Everyone in trucking is talking about AI, visibility, fleet optimization, and “smart” dispatch. Here’s the uncomfortable truth: most fleets are trying to build a penthouse on a cracked foundation.
That foundation is your operational data.
When your miles are wrong, your ETAs are wrong. When your ETAs are wrong, your service score is wrong. When your service score is wrong, your pricing, claims, detention recovery, and customer trust start bleeding. Quietly. Daily.
And in a 2025 market that still feels more like survival than a clean recovery, you do not have room to leak money because your system thinks a truck teleported 40 miles in 2 minutes.
The hidden tax: “close enough” data
Bad data does not show up as a single big failure. It shows up as a hundred tiny costs you stop noticing:
- Dispatch plans built on wrong transit times, so drivers arrive early and sit, or arrive late and get penalized.
- Routing based on “paper miles” that do not match real-world GPS and toll patterns, so your cost-per-mile is fantasy.
- Detention disputes because your arrival event was triggered by a loose geofence, or not triggered at all.
- Missed accessorials because someone typed “lumper” in a notes field instead of a billable code.
- Safety and compliance exposure because device data is inconsistent, incomplete, or coming from questionable hardware.
Regulators are tightening how device data enters the ecosystem too. FMCSA is actively overhauling how ELDs get vetted for compliance, which tells you where the industry is headed: cleaner, more reliable data standards, whether fleets like it or not.
Why this is getting worse in 2026, not better
Tech stacks are bigger now. TMS, ELD, telematics, trailer tracking, fuel cards, visibility platforms, shipper portals, EDI, APIs, driver apps, maintenance systems. More tools, more “events,” more opportunities for mismatch.
Integration friction is real: different formats, different update frequencies, different definitions for the same field, and partners who map data differently.
At the same time, shippers are demanding more accurate, predictive ETAs and disruption alerts, not vague check calls. Many are explicitly calling ETA accuracy a core value of AI-enabled transportation networks.
So the bar is rising, and sloppy data gets exposed faster.
ETAs are only as good as your “events”
Most fleets think their ETA problem is a software problem.
It’s usually an event problem. Here’s a common scenario I’ve seen play out:
A driver hits the receiver, but the app does not capture “arrived” because location services were off, the geofence was too tight, or the yard entrance is a quarter mile from the dock. Dispatch thinks the truck is still rolling, the customer sees a late ETA, and your team starts a chain reaction of calls. Two hours later, the driver leaves, and the system finally logs “arrival” when the truck passes back through the geofence on the exit road.
Now you have:
- A fake late delivery narrative
- No clean detention proof
- A customer who trusts the portal less
- A dispatcher who starts ignoring system ETAs and goes back to gut feel
That’s how bad data kills fleet optimization. It trains your operation to stop believing its own tools.
The solution: stop treating data cleanup like an IT project
If you want results, treat data quality like an operations discipline.
Not a dashboard. Not a one-time “data migration.” A discipline, like safety, maintenance, and dispatch strategy.
Here’s a practical playbook that works without turning your company into a science experiment.
The 30-day “Clean Ops Data” sprint
1) Pick 12 data fields that actually move money
Do not boil the ocean. Start with the fields that impact service, billing, and utilization:
- Appointment time (scheduled)
- Arrival time (actual)
- Depart time (actual)
- Loaded vs empty miles
- Stop type (shipper, receiver, yard, fuel, scale)
- Reason codes for delay
- Detention start and end triggers
- Accessorial codes (lumper, layover, TONU, etc.)
- Trailer number accuracy
- Driver HOS availability at tender time
- Macro location (city/state) normalization
- Customer facility ID (not just a free-typed name)
If your team cannot define these cleanly, your “AI dispatch” conversations are premature. Period.
2) Write a one-page data dictionary, then enforce it
Most fleets have five different meanings of “arrived.” That alone will wreck every KPI.
Define:
- What counts as “arrived” (geofence entry, manual check-in, guard shack, dock check-in)
- What counts as “on-time” (appointment time vs delivery window)
- What counts as “empty miles” (deadhead to pickup vs yard move vs bobtail)
One page. Shared. Non-negotiable.
3) Remove free-text billing from the process
If accessorials live in notes, you are donating revenue.
Create a simple rule: if it’s billable, it must be coded. Notes can support the claim, but codes trigger the workflow.
4) Fix your geofences and event logic
This is where most fleets stop, because it feels “technical.” It’s not. It’s money.
- Expand geofences at facilities with yards, guard gates, and multi-entrances.
- Require a backup event option: a one-tap “arrived” button with photo or gate timestamp when geofence fails.
- Audit your top 25 facilities by volume. Those are your biggest leakage points.
5) Run a weekly exception review, not a monthly post-mortem
Every Friday, 30 minutes:
- Top 10 loads with ETA misses
- Top 10 loads with missing arrival/depart events
- Top 10 detention disputes
You are not blaming anyone. You are tightening the system.
6) Stop feeding garbage into your planning
If your dispatch board shows impossible drive times, fix the inputs before you “optimize.”
This is where fleets waste money: they force dispatchers to work around bad data, and then wonder why adoption fails.
Telematics and fleet data are powerful, but only when you trust them enough to make decisions from them.
7) Assign one owner: the Data Foreman
Not an analyst who builds charts. An operations person who protects definitions, monitors exceptions, and pushes corrections back into the system.
If nobody owns the data, everyone suffers from it.
What good looks like (and why it pays fast)
When data is clean, three things happen immediately:
- Dispatch strategy gets sharper Your planners stop padding transit and stop gambling on HOS.
- Fleet optimization becomes real Not “we bought software,” but actual improvements in utilization and fewer self-inflicted service failures.
- You win arguments with proof Detention, late delivery disputes, service scorecards, claims timelines. Clean timestamps change outcomes.
And in a world where networks are pushing harder on predictive visibility and better ETA quality, clean data is not optional anymore. It is the entry fee.
If your operation is still running on “close enough” data, you are paying a daily tax you never approved.
Clean data is not a tech upgrade. It’s an operational upgrade.
Start small, define what matters, enforce it, and make exceptions visible every single week. Do that, and you will feel the difference in cost-per-mile, on-time performance, and dispatcher sanity faster than any shiny new tool can deliver.
About the Author:
Bhavya Vashisht is the Director of Operations at Canamex Carbra Transportation and the voice behind Truck & Trade Trends. He shares field-tested insights from the frontlines of U.S. trucking and logistics to help fleets operate smarter, safer, and more profitably.
👉 Connect with me on LinkedIn (Bhavya Vashisht) for more insights on trucking, logistics, and fleet optimization.
👉 Subscribe to the Truck & Trade Trends newsletter to stay ahead of weekly supply chain updates, real-world fleet stories, and strategies shaping the future of logistics
Truck & Trade Trends 
Leave a comment