Operational Efficiency Starts With the Right Questions

Every good analysis starts by asking the right questions. Without them, even the most robust datasets fall flat. That's why, once you identify the key questions your business needs to answer, it's much easier to define which data is useful, what needs to be measured, and how to go about it. This approach applies to everything, from personal finances to large-scale business challenges. And when vehicles are at the heart of your operations —- like most of the businesses I’m working with at the moment — asking the right questions is essential for achieving efficiency.

Throughout my career, whether in marketing or operations, this principle has stayed the same: data alone isn’t enough. You need clarity on what you're trying to understand or solve. Specifically in fleet operations, I’ve seen time and again that many challenges are better addressed with a strong foundation in data analysis than with mechanical expertise.

Good Questions Set a Proper Direction

When it comes to fleet maintenance, many assume that knowing how much is spent is sufficient. But that’s just the tip of the iceberg. If we truly want to understand the efficiency of a fleet, we need to explore further:

• Why are we spending that amount?

• What exactly are we spending on?

• When and under what conditions are these costs incurred?

To answer these questions, we need to dig into the data and look for specific insights. For example:

• How often is each type of service performed?

• What’s the average cost by vehicle type or unit?

• Is maintenance being done on time, or are delays leading to duplicated or unnecessary services?

Asking these types of questions gives structure to our analysis and helps us focus on areas for improvement. Otherwise, we’re left with thousands of data points and no clear path forward. The difference between a generic report and a useful analysis lies in the quality of the questions guiding it.

Distinguishing Insight From Noise

Even with just 500 vehicles, a fleet can generate hundreds of data points daily: fuel purchases, driver assignments, service requests, replaced parts, supplier records, tolls, fines, and more. But collecting data isn’t the same as understanding it.

Let’s say your goal is to answer: “Am I paying competitive prices for maintenance services?” To get there, you’ll need a history of costs by service type and provider. Then you can start comparing, spotting deviations, and identifying outliers.

That’s when a new problem arises, one that has nothing to do with mechanics and everything to do with data structure. If each workshop calls the same service by a different name, or one logs it under “General Checkup” while another writes “Preventive Service Level 1,” your analysis is doomed from the start.

Non-standardized data blocks meaningful analysis. It prevents pattern detection and comparison. Standardization, on the other hand, ensures better data quality and allows for consistent, actionable insights. When you know what you’re trying to solve, it becomes easier to cut through the noise and focus on what matters.

Turning Business Problems Into Data Problems

When I speak with logistics or fleet managers, I often hear goals like “reduce time in the workshop” or “avoid unnecessary services.” These are great starting points, but to solve them, we need to translate them into specific questions that can be answered with data:

• How much time is each unit spending off-route?

• How many services are being executed outside of the established plan?

• Which suppliers have the longest response times or fail to meet delivery commitments?

Answering these accurately means having clear checkpoints throughout the service process. A unit might be delayed not because of service complexity, but because the driver didn’t retrieve it on time. Or maybe the delay was due to late approvals, parts availability, or invoicing issues. Without a record of each step, from request to assignment, execution, and closure, it all looks the same in the system: just another delay.

By establishing traceability, we can go beyond vague symptoms and identify exactly where intervention is needed. It’s the only way to understand if the problem lies in scheduling, service execution, supplier reliability, or internal coordination.

Goodbye to Superficial Conclusions

“High maintenance costs” don’t automatically mean inefficiency. Every company has different priorities and constraints. A distribution fleet operates under different conditions than a corporate vehicle pool. Some organizations may prioritize availability, accepting higher preventive costs in exchange for uptime. Others may focus on short-term cost control.

That’s why your questions must be tailored to your company’s goals. Are you looking to:

Maximize unit availability?

Improve the efficiency of your internal team managing service requests?

Work only with suppliers who consistently meet their delivery commitments?

Your answers will vary depending on what you’re trying to optimize. So will your analysis and how you visualize your data.

Without this context, it’s easy to fall into flawed conclusions. For example, calling a supplier “more expensive” without considering faster turnaround or broader geographic coverage might lead you to the wrong decision. The key is to define what “better” really means for your business.

From Data to Decisions

Once you’ve identified what matters most and know exactly which questions will reveal your progress, communicating insights becomes much simpler.

Let’s revisit the question: “Which suppliers are causing me cost overruns?” The answer could be presented in a simple bar chart comparing price ranges and specific cases of cost deviations—clear, direct, and actionable.

When teams see that analysis is helping them solve real problems — not just feeding KPIs — they become more engaged. Data becomes a tool, not a burden.

If your questions are clear and your data sources are aligned, the next step is to choose a technology that supports your strategy. One that not only displays the information but helps improve its quality, adds operational context, and turns raw numbers into meaningful actions. Because true optimization isn’t about recording more, it’s about understanding better. And that understanding takes more than just tools; it takes thoughtful questions, structured processes, and the right guidance to drive real impact.

In Closing

While this article focused on fleet management, these principles apply to almost any operation that needs to improve performance. The secret isn’t in collecting more data. It’s in knowing what you want to solve, identifying the questions that matter most to your business, and building your analysis around them.

Once you have that, the rest starts to fall into place. Are you ready?