Unlocking Potential in a Changing Automotive Market: Part Two
How can automotive companies turn data into a competitive advantage?
In this second part of our three-part series, co-authored with global mobility leader Iveco Group, we uncover how a clear business vision, practical data-driven use cases, and a well-designed target data architecture can unlock real value and drive innovation.
Realizing Business Value Through Data-Driven Use Cases
The journey to achieving data maturity is not simply an IT-driven initiative. It is a fully transformative process that begins with aligning data strategy to the business value that a company aims to generate.
For automotive providers, in particular, this means focusing on data-driven use cases that directly create value. The first crucial step is to define areas of opportunity along the entire value chain—from research and development (R&D) to vehicle remarketing—and understand the role each stakeholder plays within this ecosystem.
The Value Chain and Key Stakeholders
Automotive companies must evaluate the impact of data-driven initiatives across several stages of their value chain. These include R&D, sourcing and manufacturing, distribution and sales, after-sales services, and vehicle remarketing. Each stage offers unique opportunities for optimization and innovation.
The impact of data-driven initiatives extends across a diverse range of stakeholders, each benefiting in distinct ways. For partners, these initiatives create opportunities to diversify revenue streams by leveraging data from existing collaborations. This can mean integrating insights from spare parts suppliers, insurance providers, charging station operators, and online platforms.
Internal stakeholders also gain significantly from enhanced data capabilities: optimized internal processes and improved operational efficiency are key outcomes. Finally, for customers and partners data provides a powerful tool for enhancing the value proposition of assets throughout their lifecycle. These data-driven insights improve customer satisfaction and foster stronger, more collaborative relationships.
Key Use Cases in the Automotive Sector
Data-driven transformation in the automotive industry can be realized through specific use cases that address critical business needs. Some of the most promising categories include integrating data from Manufacturing Execution Systems (MES) with machine learning algorithms; this allows companies to identify potential defects in real time, significantly enhancing both the quality and efficiency of production processes.
In the realm of sales, lead management tools are empowering dealers to better target their efforts. By leveraging data to generate accurate lead scoring, dealers can identify potential clients with the highest likelihood of conversion. This precision enables them to customize vehicle and service offerings to align with individual preferences, enhancing customer satisfaction and increasing the effectiveness of sales efforts.
Real-time vehicle residual value tracking is another innovation driving value in the automotive sector. Automated notifications, informed by factors such as a vehicle’s residual value, client payment behavior, and associated risk levels, enable dealers, OEMs and mobility players to proactively manage vehicle redemption strategies.
Prioritizing Use Cases for Maximum Impact
To implement these initiatives effectively, automotive companies must prioritize use cases based on their business impact and complexity. This approach groups use cases into four clusters:
- Foundation: Initiatives to be executed in the first 6–12 months, focusing on foundational activities like data model creation, as well as initial data collection.
- Growth: Use cases that build upon the foundational stage, typically implemented within 1–3 years to advance the company’s data capabilities.
- Consolidation: Long-term initiatives aligned with evolving business needs, requiring an elevated level of complexity, and offering medium-to-high business impact (beyond three years).
- Low priority: Use cases with moderate business impact and complexity, suitable for further evaluation in the long term.
By anchoring the data maturity journey in value-driven use cases, automotive companies are empowered to not only enhance their operational efficiency but also unlock new revenue streams and create exceptional customer experiences. This strategic alignment between data and business value helps to ensure long-term competitiveness.
Core Principles of a Target Data Architecture: Layers
Adopting industry-standard platforms and widely used frameworks is essential for simplifying system development, reducing costs, and accelerating the deployment of advanced features. By focusing on scalable solutions that align with evolving industry requirements, companies can ensure long-term compatibility while meeting immediate business needs.
A well-designed data architecture should reflect business priorities, prioritizing the deployment of capabilities that deliver tangible value. To support these objectives the architecture must function as a multi-layered system, integrating computational power with efficient data processing and delivery to both internal teams and external stakeholders (insurance companies, dealers, etc.).
Four primary layers define this architecture. First, the smart business layer, which empowers both internal and external stakeholders (insurance companies, dealers, etc.) with advanced capabilities and use cases across channels, processing raw data into actionable insights. Beneath this sits the data layer, which orchestrates the ingestion and distribution of data across the organization.
Supporting these functions is the core transactional layer, which handles the operational systems and workflows and the infrastructure layer, which supplies the computational power and storage capacity required to sustain the entire system. Together, these interconnected layers create a cohesive framework for leveraging data effectively.
Key Clusters of Data Platform Capabilities
After establishing the layers of the architecture, experience has indicated that automotive players now need to focus on five main clusters of capabilities to build an effective data platform:
- Ingestion and distribution: Facilitate large-scale, scheduled data ingestion from suppliers and business partners, ensuring seamless data integration.
- Serving layer: Provide native integrations via connectors to consumer systems or leverage custom APIs to deliver data as needed.
- Advanced analytics and AI: Analyze structured and unstructured historical data to uncover insights. AI capabilities enable synthetic data to be generated to simulate scenarios and validate accuracy, using regression models and statistical analysis.
- Data storage: Establish a central repository for structured, unstructured, and semi-structured data, forming the basis for further analytics and insights.
- Data enablers and governance: Implement robust data management, protection, and utilization frameworks to maximize the effectiveness of data assets across the organization.
In summary, to ensure a seamless transition automotive players should adopt an incremental approach to deploying use cases and building their target architecture. Starting with the IT capabilities that are most critical for initial use cases allows companies to create a foundation that supports continuous evolution and scaling.
By following these principles and focusing on a layered, capability-driven data platform, automotive leaders can better position themselves to take advantage of the full potential offered by data.
In the final part of our series (coming soon), we’ll explore how to build the teams and cultivate the data-driven culture required for long-term success.
