Big data analytics in fleet management

“Big data” is a common buzzword, referring to the large volumes of structured and unstructured data generated every day across a myriad of industries. These “big data” sets are growing at an exponential rate, and the data itself can be gleaned from many sources. For fleet management companies and organizations that manage fleets, this could include GPS fleet tracking platforms, Internet of Things (IoT) sensors and the integrated original equipment manufacturer’s (OEM) sensors. Data can then be mined by businesses to derive impactful operational insights with the use of big data analytics. 

Let’s take a closer look at big data analytics in fleet management and how it can help organizations improve their day-to-day fleet operations. 

Why is big data important for fleet management? 

Big data can be extremely important to organizations and their fleets — when combined with fleet analytics, it can help streamline many aspects of business operations. Every day, fleets amass information around engine status, vehicle speed, number of stops, vehicle location, fuel usage, routes driven, tire pressure, ETAs and so much more. When this large volume of data is integrated into, and funneled through, a data-driven fleet management software platform with analytics capabilities, it results in real-time feedback and data-driven insights that managers can use to employ better business practices. 

It’s through these insights that organizations can get a clearer picture of fleet expenses, inefficiencies and ongoing fleet trends. Addressing concerns and implementing new plans based on this data can have a direct positive impact on ROI and the bottom line. 

Want to see the real ROI you can realize from big data analytics in fleet management? Calculate it now. 

Moving from data to action with the four types of big data analytics

Big data fuels modern fleet management by capturing what’s happening across vehicles, drivers and operations. But the real value comes from big data analytics in fleet management: the process of examining fleet data to uncover insights, explain outcomes and guide smarter decisions. Most high-performing fleets tend to progress through a “maturity curve” consisting of four distinct types of big data analytics. Together, these analytics help organizations understand current performance, identify root causes and take informed action.

1. Descriptive analytics: "What happened?"

Descriptive big data analytics forms the foundation of fleet reporting by organizing raw fleet data into clear summaries of past activity. This type of analysis answers basic performance questions and establishes a historical record as a baseline.

Example: A month-end report shows total idle time increased by 15% compared to last month.

2. Diagnostic analytics: "Why did it happen?"

Diagnostic big data analytics go a step further by examining relationships within the fleet data to identify contributing factors. This type of analysis helps fleets move from awareness to understanding by revealing patterns behind performance changes.

Example: After spotting the idle spike, a fleet manager filters by driver, location and route and discovers three vehicles consistently idling at a new job site with long wait times.

3. Predictive analytics: "What might happen?"

Predictive big data analytics uses historical trends and statistical modeling to estimate likely future outcomes. While often discussed as forecasting, many fleets apply this analysis to anticipate future operational risks and plan ahead using existing data patterns.

Example: Based on the historic idling data, route schedules and job site patterns, the fleet manager sees that if the idling trend continues, fuel costs and engine wear are likely to increase over the next quarter, making this new job site more costly.

4. Prescriptive analytics: "How can we make it happen?"

Prescriptive big data analytics represent the most advanced stage, where AI and machine learning recommend specific actions to address an issue or achieve a desired goal.

Example: An AI tool analyzes the idling trend alongside location data and recommends adjusting arrival times and alerting managers to coach drivers on engine idling reduction techniques to bring idling back into acceptable thresholds. 

Using big data to support balanced fleet operations

Big data analytics shouldn’t exist in a vacuum. To see the strongest ROI, organizations use fleet analytics to support successful fleet operations across several critical areas. This approach helps managers move beyond basic vehicle tracking and toward a more balanced, performance-driven operation.

• Stakeholder satisfaction: A fleet only succeeds if it meets the expectations of those who rely on it, including drivers, customers and leadership. Big data analytics provides the transparency stakeholders expect, such as more informed routing, clearer reporting and greater visibility into operations.
• Safety culture: Without fleet data, developing a safety culture can be inconsistent or subjective. An intentional safety culture is one where excellence is recognized based on facts. Using data to create driver scorecards and coach driving behaviors based on objective evidence fosters a culture of fairness and trust.
• Operational efficiency: Data-driven fleet management insights help fleets make better use of their most valuable assets: time and money. By analyzing vehicle utilization, idle time and engine health, managers can reduce waste, underused assets and ensure every vehicle is working toward the bottom line.
• Risk management: Constantly reacting to incidents, violations or breakdowns makes it difficult to maintain a productive, efficient fleet. Big data analytics supports a more proactive approach, allowing fleets to intervene with coaching or policy adjustments before minor issues escalate.

How can big data help improve fleet management? 

Leveraging big data analytics helps organizations improve fleet management by streamlining data-gathering and supporting faster, more informed decision-making. Organizations can leverage cloud-based platforms to collect, track and analyze in near real time. And, when combined with telematics software, big data can provide insights around several crucial fleet aspects. 

• Driver behavior: Safety is a top priority for every fleet. This includes how drivers operate vehicles both on and off the road. Combining big data and fleet data analytics strategies with telematics lets fleet managers gather an accurate, ongoing picture of driving patterns pertaining to speeding, harsh braking, idling and other risky driving behaviors. Managers can evaluate this data to see where driver performance is exceeding expectations or where additional coaching is needed. 

