In 2006 mathematician Clive Humby famously declared that “data is the new oil.” Nearly two decades later, as we navigate roads filled with increasingly connected vehicles, this metaphor feels awfully familiar, the gold rush. Every journey we take, whether it’s your morning commute through or a delivery run along a city’s road network, there is a constant digital exhaust trail. This data, invisible to most drivers yet immensely valuable to those who capture it, represents one of the most significant yet under examined value transfers of our time.
Now unlike oil, which must be extracted from finite reserves, the data we are talking about is “renewable resource”, generated continuously, and created through the simple act of travelling on roads that we, the public, have collectively funded and maintained.
The Data Harvest: What Modern Vehicles Actually Collect
To understand the hidden economy of vehicle data, we must first grasp the sheer scope and variety of information that modern cars generate. The data collection extends far beyond what most drivers realise, touching nearly every aspect of their journey and behaviour.
Location and Movement Intelligence
At its most basic level, every connected vehicle functions as a sophisticated tracking device. GPS coordinates are captured continuously, creating trails of where vehicles travel, when they stop, and how long they remain stationary. This location data can be augmented with speed profiles, route preferences, and timing patterns that with the right data science, reveal the intimate details about daily routines.
As the car becomes more computer, increasingly advanced systems can distinguish between motorway driving, urban navigation, and car park manoeuvres. They track whether a driver tends to take the most direct route or prefers scenic alternatives, whether they’re prone to speeding, and how their driving patterns change across different times of day and weather conditions.
Vehicle Performance and Predictive Analytics
Beyond movement, vehicles continuously monitor their own mechanical state. Engine performance metrics, braking patterns, acceleration profiles, and fuel consumption create detailed pictures of both vehicle health and driver behaviour, great if just for predictive maintenance. For electric vehicles, this extends to charging patterns, battery performance, and energy consumption across different driving conditions.
This telemetry serves immediate safety and maintenance purposes, but can, or rather is used to create predictive analytics that can forecast when components might fail, how driving habits affect vehicle longevity, and what maintenance scheduling might be optimal. Such information has obvious value for manufacturers, insurers, and service providers.
The Personal Layer: Biometrics and Behaviour
Increasingly, vehicles are gathering data about the humans inside them. Driver monitoring systems use interior cameras and sensors to track eye movements, facial expressions, and signs of fatigue or distraction.
Some manufacturers have implemented systems that monitor voice patterns for stress indicators, analyse driving behaviour for signs of impairment, and even track physiological responses through steering wheel sensors. As autonomous features expand, these human monitoring systems are becoming more sophisticated but also more intrusive when the data leaves the vehicle for other purposes.
Digital Life Integration
When you connect your smartphone to your car, that benefit for you of maps, music and the like expands the potential data take dramatically. Your contact lists, call logs, text messages, and application usage patterns. Even seemingly innocuous features like automatic calendar integration can reveal appointment patterns, work schedules, and personal relationships.
Environmental and Contextual Data
Vehicles equipped with advanced sensors also function as mobile environmental monitoring stations. They can collect data on road conditions, weather patterns, air quality, and traffic flows. This information, when aggregated across fleets, creates valuable datasets for urban planning, environmental monitoring, and infrastructure management.
However, this environmental data is typically collected alongside personally identifiable location and timing information, making it difficult to separate the valuable societal insights from the privacy-sensitive personal data.
The Value Chain: Who Profits and How
The transformation of vehicle data into commercial value operates through several interconnected markets, each representing billions of pounds in global economic activity.
Insurance: Risk Assessment in Real-Time
The insurance industry has been amongst the most aggressive adopters of vehicle data, fundamentally reshaping how risk is assessed and premiums calculated. Traditional insurance relied on demographic proxies and historical claims data. Today’s telematics-based policies can adjust premiums based on actual driving behaviour, captured in real-time, you didn’t need to opt in for a black box to be installed, it was there already.
Insurers value this data because it enables more precise risk assessment. Harsh braking events, rapid acceleration, cornering speeds, and time-of-day driving patterns all feed into algorithmic risk models. Some insurers now offer significant premium discounts for drivers willing to accept continuous monitoring, creating powerful incentives for data sharing.
Advertising: Location-Based Commercial Targeting
Vehicle location data represents a goldmine for advertisers seeking to target consumers based on their physical movements and routines. Knowledge of where someone drives, when they visit certain locations, and how frequently they return to specific areas enables extraordinarily precise advertising targeting.
