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Development of Scalable Real-Time Analytics Dashboard for Data Science Consultancy
  1. case
  2. Development of Scalable Real-Time Analytics Dashboard for Data Science Consultancy

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Development of Scalable Real-Time Analytics Dashboard for Data Science Consultancy

ahex.co
Information technology
Business services
Consulting

Business Challenges and Technical Limitations

The client required a centralized analytics dashboard to overcome challenges including real-time data integration from multiple sources, performance optimization for large datasets, creation of an intuitive UX for complex data visualization, scalability for growing data volumes, and delivery within strict time and budget constraints.

About the Client

A forward-thinking data science and consulting company providing businesses with deep insights from complex data across multiple industries.

Key Project Goals

  • Develop a highly intuitive analytics dashboard for complex data visualization
  • Ensure seamless integration with existing data analytics systems
  • Implement real-time data processing capabilities
  • Optimize user experience for collaborative data analysis
  • Deliver solution within defined timeline and budget

Core System Capabilities

  • Real-time data analytics with API-driven updates
  • Interactive customizable charts/graphs (Chart.js)
  • Multi-source data integration framework
  • Scalable microservices architecture
  • Role-based user authentication
  • AWS cloud-hosted deployment
  • Custom dashboard widgets

Technology Stack

ReactJS
ASP.NET
MySQL
Tailwind CSS
AWS
REST APIs
Git

System Integrations

  • Existing data analytics systems
  • Google Analytics API
  • Third-party data APIs

Non-Functional Requirements

  • High-performance data processing
  • Horizontal scalability
  • Enterprise-grade security
  • 99.9% system availability
  • Cross-browser compatibility
  • Responsive UI/UX

Expected Business Outcomes

Implementation of this analytics dashboard is expected to deliver real-time decision-making capabilities, reduce manual reporting time by 60%, improve data interpretation efficiency across teams, and provide scalable infrastructure to support 300% data volume growth over five years while maintaining compliance with enterprise security standards.

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