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Development of a Big Data Analytics Platform for Enhancing Mobility Asset Development
  1. case
  2. Development of a Big Data Analytics Platform for Enhancing Mobility Asset Development

Development of a Big Data Analytics Platform for Enhancing Mobility Asset Development

99x.io
Automotive

Identifying Data-Driven Challenges in Vehicle Development Processes

The client faces a shortage of skilled software engineers capable of managing complex systems over extended periods, hindering the effective development and enhancement of mobility assets. They need a scalable, integrated data platform to streamline data collection, processing, and analysis for vehicle development projects, while ensuring rapid team onboarding and workflow alignment amidst evolving technology stacks.

About the Client

A mid-to-large sized automotive technology firm specializing in mobility solutions, focused on leveraging data to optimize vehicle development and lifecycle management.

Goals for Building a Next-Generation Mobility Data Analytics Solution

  • Accelerate vehicle software development timelines through enhanced data-driven insights.
  • Improve collaboration between internal R&D teams and external technical partners by providing integrated analytics tools.
  • Expand platform capabilities to enable real-time data collection, enrichment, visualization, and model sharing.
  • Support seamless integration with existing R&D tooling and external data sources via robust APIs.
  • Enhance user experience with customizable dashboards and geospatial query capabilities.
  • Ensure the platform is scalable, reliable, and secure to accommodate large datasets and sensitive information.

Core Functional Components of the Vehicle Data Analytics Platform

  • Real-time data collection from vehicle sensors and systems.
  • Data enrichment by integrating external sources and filling missing values.
  • Automated big data analysis pipelines for pattern detection and model generation.
  • Generation and validation of analytical results with options for sharing and publication.
  • Customizable dashboards for visualization of analysis results, including geographic-based queries.
  • APIs for seamless integration with existing R&D tooling and data download in multiple formats.

Technologies and Architecture for the Data Platform

Python for data processing and analysis
FastAPI for API development
SQL-based databases (e.g., MSSQL Server) for structured data storage
Cloud platforms (e.g., AWS) for scalable infrastructure
Object storage solutions (e.g., S3) for data lake management
Messaging services (e.g., SQS) for real-time data streaming
Jupyter notebooks for data exploration and analytics
Version control (e.g., Git)
Collaborative documentation tools (e.g., Confluence)
Project tracking systems (e.g., Jira)

Essential External System Integrations

  • Vehicle data sources for real-time data ingestion
  • External datasets for enrichment and analysis
  • Existing R&D tools via APIs for data integration and workflows
  • Visualization systems for dashboards

Non-Functional System Design Priorities

  • Scalability to handle increasing data volume from a growing fleet of mobility assets
  • High performance with real-time data processing capabilities
  • Security measures to protect sensitive vehicle and user data
  • Availability and reliability to ensure continuous data ingestion and analysis
  • Maintainability and ease of integration with existing development workflows

Projected Business Benefits from the Data Platform Initiative

By implementing this data-driven vehicle development platform, the client aims to accelerate development cycles, improve collaboration efficiencies, and enhance platform functionalities, leading to increased operational velocity and a more innovative product pipeline. Anticipated outcomes include faster project delivery, better decision-making through advanced analytics, and a more flexible platform that supports future growth and technological advancements.

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