Vehicle telematics generates detailed data about identifiable employees: where they are, how fast they drive, when they stop, how long they take. In the EU, UK and an expanding list of jurisdictions, this is personal data and is regulated accordingly.
The framework
1. Identify a lawful basis. For employee monitoring, consent is usually inappropriate, because the imbalance of power between employer and employee means consent is rarely freely given. Most operations rely on legitimate interests, which requires a documented balancing assessment: your purpose, why the processing is necessary, and why it does not override the employee's rights.
2. Define specific purposes. "Fleet management" is not a purpose. Route optimisation, vehicle security, maintenance scheduling, safety improvement, customer ETA provision and evidence in disputes are purposes. Data collected for one purpose should not silently be used for another.
3. Apply data minimisation. Collect what the purpose requires. Continuous second-by-second tracking is harder to justify than periodic position reporting where the purpose is maintenance and utilisation.
4. Be transparent. Drivers must know what is collected, why, who can see it, how long it is kept and what rights they have. A clear written policy, explained rather than merely issued, is the standard.
5. Assess the impact. A data protection impact assessment is generally required for systematic monitoring of employees, and certainly for driver-facing cameras. Document it before deployment, not after a complaint.
6. Consult. In several European countries, consultation with works councils or unions is a legal precondition for deploying monitoring technology, not a courtesy. In others it is strongly advisable.
Before enabling any data collection, write one sentence: "We collect X in order to Y, and we cannot achieve Y with less data." If you cannot complete that sentence honestly, do not collect it.
Proportionality in practice
The test is whether a less intrusive means would achieve the purpose. Practical implications:
| Practice | Position |
|---|---|
| Tracking during working hours for operational purposes | Generally defensible |
| Tracking outside working hours | Difficult to justify; privacy mode expected where personal use is permitted |
| Continuous cab video | Hard to justify; event-triggered capture far easier |
| Behaviour data for coaching | Defensible with transparency |
| Behaviour data for automated disciplinary action | Requires care; automated decisions with significant effects attract specific rules |
| Location used to verify timesheets | Defensible if disclosed as a purpose |
| Live location visible to any manager | Access should be role-limited and logged |
Personal use of company vehicles is the recurring flashpoint. Where personal use is permitted, a privacy mode that suppresses detailed location outside working hours is the standard expectation — and it must actually work, not merely exist as a menu option.
Retention
Indefinite retention of granular driver location history is difficult to defend and creates unnecessary exposure. A defensible structure:
- Granular position data: a short period — many fleets use around 90 days — sufficient for dispute resolution and operational analysis
- Aggregated trip and behaviour summaries: longer, typically 12–24 months, for trend analysis
- Incident-related data: retained under legal hold for as long as the matter is live
- Video: short by default, days rather than months, with incident footage preserved separately
Set the policy, configure the platform to enforce it, and check that deletion actually occurs. Retention policies that exist only on paper are a common finding.
Employee rights
Drivers generally have rights to:
- Be informed about the processing
- Access their personal data, including telematics records about them
- Have inaccurate data corrected
- Object to processing based on legitimate interests
- Complain to the supervisory authority
Build a process for access requests before you receive one. A driver requesting their full telematics history is entitled to a response within the statutory period, and assembling it manually under time pressure is unpleasant.
Using the data fairly
Legal compliance is the floor. Practices that keep the system credible:
- Publish which reports exist and who sees them
- Never introduce a metric that people first learn about when it is used against them
- Investigate before acting — data raises questions rather than answering them
- Give drivers access to their own data
- Use behaviour data for coaching first, and for discipline only after a documented process
- Do not use location to micro-manage break timing
Fleets that get this wrong do not usually face a regulator. They face disconnected devices, grievances, industrial relations problems and data that nobody trusts.
Common questions
Do we need driver consent for telematics?
Usually not, and usually consent is the wrong basis because it cannot be freely given in an employment relationship. Most operations rely on legitimate interests, documented with a balancing assessment, plus clear transparency.
Can we track vehicles outside working hours?
Only with a purpose that justifies it — vehicle security is the usual one — and with proportionality. Where personal use is permitted, detailed location tracking outside working hours generally requires a privacy mode.
Is a data protection impact assessment mandatory?
For systematic monitoring of employees it generally is under GDPR, and it is certainly expected for driver-facing cameras. Even where not strictly required, it is the cheapest way to demonstrate that you considered the issues before deploying.
What happens if we get this wrong?
Consequences range from regulatory enforcement and fines to employment tribunal claims and industrial disputes. The more common outcome is practical rather than legal: a workforce that resists and undermines the system, which destroys the return on the investment.
Can drivers see their own data?
They generally have a legal right to it, and giving them routine access is good practice regardless. Drivers who can see their own data are far more likely to trust the system and to accept coaching based on it.
