A new era of mobility part one: The future of connected vehicles

Nigel Hughes

A new era of mobility part one: The future of connected vehicles

By 2030, nearly all new vehicles sold globally are expected to be connected, enabling real-time data streaming and enhancing the internet of things (IoT) for vehicles. This connectivity facilitates a range of new services, from advanced maintenance and energy management to enhanced infotainment systems and smarter fleet management. Original equipment manufacturers (OEMs) are evolving from their traditional roles as hardware manufacturers to becoming key players in the digital and data-driven ecosystem. This transformation is driving the development of sophisticated data analytics and connectivity solutions that improve vehicle performance, safety, and user experience.

In this first blog post of a three-part series, we highlight the crucial role connected vehicle technology plays in the future of the automotive industry and how treating data as a product can transform OEMs.

Connected data and technology

Data brokers enable automotive companies to offer personalized services to their customers. By understanding individual preferences and behaviors, companies can tailor their offerings, such as customized infotainment options, maintenance reminders, and personalized marketing.

As vehicles become more connected, the role of data brokers has become crucial. These intermediaries fill the gap between traditional OEMs and customers across various sectors, including fleets, insurance, security, and traffic management. Data brokers help translate the vast amounts of data generated by connected vehicles into actionable insights and services, enhancing the value proposition for end users.

The role of the data broker

Currently, OEMs are primary generators of vehicle data. The numerous sensors and connected technologies on modern vehicles produce vast amounts of data related to vehicle performance, driver behavior, location, and usage patterns. This data is invaluable for improving vehicle design, safety features, and overall performance.

While some OEMs have focus primarily on using data internally to enhance their products and services, others are beginning to explore monetizing this data. By acting as data brokers, they can sell or share aggregated and anonymized data with third parties, such as insurance companies, fleet managers, and smart city planners.

Increasingly, we are seeing OEMs often collaborate with technology companies to leverage their expertise in data analytics and cloud computing. These partnerships help OEMs enhance their data processing capabilities and develop new data-driven services. The application of data as a product principles has been key to this approach.

Data as a product principles

Within an evolving automotive ecosystem, data must be treated as a product—a concept that has gained prominence in recent years through architectural frameworks like data mesh. Data as a product refers to the deliberate management and packaging of data for specific purposes, ensuring it is consumable, reliable, and valuable for various stakeholders, much like any traditional product. Whether data is used for reporting, powering applications, or feeding artificial intelligence (AI) models, it must be designed, governed, and maintained with the end user in mind.

At the heart of this approach is the notion of data products, which are any use of data that facilitates a specific business goal. A crucial element in treating data as a product is establishing technical contracts that define how data is exposed to users. These contracts specify the format of the data, whether it is available for real-time queries, or if it consists of a finite set of records meant for a specific use. Additionally, operational contracts play an equally critical role by outlining service levels, data quality standards, retention policies, and governance protocols that must be agreed upon between business and engineering teams.

This alignment between technical and operational agreements ensures data is not only technically sound but also operationally ready to serve the needs of the business. It moves beyond the traditional data management practices, where data was often delivered without accountability for quality, availability, or relevance. By contrast, data as a product involves an ongoing relationship between data engineering teams and business units, allowing businesses to extract maximum value from their data assets.

The shift toward product thinking

In the traditional data landscape, data was often siloed, dumped into repositories with little consideration for how it would be consumed or developed. As a result, businesses faced friction in leveraging that data, leading to underutilization. Data as a product changes this by wrapping a product team around the data, continuously developing and improving it to better meet the needs of its users. This team, often led by a product owner, ensures the data is fit for purpose and addresses evolving business needs.

The product owner acts as a crucial interface between the business and the data engineering teams, collecting feedback and translating business requirements into technical specifications. They ensure the data is delivered in a specific format, at the required frequency—whether daily, weekly, monthly, or in real time—and with a level of quality that stakeholders can trust. This approach helps to foster confidence in data, making it a reliable asset upon which businesses can build their operations.

For example, in the automotive sector, treating telematics data as a product can transform the way businesses use vehicle data. Traditionally, telematics data from connected cars was collected and stored without a clear understanding of its potential applications. However, by applying the data as a product approach, OEMs and businesses can unlock valuable insights, such as predicting when a car needs servicing, determining which parts require replacement, and analyzing driver behavior for insurance purposes. The product team can then present these insights to the business in a way that aligns with its goals, whether that’s through real-time dashboards, reports, or embedded services within applications.

The importance of domain-driven data products

One of the foundational elements in implementing data as a product is the breakdown of data into specific domains. From a connected vehicle perspective, it’s not just about gathering data from the car; it’s about understanding how that data should be categorized and managed according to its use cases. For instance, data related to vehicle servicing, driver behavior, or fleet management must be classified into separate domains. This domain-oriented approach enables businesses to organize their data products around these domains, ensuring they can be consumed by relevant stakeholders within the organization.

At Credera, this domain-based strategy has proven instrumental in helping automotive businesses make sense of the vast amounts of connected vehicle data they generate. By aligning data products with specific business functions, such as vehicle maintenance or insurance underwriting, we ensure the data is structured, governed, and delivered in a way that maximizes its value across different departments.

Operationalizing data as a product

A key differentiator in the data as a product model is the emphasis on operationalizing data delivery. This means data is not simply delivered once and forgotten but is continually developed and iterated upon. Regular updates, enhancements, and feedback loops ensure the data remains relevant to its users and aligned with business objectives. This continuous improvement cycle also involves setting clear service levels, such as how often the data will be refreshed, how quickly issues will be resolved, and the expected data quality thresholds.

In our own work with automotive companies, we emphasize embedding individuals with deep business knowledge within data engineering teams to ensure the data products are not only technically sound but also meet the specific needs of the business. This creates a seamless connection between those producing the data and those consuming it, allowing for more efficient communication and greater responsiveness to changing business requirements.

By approaching data with this product mindset, businesses can ensure their data assets are not just passive repositories but active enablers of innovation and value creation. Whether it’s improving vehicle performance, enhancing customer experiences, or optimizing operations, the possibilities for leveraging data are vast—provided the data is managed and treated as a product.

Closing thoughts

The drivers of change in the automotive industry are multifaceted, with data playing a central role. As sustainability, consumer behavior, public policy, and technology continue to evolve, businesses must adapt to remain competitive. By treating data as a product and leveraging its full potential, companies can unlock new opportunities and drive innovation in the mobility ecosystem.

How Credera can help

By leveraging our expertise in data technology and architecture, we can help businesses across the automotive and insurance sectors navigate an increasingly complex field. Our focus on understanding and managing data assets, coupled with our commitment to effective governance and controls, ensures we keep both our clients and their customers safe.

Up next: Monetizing connected vehicle data

In the next installment of our three-part series, we'll take a closer look at the practical use cases that demonstrate the financial value of connected vehicle data. From new revenue streams through partnerships to creating entirely new business models, stay tuned as we dive deeper into the monetization opportunities that await OEMs in the evolving data-driven landscape.