The Challenges of Manual Reporting

Mike HuhnJanuary 7, 2022

On any given day, fleet managers can be faced with endless data from their fleets’ telematics devices, dash cams, FMIS, fuel cards and more. Multiply this by the number of vehicles in the fleet and the days in operation and you end up with a daunting amount of data to scrub and make useful. Doing this manually, as opposed to through an automated, business intelligence platform, makes this process more challenging than it needs to be.

Still, the problem often originates with fleet managers being hired into their roles and continuing the use of the inefficient, legacy systems that were already in place. This can present many challenges on a day-to-day basis that can lead to wasted time, erroneous data and an unreliable dataset. Ultimately, this is unproductive for any fleet manager looking to make smart business decisions, as the conclusions being drawn can only be as good as the data being used.

Challenges throughout the process

Manually reporting fleet data carries the implication of hidden costs that are aggregated throughout the reporting process. Cleaning and standardizing are essential steps before data can be analyzed or become actionable, so the margin of error here should be as low as possible. When these processes are done manually, however, the chance of human error becomes fairly high.


Data cleaning is a tedious process that includes sifting through data to identify what is relevant, what is incorrect and where additional information needs to be filled in. This is the initial step as it provides a base that you know is accurate and reliable. Though this first step can seem minor, without reliable data fleet managers cannot have total confidence in any insights or decisions made based on these datasets. Essentially, low quality data leads to low quality results.

This initial step presents a high risk for minor mistakes to make their way into the dataset and affect the bigger picture later on. In brief, manual spreadsheets are extremely error-prone due to simple human mistakes and can ultimately diminish confidence in any decisions made based on their data. Even the smallest error is significant when managing high value assets, as it can result in lost opportunity for cost reduction. This process is also extremely time consuming, from gathering data from all different streams to scrubbing it down to what is useful. This can be an overwhelming amount of numbers for anyone to work with.


Data standardization is the process of aggregating and transforming disparate datasets into one uniform format. This format ensures that all data, no matter its origin, follows the same rules for consistency. Ultimately, standardization makes it easier to filter through, compare and analyze overall. This makes the process of digging through data more efficient for the analyst and allows for collaboration.

Through this process there is plenty of room for inconsistencies in formulas and miscommunication in what is considered ‘the standard.’ Unless clearly established across the board, creating a standard that is followed exactly can be tricky in manual reporting. This is especially true when one person is the expert on all things fleet data, and eventually passes on that role and responsibility to a newcomer. Any change in management or personnel will present the implication of onboarding and getting this person to a total understanding of how data reports are created. Similarly, anytime this data expert is out sick or on vacation, all knowledge of fleet data becomes unavailable.


Automating the data reporting process entails implementing tools to aid with the intake and handling of data. This swiftly resolves many of the previously mentioned inefficiencies caused by manual reporting and allows more room for opportunity.

A business intelligence tool is a game changer in fleet management as it shifts the focus away from preparing data to actually analyzing it. This eliminates the time-intensive steps of cleaning and standardizing, setting managers up for success in telling the story behind the numbers and tracking progress toward goals.

Data automation also helps managers and analysts to keep up-to-date on the latest approaches, methodologies and technologies being used in data reporting. Outsourcing this responsibility allows for a greater breadth of knowledge and level of expertise regarding best practices.

Bottom line

At the end of the day, an automated data reporting tool increases three things for your fleet: efficiency, confidence and results. A powerful BI tool clears away hours of manual data processing from a fleet manager or analyst’s duties. It seamlessly handles the heavy lifting in data reporting so that all that’s left to do is analyze the bigger picture. It also processes and aggregates data streams automatically, ensuring that that data is accurate and free of human error. Finally, the increase in efficiency and confidence culminates in improved results overall. Business decisions can be anchored on trusted data and backed by custom reports and dashboards.

If you’re interested to see how our Business Intelligence platform and team of analysts are driving fleet success, schedule a demo with us today.

Mike Huhn

Director of Analytics

Mike Huhn is the director of analytics at Utilimarc. He has spent the last ten years with Utilimarc, and helped to identify a new facet of loop theory – he calls it, the FRUTE loop. See more from Mike

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