Strategy and challenges

Modernizing Your Automotive Data Strategy

This document provides a data strategy for automotive executives. The strategy includes procedural, organizational, and technical guidance for leaders who want to advance the mission of their organization by making it more data-driven.

Overview

As an automotive executive, you work in a challenging environment where vehicle data is growing exponentially in size, variety, and complexity. Connected vehicles generate terabytes of telemetry data daily, while customer interactions span sales, service, warranty, and digital touchpoints. Engineering teams need more data, more quickly, to accelerate product development and improve vehicle quality. Regulatory compliance requires increased rigor around data handling and data sharing, particularly with new regulations like the EU Data Act mandating customer access to vehicle-generated data. Sophisticated bad actors frequently threaten data security, targeting both vehicle systems and enterprise data. Despite these challenges, you must improve customer experience and vehicle quality, make data available for product innovation and autonomous vehicle development, and optimize costs so that you can sustain your organization over the long term. This document presents how you can use a modern data mesh architecture to address these challenges and meet your goals.

A modern automotive data strategy can help organizational leaders meet many strategic objectives. It can help your organization improve across all aspects of the automotive value chain:

A cohesive, multimodal data strategy considers the entirety of a customer’s experience with your vehicles and brand—from initial research and purchase through ownership, service, and eventual trade-in or resale.

Data Challenges of Automotive Organizations

To provide optimal experiences for customers and insights that help engineering teams make good product decisions, automotive teams need high-quality data about vehicles, customers, and operations. Delivering the right data, in the right format, to the right person at the right time, is challenging for automotive IT, especially given the ethical and regulatory requirements for vehicle data handling. In addition, automotive innovations are constantly increasing the amount and complexity of vehicle data. According to industry analysts, connected vehicles generate up to 25 gigabytes of data per hour. By 2030, the global automotive data market is expected to exceed 750 billion dollars annually. Traditional automotive data-processing strategies struggle to support this rapid increase in data volume and complexity.

Many automotive organizations are improving customer outcomes by using predictive analytics. Organizations are also using advanced machine learning for autonomous driving, which requires processing massive amounts of sensor data in real-time while considering individual differences in driving patterns, road conditions, and vehicle configurations. Autonomous vehicle development is accelerating innovation, but it’s also creating novel data-processing challenges for automotive organizations. Standard approaches are also difficult to scale beyond single-vehicle testing to fleet-wide deployment. Automotive organizations must reduce the time from acquiring raw telemetry data to delivering actionable insights to engineering teams, service technicians, and customers. That information must be accurate, and it must be presented in a form that stakeholders can easily access, understand, and apply.

Automotive data is irreplaceable and is a highly valuable asset of many automotive organizations. Therefore, you must treat automotive data as an asset. Your automotive organization must earn customer trust and manage reputational risk by collecting and honoring customer consent and protecting data from improper access and use. Your automotive organization must simultaneously protect customer privacy, comply with rigorous, diverse regulatory constraints (including GDPR, CCPA, and the EU Data Act), and provide high-quality data quickly to engineers, service technicians, dealers, and customers. You must also decide whether you can safely monetize vehicle data in a way that is consistent with your brand values, your data security and privacy policies, and customer consent. Challenges include the following:

Traditional Automotive Data Pipelines Are Overwhelmed

Traditional automotive data pipelines are being overwhelmed because they were not built to handle these progressively more rigorous and challenging requirements:

Siloed Systems Prevent Comprehensive Views

Traditional systems are typically siloed. To provide a comprehensive view of the relevant data and the individual customer or vehicle, modern systems must be integrated and interoperable:

Single-Modality Systems Can’t Handle Modern Requirements

Traditional systems are often organized around a single data modality. Modern systems must be inherently multimodal:

Legacy Infrastructure Can’t Scale

Traditional systems were not designed to handle data at the scale and velocity required of modern systems:

On-Premises Constraints Limit Innovation

Traditional systems are typically designed to run on premises and are optimized for available IT resources. Modern systems must be able to take advantage of data storage and processing resources in hybrid on-premises–cloud environments and sometimes multicloud environments:

Regulatory Complexity Increases Risk

Modern automotive organizations face unprecedented regulatory complexity:

Automotive organizations that adopt and run on a modern automotive data mesh strategy position themselves to advance as innovation accelerates in connected vehicles, autonomous driving, and customer experience.

The Challenge: Traditional Data Architectures Don’t Scale

Traditional centralized data architectures create bottlenecks that prevent automotive organizations from realizing the full value of their data:

Organizational Bottlenecks

Technical Limitations

Governance Challenges

Why Data Mesh is the Solution

A data mesh architecture addresses these challenges through four core principles that we’ll explore in detail in the next section.