The data aggregators can help retailers identify optimal locations for x y z store. Retailers can identify potential customers who frequently drive past their stores but never visit. Restaurants can target drivers who regularly travel through specific areas during mealtimes. Service providers can reach vehicle owners just as their cars approach service intervals or when they frequently drive through areas where maintenance services are available.
This location-based advertising market has grown rapidly, with vehicle data providing some of the most accurate and valuable targeting information available to advertisers. Unlike smartphone location data, which can be turned off or spoofed, vehicle location data is typically more reliable and harder for you, the consumer, to control.
Data Brokers: The Invisible Marketplace
Perhaps the most opaque aspect of the vehicle data economy involves data brokers, companies that aggregate, process, and resell data across industries. Vehicle data flows into this ecosystem through various channels: direct partnerships with manufacturers, agreements with telecommunications providers, and integration with mapping and navigation services.
Data brokers combine vehicle data with other information sources to create comprehensive consumer profiles. Shopping patterns, demographic information, financial data, and online behaviour can all be correlated with driving patterns to create valuable marketing personas.
The value of this combined data often far exceeds the worth of vehicle data alone. A data broker might combine information about where someone drives with knowledge of their online purchases, creating insights about potential demand for products or services in specific geographic areas.
It also gets worse than this, but I will come back to that at the end.
Manufacturers: Beyond the Initial Sale
Vehicle manufacturers themselves represent significant consumers of the data their products generate. This information helps them understand how vehicles are actually used, which features are popular, and how different components perform under real world conditions.
More strategically, vehicle data enables manufacturers to shift from one time sales to ongoing service relationships. Predictive maintenance services, OTA (over the air) updates, and subscription-based features all depend on continuous data flows from vehicles to manufacturers.
Some manufacturers are also exploring direct monetisation of vehicle data through partnerships with insurers, advertisers, and data brokers. This represents a fundamental shift in business models, where vehicles become ongoing revenue generators rather than discrete products.
The Scale of Value: Quantifying the Data Economy
Estimating the precise value of vehicle data proves challenging due to the fragmented and often opaque nature of the market. However, from SEC filings to Companies House accounts, these all show the scale is substantial and growing rapidly.
Various industry analysis reports report that automotive data monetisation could represent a market worth £650 billion globally by as soon as 2030. For individual vehicles, estimates vary widely, but some analysts suggest that the data generated by a single connected car could be worth £5,000 to £8,000 annually to data collectors and processors. Your data, created by you, doing whatever it is you are doing, driving on public roads, in your own vehicle. Public Roads, Private Benefit.
These numbers we are talking about are particularly striking when considered in the context of vehicle ownership costs. For many drivers, the annual value of their data approaches or exceeds what they spend on fuel, insurance, or maintenance. Yet unlike these costs, which drivers can see and control, data value extraction typically occurs invisibly and the benefit, well its not for them.
The Distribution Problem: Who Captures What Value?
The current distribution of value from vehicle data raises fundamental questions about fairness and equity. Drivers provide the data through their journeys and behaviour, yet they typically receive minimal direct compensation. Vehicle manufacturers, technology companies, insurers, and advertisers capture some of the economic value and then there are the data brokers with the lions share.
This asymmetry is particularly pronounced given that vehicle data is generated through the use of public infrastructure. Roads, traffic management systems, and navigation infrastructure are funded through public investment, yet the data value they enable typically flows to private entities.
Some manufacturers offer minor incentives for data sharing, it might be discounted insurance, free navigation updates, or enhanced services, but these represent a tiny fraction of the data’s commercial value. On top of all of this, the vast majority of value extraction occurs without explicit consent or compensation.
Emerging Models: Towards More Equitable Data Governance
Recognising these inequities, various stakeholders are beginning to explore alternative approaches to vehicle data governance. Some European initiatives are experimenting with “data trusts” that would aggregate vehicle data for public benefit whilst ensuring individual privacy protection.
Other models involve more direct compensation for data subjects. Some insurance companies now offer transparent data-sharing agreements where customers receive clear financial benefits in exchange for detailed driving data. A few technology companies have experimented with direct payments to drivers willing to share comprehensive vehicle data. Have you tried reading the End User Licence Agreement on your own in car systems?
