allow you to make quick decisions about the business. In contrast, raw data has you going down rabbit holes, answering questions that do not help your core business thrive and waste your precious time. This is the biggest problem with data overload. There is too much information to consume, and generating decision intelligence is at significant risk. 


Luckily, along with the rise of data, there has also been notable growth in fleet technology and software. Now, an AI-based analytics solution can tell you what matters from the data noise. Empowering you to make the right decisions in real-time has significant financial benefits for your fleet. The benefits significantly outweigh the investment and make predictive analytics the next wave of necessary fleet technology. I would value your thoughts, opinions and experience with data overload. Please visit Pitstopconnect.com to chat with my team and me! 


About the Author:

Shiva Bhardwaj is the Founder and CEO at Pitstop. While earning a degree in Electrical & Computer Engineering from UWaterloo, Shiva spent time working at NVIDIA and Blackberry. Shiva started his entrepreneurship journey by inventing ShockLock which brought a safe, simple and cost-effective hardware solution to the automotive world. His largest and most recent accomplishment is Pitstop, a predictive maintenance platform for the automotive industry founded in 2015. Shiva has also been recognized as the Vaughan Entrepreneur of the Year & SEMA 35 Under 35.