Amit Jain is co-founder & COO at Roadz, a Silicon Valley based fleet-tech company.
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Although modern fleet management is predicated on data, this information is often heavily segregated and segmented, resulting in organizations drowning in data they can’t effectively use or apply. In other words, modern fleet management is falling victim to death by a thousand apps.
According to the results of one survey, 46% of fleets use over 10 apps to manage operations. Incredibly, 30% use too many apps to count, underscoring the diminishing returns from information overload.
These fragmented datasets mean that many organizations are making less informed decisions or are dedicating resources to manually aggregating data from different systems—a wildly inefficient process to address a complicated problem. One solution to this is greater singularity.
Achieving Greater Singularity
Nearly half of the respondents of another survey said that "the most helpful technology would be one platform that provided the information they needed to manage their fleet." That’s why effective fleet management needs an integrated approach to handling data from disparate sources, streamlining processes and eliminating inefficiencies in current practices.
Here are three ways companies can begin accomplishing that now.
1. Normalized And Contextual Data
Business and tech innovators understand that data has to come together to be effective, often turning to application programming interfaces (APIs) to create connections between various software solutions. Unfortunately, varying terminologies used by different systems create confusion and make it more difficult to aggregate various datasets.
For example, one system might refer to a location as “location” while another might call it “place.” It’s a subtle difference with enormous implications for practical data analysis. Data normalization standardizes data fields across different systems for consistency and recognizability. It’s an essential step when truly integrating data.
This has implications for several facets of fleet management, including driver safety. Normalizing and contextualizing data allows for a comprehensive and clear understanding of various aspects of driver safety, such as training program effectiveness, speeding patterns and distracted driving incidents. By properly consolidating this information, fleet managers can identify high-risk behaviors and trends more accurately, leveraging this information to create more effective monitoring, training and enforcement of safety protocols that improve driver behavior and reduce accidents.
Holistically, data normalization and contextualization provide a unified understanding of various aspects of fleet management, allowing for more accurate and comprehensive insights.
2. A Unified Fleet Workspace
A unified fleet workspace provides a single user interface where aggregated data is displayed. The idea is to create a workspace where data from various fleet management solutions are aggregated and integrated in a way that provides a holistic view of operations and the fleet managers don't have to navigate multiple user interfaces. This would include features like dashboards, alerts, task management and exception reporting, all accessible in one place. As such, a unified fleet workspace allows for easier monitoring, alert flagging and decision-making based on comprehensive data.
3. Built-In Predictive Analytics And AI Capability
Access to real-time, integrated data from once-disparate information sources allows fleet managers to understand more than just what happened before. It can unlock key insights that empower them to make robust business and operational decisions.
For instance, real-time data allows for prompt and informed decision-making, enabling fleet managers to respond quickly to changing situations on the road, including live vehicle tracking, driver behavior analysis and critical issue alerts. Increasingly, fleet managers can couple these efforts with powerful predictive analytics built into a platform that can anticipate future trends, like vehicle maintenance schedules, route optimization and fuel consumption forecasting.
When you add generative artificial intelligence (GenAI), fleet managers no longer have to rely on IT departments to run queries for them. Using natural language, they can perform what-if analyses in real time, flag events that are outside company-defined thresholds and gain predictive insights, which can lead to more effective, efficient and safer fleet operations. For instance, GenAI-equipped data dashboards can provide fleet managers with actionable insights on asset utilization, fuel fraud and driver behavior, and some can uncover future demand.
Integrated Data Is Actionable Data
Digital fleet management is a data-driven endeavor that’s being undermined by too much information in too many different places. When data is an abundant resource, information overload can be a real hindrance to effective decision-making.
But the answer isn’t less data. It’s integrated data used differently. By leveraging real-time data, predictive analytics and the power of GenAI, fleet management can transcend information overload to potentially achieve improved efficiency, safety and decision-making.
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