Cities are also beginning to assert greater control over data generated on their streets. Some Cities now require ride-sharing companies and delivery services to provide anonymised trip data in exchange for operating licences, whilst not a wide spread practice, it represents a small step in the right direction and ensuring that public infrastructure generates public value.
The Road Ahead: Balancing Innovation and Equity
Vehicle data undoubtedly offers significant potential benefits. Improved road safety, more efficient traffic management, better urban planning, and enhanced emergency response all depend on access to detailed mobility data. The challenge lies in ensuring that these benefits are realised whilst addressing the current inequities in value distribution.
This will likely require new frameworks for data governance that explicitly balance innovation incentives with public benefit and individual rights. Rather than rejecting data collection entirely, the goal should be creating systems where the value generated from public infrastructure and personal data serves broader societal interests alongside commercial ones.
Returning to my point of “It also gets worse than this”. The connected and autonomous vehicle sector has spawned a particularly troubling ecosystem of data brokers who harvest intimate details about drivers’ movements, destinations, and behaviours, all with minimal oversight.
Vast databases have been created from location data from telematics providers to those free mobile apps which capture more than you know. This has enabled the creation of profiles that law enforcement agencies today increasingly exploit to circumvent traditional warrant requirements.
The real-world consequences of the data brokers impact became starkly apparent in a 2021 data breach which exposed the location histories of 5 million customers. So what? Who, where, when in the data lead to domestic abuse victims being tracked by their perpetrator who in turn had purchased the leaked information from dark web markets. A 2022 data leak, went further than just providing who where when as data points and actually enabled car thieves to locate and steal hundreds of vehicles by accessing real-time positioning data, whilst simultaneously exposing the daily routines of vulnerable individuals to potential stalkers and criminals.
These incidents feel dystopian enough to have come from the pages of a novel, sadly not, this happened and continued to happen. It really highlights how the commodification of automotive data has created a surveillance infrastructure that not only erodes privacy but actively endangers public safety, particularly affecting women, minorities, and other vulnerable populations who are disproportionately targeted by those seeking to exploit such granular personal information. Worse than you thought?
Conclusion
The data as “the new oil” is particularly apt when applied to vehicle data. Much like oil, it represents enormous economic value. Like oil extraction, it often occurs with minimal compensation to those whose resources enable its collection. And like the oil economy, it risks creating concentrations of wealth and power that probably won’t serve broader societal interests.
But unlike oil, vehicle data is renewable and generated through activities that serve multiple purposes beyond resource extraction. The challenge now is ensuring that this resource serves the public interest as effectively as it serves private profit.
As we continue our journeys on public roads, we should remember that we are not merely drivers, we are also data producers contributing to a multi-billion-pound economy. The question is whether we will allow this contribution to remain invisible and uncompensated, or whether we will demand governance frameworks that recognise and reward the public’s role in creating this valuable resource.
Next in the series: “From Smart Cars to Surveillance Cars” will examine how the data collection capabilities of connected vehicles extend into surveillance territory, with implications for privacy and civil liberties that reach far beyond commercial data use.
References
Humby, C. (2006). “Data is the new oil.” Presentation at Association of National Advertisers (ANA) Senior Marketer’s Summit.
McKinsey & Company (2021). “Unlocking the full life-cycle value from connected-car data.” McKinsey Insights.
Deloitte (2019). “Monetizing data in the age of connected vehicles.” Deloitte Insights.
KPMG (2020). “2020 Autonomous Vehicles Readiness Index.” KPMG Global.
European Data Protection Board (2021). “Guidelines 04/2021 on codes of conduct as tools for transfers.”
Stilgoe, J. (2018). “Machine learning, social learning and the governance of self-driving cars.” Social Studies of Science, 48(1), 25-56.
Transport Research Laboratory (2020). “Data governance for connected and autonomous vehicles.” TRL Report PPR912.
Centre for Connected and Autonomous Vehicles (2021). “Connected and automated mobility 2025: Realising the benefits of self-driving vehicles in the UK.” Department for Transport.
Zuboff, S. (2019). “The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power.” Profile Books.
“Data Broker Helps Police See Everywhere You’ve Been with the Click of a Mouse: EFF Investigation” (September 1, 2022)
“Closing the Data Broker Loophole” Available at: https://www.brennancenter.org/our-work/research-reports/closing-data-broker-loophole
