Break down silos between vehicle telemetry, customer records, and operational systems to deliver a single, governed view of your business across sales, service, and connected vehicle data.
Overview
This Guidance demonstrates how automotive organizations can address the challenge of fragmented customer data across multiple systems by integrating vehicle telemetry, customer experience data, and operational information into a unified platform for comprehensive insights. The platform connects internal CRM and service management systems with real-time vehicle diagnostics and supply chain data, creating a complete customer view through automated data processing and entity resolution. Natural language query capabilities enable teams to explore vehicle performance metrics and customer interactions without technical expertise, while machine learning models analyze patterns to predict maintenance needs and identify opportunities for proactive customer engagement. You can deliver personalized customer experiences based on real-time vehicle health data while maintaining compliance with data governance requirements across multiple regions.
Benefits
Unify your automotive data ecosystem
Accelerate predictive maintenance at scale
Deploy ML pipelines that analyze vehicle telemetry near real-time to detect issues like tire pressure anomalies before they become warranty claims, reducing costs and improving customer safety.
Simplify compliance with data regulations
Automatically classify and separate PII from anonymized vehicle data across regions, helping you meet EU Data Act and GDPR requirements while keeping R&D teams productive with governed data access.
How it works
Data platform overview
This architecture diagram illustrates how to build a scalable data mesh platform to deliver insights and analytics for automotive customers on AWS.
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Step 1
Internal platforms deliver collaboration, ERP, development, and CRM data to automotive data platforms in real-time, while external sources add vehicle telemetry, supply chain, and sales data for comprehensive analytics. Ingestion services handle data at varying scales and latencies, from millisecond sensor data to scheduled enterprise synchronization.
AWS IAM controls access to data lake resources, AWS CloudTrail provides an immutable audit log of all API calls and data access for compliance, and AWS Security Hub aggregates security findings while continuously monitoring against frameworks.
AWS Glue Data Catalog provides centralized management for all data lake assets, Lake Formation enforces fine-grained access controls at the database, table, and column level, and Amazon Athena enables serverless SQL queries with permissions inherited from Lake Formation policies for secure data access.
Amazon S3 provides scalable storage for the automotive data lakehouse, supporting Parquet files for columnar analytics, Apache Iceberg tables for ACID transactions and schema evolution, and vector embeddings for semantic search and AI-powered applications.
Amazon Quick Suite makes generative AI securely accessible throughout the organization, enabling natural language queries and conversational analytics with permissions-aware access to enterprise data. This allows automotive teams to visualize vehicle metrics, supply chain KPIs, and operational data with embedded insights and role-based access.
Amazon Bedrock AgentCore Gateway for tool integration enables natural language access to the automotive data platform, allowing users to query data without writing SQL or navigating dashboards.
Tire prediction ML pipeline
This architecture diagram illustrates how to build a machine learning pipeline to optimize vehicle data intelligence with a tire prediction ML model.
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Step 1
Vehicle telemetry data from connected cars, warranty claims from dealer management systems, and parts inventory from supply chain systems are ingested into Amazon Redshift databases. Amazon EventBridge triggers hourly AWS Glue ETL jobs that query and transform data from all three Redshift sources into standardized formats for downstream ML processing.
EventBridge triggers hourly Glue ETL jobs that query tire pressure readings, warranty coverage, and parts availability, transforming raw data into standardized formats for downstream ML processing.
AWS Glue processes telemetry through parallel pipelines: the ML ETL prepares features for model training, while the Root ETL validates and enriches sensor readings, with both pipelines storing intermediate results in Amazon S3 using Parquet format for efficient columnar analytics.
AWS Step Functions orchestrates the ML workflow from data validation through feature engineering, model training, evaluation, and deployment, while Amazon EventBridge schedules training runs weekly and inference jobs daily to maintain prediction accuracy.
A parallel filter-based pipeline uses statistical analysis to detect rapid pressure drops exceeding 2 PSI per hour, calculating leak rates through time-series regression and cross-referencing results with ML predictions for validation.
Amazon DynamoDB tracks alert state to prevent duplicate notifications, while AWS Lambda consolidates predictions from both ML and filter pipelines, assigns severity levels based on pressure drop rates, and delivers alerts to fleet management systems via REST API or Amazon SNS.
Amazon API Gateway provides real-time access to prediction data and alert history, enabling integration with dealer management systems, mobile apps, and customer notification workflows with sub-second latency.
Data governance and compliance
This architecture diagram shows how to handle PII classification and cross-region data governance for compliance with the EU Data Act and GDPR.
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Step 1
AWS Lake Formation serves as the global governance hub, enforcing fine-grained access control policies across all regions. AWS Glue Data Catalog maintains centralized metadata for all data assets. AWS Organizations and IAM manage multi-account structure and access permissions. AWS CloudTrail logs all data access for comprehensive audit trails, and Amazon Macie continuously monitors for PII compliance.
AWS Glue Data Quality validates incoming data. AWS Glue ETL Streaming performs real-time classification, separating telemetry into PII and anonymized data stores. The anonymization process includes structured data transformation and video/image anonymization via partner AI solutions.
The S3 Local PII Data Store contains precise GPS coordinates, driver information, and detailed vehicle identifiers. The S3 Anonymized Data Store contains hashed identifiers, city-level locations, and aggregated metrics.
Amazon Cognito authenticates users. The User Portal and Amazon API Gateway provide vehicle owners and authorized third parties access to their PII data as required by the EU Data Act and GDPR. AWS Lake Formation policies validate all access requests.
R&D teams access anonymized data through Amazon SageMaker for ML model training and Amazon Quick Suite for analytics dashboards. AWS Lake Formation ensures R&D teams can only access anonymized data—never PII.
AWS Lake Formation's centralized governance enforces consistent policies across regions. Vehicle owners access only their own PII data, while R&D teams access all vehicles' anonymized data. CloudTrail logs all access for compliance reporting, providing complete data lineage from ingestion through classification to consumption.
Customer 360 agentic AI
This architecture diagram details how to build a Customer 360 agentic AI platform for creating value and personalized experiences with a holistic view of the customer.
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Step 1
A Customer 360 platform unifies internal systems: CRM, service management, finance, and dealer networks with external data sources including connected vehicle telemetry, warranty claims, social media sentiment, and third-party market data.
AWS offers various ingestion pipelines to accommodate diverse data velocities: real-time streaming for vehicle diagnostics and customer interactions, near-real-time APIs for transactional updates, and batch processing for historical sales and demographic enrichment, creating a complete view of customer behavior, vehicle health, and lifetime value.
Processing pipelines leverage AWS Entity Resolution to deduplicate and link customer records across disparate systems. This unified customer identity is critical for accurate analytics and prevents diluting churn signals, enabling personalized engagement strategies that require understanding the complete customer relationship across sales, service, and connected vehicle interactions.
Amazon SageMaker Lakehouse enables a medallion architecture where raw customer and vehicle data lands in the bronze layer, undergoes cleansing and entity resolution in the silver layer to create unified customer profiles, and aggregates into gold layer tables with pre-computed metrics providing optimized tables for real-time dashboard queries.
Agentic AI systems built with Quick Suite autonomously investigate customer sentiment decline by orchestrating multi-step workflows: detecting negative NPS trends, querying vehicle telemetry for battery degradation patterns, analyzing support case histories for recurring issues, correlating with service appointment delays, and synthesizing findings into root cause reports with recommended interventions.
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