While the industry has embraced what data can deliver commercially, far less attention has been paid to where that data comes from and whether it has been sourced and used responsibly, argues Chris Wright, regional vice president for Solera, heading up Cap HPI and Audatex UK.
Dealers rely on data to value stock, assess credit risk, target customers and, increasingly, power the AI-driven tools that are reshaping how cars are bought and sold. It has become a core part of how the industry operates.
Yet scrutiny of the data’s ethical quality is often limited. Many dealerships understand what their data can do, but far fewer know where it came from, how it was gathered or what controls sit behind it, and that imbalance is becoming harder to ignore.
Unethical data gathered through questionable scraping practices, unclear supply chains or poorly documented sources creates commercial exposure as well as compliance concerns. A dealer whose pricing models, finance decisions or customer profiles are built on poorly governed data is making decisions on uncertain foundations. Responsibility does not disappear because the data came from a third party.
The issue extends beyond retail
The issue extends well beyond dealerships, with insurers, finance houses and other automotive stakeholders both contributing to and relying on vehicle data throughout the industry. Insurers provide valuable information that helps build a vehicle’s history and use data to support underwriting, pricing, fraud prevention, and claims management. Finance providers depend on trusted automotive data for credit decisions, residual value forecasting and asset risk assessment.
When the underlying data is wrong, incomplete or difficult to verify, poor decisions follow. Risk profiles can become distorted, lending and underwriting decisions compromised, claims outcomes affected and regulatory scrutiny increased. As automotive businesses become more connected, weaknesses in one part of the data chain can quickly spread elsewhere.
The quality, fairness and explainability of data used in automated and AI-assisted decision-making are attracting growing regulatory attention. For insurers, lenders, retailers and manufacturers alike, confidence in business decisions depends on confidence in the data that underpins them.
So what does ethical data look like?
Good data starts with collecting only the information genuinely required for a specific purpose, being clear with users about how that information will be used and retaining it only for as long as necessary. Building privacy and security into systems from the outset through measures such as encryption, anonymisation and auditable data trails is also essential. Most importantly, it means being able to demonstrate those standards when challenged.
Trusted datasets, anchored to recognised sources such as DVLA records, insurer data or verified telematics providers, offer accountability. Dealers should ask more detailed questions about how suppliers source, validate and maintain the data they provide. Vague assurances and opaque methodologies are no longer enough, particularly when AI-generated outputs are involved.
Putting safeguards in place
AI raises the importance of getting this right, because the tools dealers are deploying for valuations, forecasting, and customer engagement are only as reliable as the information on which they were trained. Flawed or biased inputs influence outputs, whether that means inaccurate pricing, discriminatory lending decisions or misleading recommendations. Explainability is a business requirement, and suppliers should be able to explain how their models work and what safeguards are in place to identify and correct errors.
Other industries have already been through this process. Financial services and healthcare have spent years developing governance frameworks, audit procedures and accountability standards around data. Transport for London has incorporated ethical review into its data operations. Major technology companies have invested heavily in transparency and consent management. Automotive retail has made progress, but there is still work to do.
Changing the questions we ask
For dealers, that means changing the questions asked during supplier selection. Technology procurement should consider governance standards alongside functionality and cost, rather than treating them as separate issues. Due diligence should cover data provenance, retention policies, AI training practices, cybersecurity controls and the existence of formal governance processes.
Digital transformation has been the industry’s ambition for years. The next phase will be built on ethical data. Whether that transformation succeeds may depend on something far more fundamental: understanding where the industry’s data comes from and whether it can be trusted.
Author: Chris Wright, regional vice president, Solera, heading up Cap HPI and Audatex UK
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