• Fleet maintenance: Getting ahead of vehicle maintenance issues provides valuable cost savings. By leveraging big data related to vehicle health and diagnostics (e.g., engine status, fuel level and mileage) and funneling it through a fleet management system, managers can proactively address vehicle issues before they become a problem. Managers can also analyze the fleet data to see which vehicles tend to remain in better shape and which may need to be replaced to optimize fleet performance. 

• Compliance: Many fleet vehicles are required to use electronic logging devices (ELDs) to record duty status information. These digitally connected devices can be integrated into fleet data management platforms to enable all data captured to become part of the decision-making workflow. Managers can easily track driver hours and see when drivers are nearing required rest periods. 

• Routing: Without accurate, detailed fleet data, it’s hard to know why a particular delivery is taking longer than anticipated, why certain routes are consistently problematic or if an unexpected obstacle is hindering a fleet vehicle’s progress. Having a near real-time picture into weather, traffic, accidents or even route shortcuts can help organizations save time, save fuel and reduce vehicle wear and tear. Big data analytics doesn’t just save time and money internally, it can also improve the customer experience through more reliable, predictable service schedules and arrival time windows.

• Security: By tracking usage over time, you develop historical data norms, across vehicles, assets, locations and operating hours. When activity begins to fall outside those norms, managers can determine whether usage is authorized or suspicious. Beyond individual alerts, big data enables trend analysis that helps identify recurring theft patterns, high-risk zones and vulnerable times of day across regions or divisions. Fleets can also set alerts for ignition, movement or other sensor activity based on time-of-day or location thresholds, ensuring usual activity – a trailer moving after hours – is flagged early and reviewed.

• Sustainability: Big data also supports sustainability initiatives by helping fleets track fuel consumption, idle time and vehicle utilization – all key contributors to carbon emissions. By analyzing historical fleet data, organizations can identify efficiencies to reduce fuel use, track carbon emissions through carbon footprint and emissions reporting and document progress toward emissions reduction or ESG goals. 

How can fleets leverage big data through AI and machine learning? 

Fleet operations generate massive amounts of data, but having big data analytics in fleet management doesn’t make a fleet safer or control costs. In fact, with so much data, it can be hard to mine it for the right insights that offer measurable outcomes. With analytics powered by AI and machine learning, fleet managers can more easily get insight that they can actually act on.  

With AI embedded in fleet management solutions, fleet managers can see patterns that would otherwise be easy to miss. Here are two examples of how the right solution can help: 

AI help fleets make smarter decisions 

Operational Insights, Verizon Connect’s generative AI fleet management tool, combs through the fleet data already collected to notify fleet managers of patterns or anomalies. Instead of requiring managers to know exactly what questions to ask, this advanced tool proactively highlights trends such as increases in harsh driving, rising idle time or unusual activity across locations.  

With these insights, fleet managers can focus on what matters most by identifying risks and inefficiencies earlier, prioritizing corrective action and sharing clear, data-backed findings across teams.  

Video telematics supports safer driving operations 

Modern video telematics uses big data and AI technology to give fleet managers more visibility into what happens inside and outside the vehicle. It also enables fleet managers to analyze the fleet safety data gleaned from the video footage and receive notifications that prioritize viewing of incidents categorized as unsafe. Cloud AI automatically categorizes HD video so managers can quickly review the events that matter. And AI video alerts drivers in real time to help reduce accidents and transform fleet safety.  

Visual information provides a factual account of unsafe driving behaviors (tailgating, stop sign violations, near-misses, distracted driving, speeding, etc.) to give both managers and drivers a better sense of what needs improvement. This allows managers to tailor coachable moments to each driver, reinforce safe-driving policies and reward drivers as part of an incentive program for demonstrating safe driving habits. 

Integrations matter. Verizon Connect partners with Sawatch Labs to leverage machine learning to identify opportunities to add electric vehicles within fleets. 

Utilizing big data analytics in fleet management

Big data can provide big benefits for fleets — but only if it’s used with an intuitive, functional fleet data analytics interface. When it comes to fleet management, different jobs require different areas of responsibility and different data. For instance, an equipment manager might be interested in the day-to-day utilization of vehicles or powered and nonpowered assets, while a transportation executive might prefer to view overall company and industry trends that occur over several weeks, months or years. 

That’s why it’s important for organizations to look for data-driven fleet management systems that are equipped to process large amounts of varied data. Top vendors provide dashboard filters for an easy way to slice information in a manner that makes it relevant and actionable. 

Keep an eye out for fleet data management technology that provides these types of filter features, which are especially useful in aggregating data for review: 

• Autosuggest: Start typing to reveal available metrics 
• Nested filters: Drill down by filter type to get granular reports, like metrics based on daily vehicle data and fuel type 
• Operator types: Use standard operators to display data as it compares to the customized filter or results that vary from the default values 
• Hierarchical: Limit the filtered fleet data by existing divisions, crews or regions 
• Date-based: Prepare filtered reports by date to quickly compare trends over time

Big data analytics in fleet management is transforming how fleets operate, and as it evolves to encompass AI and machine learning, fleet managers can make real changes that make their fleets more efficient, increase productivity and support safety. 

See how your fleet can turn data into actionable insights. Schedule a demo to explore the real use of big data analytics in fleet management.