How data governance impacts the effectiveness of artificial intelligence in the automotive and insurance sectors, improving data quality and decision-making
Artificial intelligence is increasingly discussed for the opportunities it offers in terms of efficiency, automation and decision support. In the automotive and insurance sectors, AI is already applied across a wide range of use cases, from risk assessment and claims management to driving behavior analysis and process optimization. Less attention, however, is often paid to what truly determines its effectiveness: data quality. Every AI system relies on this essential foundation and, without a solid data governance framework, even the most advanced models risk delivering unreliable or hard-to-interpret results.
Artificial intelligence does not operate autonomously. It learns from data, processes it and uses it to generate predictions or support decisions. In automotive and insurance contexts, this data typically comes from heterogeneous sources, including connected vehicles, telematics devices, sensors, digital platforms and complex information systems. When this information is incomplete, inconsistent or poorly contextualized, AI systems tend to replicate and amplify these issues. The garbage in, garbage out principle — whereby poor-quality data leads to unreliable outcomes — becomes particularly relevant when algorithm-driven decisions directly affect safety, pricing, risk management and customer experience.
It is within this context that data governance becomes a strategic factor. It is not merely a set of technical rules, but a framework that defines how data is collected, managed, controlled and used over time. Effective data governance ensures consistency across different data sources, traceability of information, clear accountability and alignment between data usage and its intended purpose.
The relationship between data governance and artificial intelligence is therefore deeply interconnected. On the one hand, a structured governance approach is a prerequisite for developing robust, explainable and trustworthy AI models. On the other, the growing adoption of AI in critical domains makes responsible data governance increasingly urgent, as algorithms no longer simply support decisions but often help shape them, with tangible impacts on processes, strategies and business models.
The goal is not to accumulate vast volumes of data, but to transform data into high-quality information. In an increasingly complex digital ecosystem, information quality becomes a key factor in building trust in technological systems and enabling truly data-driven decision-making. Governing data therefore means reducing uncertainty, mitigating risk and increasing the value of analytics, making artificial intelligence an effective and sustainable tool for both the automotive and insurance sectors.