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Development of a Scalable Data Management Platform for Automotive Data Analytics
  1. case
  2. Development of a Scalable Data Management Platform for Automotive Data Analytics

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Development of a Scalable Data Management Platform for Automotive Data Analytics

grapeup.com
Automotive
Manufacturing
Information technology

Current Data Management Limitations

The client's existing data management solution lacked scalability, failed to support cross-functional workflows for developers, analysts, and non-technical users, and required modernization to enable advanced analytics, security compliance, and integration with third-party tools for marketing and performance monitoring.

About the Client

A leading manufacturer specializing in high-performance sports vehicles seeking to enhance data-driven decision-making across engineering, marketing, and operations.

Platform Enhancement Goals

  • Create a unified, scalable platform supporting multiple projects and use cases
  • Enhance data analysis capabilities for technical and non-technical users
  • Improve efficiency in data workflows while ensuring security compliance
  • Enable integration with analytics, marketing, and forecasting tools
  • Streamline onboarding for new projects and users

Core System Capabilities

  • Preconfigured notebooks for data analysis
  • Multi-account/project management interface
  • User-friendly tools for non-technical personnel
  • Integration with Google Analytics, HotJar, and Hubspot
  • Automated onboarding and service desk system
  • Collaborative user community platform
  • Scalable cloud infrastructure with AWS compatibility

Technology Stack Requirements

AWS
Terraform
Ansible
GitLab
Golang
Python

Third-Party System Integrations

  • Google Analytics
  • HotJar
  • Hubspot
  • Facebook Ads
  • LinkedIn Marketing Solutions

Operational Requirements

  • High scalability for future project expansion
  • Compliance with automotive industry security standards
  • 99.9% system uptime SLA
  • Cross-platform compatibility with existing workflows
  • Low-latency data processing for real-time analytics

Expected Business Impact

The platform will enable 30% faster data analysis workflows, support cross-departmental collaboration on 10+ concurrent projects, reduce onboarding time by 50%, and allow implementation of predictive use cases (e.g., weather forecasting modules, tire wear prediction algorithms), resulting in improved product development cycles and targeted marketing campaigns.

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