Your Tractor, Your Data: How the EU Data Act Enables Predictive Maintenance for Farmers

Most maintenance programmes fall into one of two categories:

• Reactive maintenance: you fix problems when something breaks. This is simple and often cheaper in the short term, until a failure occurs at the worst possible moment.
• Scheduled maintenance: you service equipment at fixed intervals. This is generally better than waiting for a breakdown, but it can mean replacing parts that still have life left, while missing faults that develop faster under heavy use.

Predictive maintenance is different. Instead of relying on the calendar, or waiting for failure, you use performance and condition data to identify when maintenance is actually needed. Uneven tire wear, higher-than-normal engine temperatures, or a gradual decline in hydraulic pressure typically show up as patterns long before there is an obvious, visible problem.

For farmers, the advantages are practical and immediate:

• Replace tires because the data shows they need replacing, not because a timetable says so.
• Identify emerging issues in the off-season, rather than during harvest.
• Make decisions based on real-world usage, rather than test-track assumptions.

The long-standing obstacle has been access to the underlying data. That is where the Data Act matters.

The Data Act establishes a clear principle: users of connected products should be able to access the data those products generate. For farmers operating modern tractors, harvesters, sprayers, and increasingly connected components such as tires—this creates a route to obtaining performance and condition data that is directly relevant to maintenance.

For predictive maintenance, that data is precisely what you need, including:

• tire pressure trends over time
• wear indicators and wear patterns
• load and stress profiles
• operating temperatures
• fault codes and performance trends
• component-level performance metrics

Previously, manufacturers and authorised dealers could use this information to generate recommendations, often without giving farmers the same visibility. With access rights in place, farmers can obtain the data themselves and, crucially, share it with third parties.

That matters for competition and quality of service. An independent tire specialist looking at your actual wear patterns can advise far more accurately than someone working from general assumptions. Likewise, an independent mechanic with real operational data can diagnose emerging issues with greater confidence. When multiple service providers can work from the same underlying information, you should see stronger price and service competition.

Access should be straightforward. In practice, you should expect the following.

1. Identify what data your equipment collects: Data varies by machine and model, but common maintenance-relevant data includes operating hours, component temperatures, pressure readings, wear indicators, load data, fault codes, and trend information. Manufacturers should provide information about what is collected and how it can be accessed.
    

2. Request access in writing: You can request access from the manufacturer or through the dealer channel. Ask for the machine’s maintenance and performance data, including real-time access where available and the ability to download historical data. You should not need to justify the request.
    

3. Expect usable formats: The data should be provided in a structured, machine-readable form suitable for analysis or import into farm management or maintenance software. It should not be restricted to unusable formats (for example, static PDFs) or locked into a single proprietary interface.
    

4. Expect no charge for raw access:  Manufacturers should not charge for access to the raw data itself. They may charge for value-added services (analytics, dashboards, reports), but the underlying data should be accessible without a separate access fee.

Once you have access, the key is to treat your data as an operational tool, not a collection of one-off readings.

• Track trends, not snapshots A single tire pressure reading tells you very little. Pressure patterns over weeks and months can reveal slow leaks, degradation, or mounting issues.
   
• Compare across similar machines If one tractor’s tires wear significantly faster than another operating similar routes and tasks, that difference is meaningful. It may indicate heavier loads, alignment problems, or operator and field-condition factors worth addressing.
   
• Link performance to conditions Data becomes more valuable when tied to context. Faster tire wear in a particular field may point to soil conditions, drainage issues, or terrain factors. Seasonal stress patterns may indicate when preventative servicing is most cost-effective.
   
• Share data with independent specialists The ability to share data with third parties is one of the most practical benefits. A tire specialist reviewing real wear patterns under your real conditions can give more accurate replacement advice than generic guidance. A mechanic with engine and temperature data may spot deterioration before routine dealership checks flag anything.
   

• Ask the right questions when buying equipment What maintenance data is collected? How do you access it? Can you download it? Can you share it with a third party? Treat data access as a procurement issue, not an afterthought.
• Audit your existing fleet Identify what each machine currently records and what is already accessible. Equipment acquired before September 2025 may still fall within scope depending on the product and circumstances—so it is worth asking what is available now.
• Find the right people to interpret the data The value is not only in the data you hold, but in who can turn it into decisions. Independent mechanics, tire specialists, and agronomy or operations advisers may all play a role.

For years, the data that could make maintenance smarter has been effectively locked inside manufacturers’ ecosystems. Farmers owned the machinery, but not meaningful access to its data. The Data Act changes that dynamic. Predictive maintenance is not about technology for its own sake. It is about preventing breakdowns at harvest, extracting maximum value from parts that still have life in them, and fixing problems when they are cheaper and easier to address. The data to do this has existed all along. Now, you can use it. You work hard to maintain your equipment. Your data can work just as hard for you.