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Services/Data & Analytics Platforms
Turn vehicle data into decisions.

Data & Analytics Platforms

Modern vehicles generate terabytes of telemetry data per day. Factory floors produce quality signals at millisecond intervals. The automotive enterprises that win are the ones that turn this data into actionable insight — in real time, at scale.

TL;DR

We design and build automotive data platforms on AWS, Azure, or GCP — from vehicle telemetry ingestion to executive dashboards. Core capabilities: real-time streaming (Kafka, Kinesis), data lakehouse architecture, and self-service BI rollouts. Typical time to first insights: 6–10 weeks.

Data is the fuel of the modern automotive enterprise — but raw data has no value until it reaches the people who need it, in the form they can act on. Most automotive organizations are sitting on enormous data assets in siloed systems: telematics platforms, MES historians, quality management tools, and ERP systems. We build the platforms that unify, process, and expose this data.

Data Platform Architecture

We design cloud-native data platforms using the lakehouse pattern: raw data stored in cost-efficient object storage (S3, ADLS, GCS), processed through transformation pipelines (dbt, Spark, Databricks), and served via high-performance query engines (Athena, Synapse, BigQuery). The architecture scales from startup-scale ingestion to OEM-scale telematics volumes.

Vehicle Telemetry & Connected Car Data

Connected vehicle data requires specialized ingestion: high-throughput MQTT or AMQP ingest, schema management for diverse ECU data formats, and GDPR-compliant data handling with consent management. We build vehicle data pipelines that comply with GDPR Article 9 requirements for sensitive personal data and integrate with existing fleet management platforms.

Manufacturing Quality Analytics

Factory floor data from PLCs, SCADA systems, and MES historians is often inaccessible to analytics users. We implement OPC-UA and MQTT-based edge data collection, aggregate it in cloud data platforms, and build quality dashboards that give production engineers real-time SPC (Statistical Process Control) views — replacing slow, manual Excel-based reporting.

Self-Service BI & Analytics Tooling

A data platform that only data engineers can use is a half-finished project. We deploy and configure self-service BI tools (Power BI, Looker, Tableau, Apache Superset) with governed data catalogs, semantic layers, and role-based access controls — so analysts, engineers, and executives can answer their own questions without IT tickets.

Our Approach

1

Data Discovery

Inventory data sources, assess data quality, map use cases to business value.

2

Platform Architecture

Design lakehouse architecture, select tooling, define governance and access model.

3

Ingestion & Pipelines

Build streaming and batch ingestion, implement transformation pipelines (dbt/Spark).

4

BI & Analytics Layer

Deploy BI tools, build semantic layer, create initial dashboards and data products.

5

Enablement & Self-Service

Train analyst and engineering users, document data catalog, establish data governance processes.

Frequently Asked Questions

How do you handle GDPR compliance for vehicle telemetry data?
Vehicle telemetry can constitute personal data under GDPR when it can be linked to an identifiable driver. Our platform architectures implement consent management at ingestion, pseudonymization pipelines, data retention policies enforced at the storage layer, and data subject request workflows. We design these controls to be auditable for GDPR Article 30 record-keeping requirements.
Can you integrate with our existing MES or ERP systems?
Yes. We have integration experience with SAP (ECC, S/4HANA, BTP), Siemens MindSphere, PTC ThingWorx, Rockwell FactoryTalk, and standard OPC-UA/MQTT-based factory systems. We use event-driven integration patterns where possible to avoid tight coupling between operational and analytics systems.
What is a data lakehouse and why is it better than a traditional data warehouse?
A data lakehouse combines the low-cost, schema-flexible storage of a data lake with the ACID transaction support and query performance of a data warehouse. For automotive, this means you can store raw telemetry cheaply, apply transformations without data movement, and serve both BI dashboards and ML model training from the same platform — reducing data duplication and infrastructure cost.
How do you handle data from hundreds of ECU types with different formats?
We implement schema registries (Confluent Schema Registry or AWS Glue Schema Registry) to manage the diversity of ECU data formats, and build normalization pipelines that transform raw signals into canonical data models. We also maintain compatibility with AUTOSAR signal databases and CAN/LIN/FlexRay DBC format parsers.

Turn Your Vehicle and Manufacturing Data into Decisions

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