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Development of an Offshore Data Analytics R&D Team for AI-Driven Enterprise Monitoring
  1. case
  2. Development of an Offshore Data Analytics R&D Team for AI-Driven Enterprise Monitoring

Development of an Offshore Data Analytics R&D Team for AI-Driven Enterprise Monitoring

newxel.com
Information technology

Identifying the Need for a Dedicated Offshore Data Analytics Development Team

The client faces challenges in scaling its data analytics and AI platform capabilities due to limited in-house resources and expertise. They require a specialized, autonomous offshore team capable of developing, maintaining, and enhancing enterprise-scale data analytics solutions, including real-time performance monitoring and AI-driven insights, to support multiple industries such as finance, ad tech, and ecommerce.

About the Client

A growing tech company specializing in AI and big data solutions, seeking to expand its R&D capabilities through offshore team development to enhance data analytics platforms.

Goals for Building an Autonomous Offshore Data Analytics R&D Team

  • Establish a highly skilled offshore development team comprising DevOps, Frontend, and Backend engineers specialized in data analytics and AI platform development.
  • Enable the offshore team to operate independently, deliver high-quality solutions, and contribute to long-term platform improvements.
  • Enhance the client’s ability to monitor and analyze business performance data in real-time across diverse industries.
  • Achieve continuous team growth and retention to support project scalability and ongoing innovation.
  • Improve internal efficiency and accelerate platform deployment timelines through dedicated offshore expertise.

Core Functional Requirements for the Data Analytics Platform

  • Real-time data ingestion and processing pipelines supporting various data types.
  • Artificial intelligence and machine learning modules for autonomous analytics and anomaly detection.
  • User interfaces dashboard for data visualization and performance monitoring.
  • Automated deployment and continuous integration/continuous deployment (CI/CD) pipelines for platform updates.
  • Role-based access control and data security mechanisms.
  • Cloud-native architecture leveraging scalable cloud services (e.g., AWS or similar) for high availability and flexibility.

Technologies and Architectural Preferences for the Data Analytics Platform

Cloud infrastructure (AWS, Azure, or Google Cloud)
Containers and orchestration (Docker, Kubernetes)
Programming languages (Python for backend development, React for frontend interfaces)
DevOps tools for automation and deployment
AI/ML frameworks for analytics modules

Necessary External System Integrations for Data Analytics Operations

  • Data sources including databases, file storage, and streaming platforms
  • Third-party APIs for additional data enrichment
  • Monitoring and alerting tools for performance tracking
  • Security and identity management services

Non-Functional System Requirements and Performance Metrics

  • System scalability to support increasing data volumes and user load
  • High performance with minimal latency for real-time data processing
  • Reliability with 99.9% uptime SLA
  • Strong security measures, including data encryption and access controls
  • Ease of maintenance and automated deployment processes

Expected Business Outcomes from the Data Analytics Platform Development

The new offshore R&D team will enable rapid development and deployment of advanced data analytics solutions, resulting in improved real-time business performance monitoring and AI-driven insights. This will support scalable platform growth, increase operational efficiency, and enhance the client’s competitive edge in multiple industries. Expected outcomes include accelerated project timelines, improved system reliability, and sustained team growth to meet ongoing analytics challenges.

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