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Scalable Analytics Dashboard Platform to Enhance Data-Driven Decision Making and Stakeholder Engagement
  1. case
  2. Scalable Analytics Dashboard Platform to Enhance Data-Driven Decision Making and Stakeholder Engagement

Scalable Analytics Dashboard Platform to Enhance Data-Driven Decision Making and Stakeholder Engagement

appsilon.com
Supply Chain
Business services

Challenge in Demonstrating Business Value and Driving Adoption of Internal Analytics Tools

The client faces difficulties in showcasing the value of their internal analytics applications to stakeholders, which hampers budget approvals for further development. They require a unified platform that enables rapid development, deployment, and monitoring of data-driven applications across various departments to improve operational efficiency and strategic decision-making.

About the Client

A mid-to-large size supply chain management company seeking to optimize operations through advanced data analytics and user engagement tools.

Objectives for Developing a Scalable Data Analytics and User Adoption Monitoring Platform

  • Rapidly develop and deploy customizable analytics applications to demonstrate business value and secure stakeholder buy-in.
  • Create a user monitoring system to track adoption rates and usage patterns across multiple analytics tools.
  • Scale the deployment of multiple analytics applications to different departments, enhancing interdepartmental data utilization.
  • Develop a centralized, scalable infrastructure supporting integration with diverse technologies and dashboards.
  • Facilitate ongoing training and support to empower data scientists and analysts in application deployment and usage metrics analysis.
  • Influence technology licensing decisions by providing tools that justify continued investment based on usage and business impact.

Core Functional Requirements for a Scalable Analytics Ecosystem

  • A flexible framework enabling rapid creation of interactive analytics dashboards with reusable templates.
  • User activity tracking and analytics to measure application adoption, engagement, and impact.
  • Seamless interoperability between different technologies such as R, Python, and JavaScript frameworks.
  • Deployment capabilities on cloud infrastructure with secure access controls.
  • Integration with existing data sources, dashboards, and enterprise systems.
  • Support for embedding applications into broader digital platforms to enhance visibility and usability.
  • Tools for data scientists and analysts to develop, deploy, and monitor applications independently.

Preferred Technologies and Architectural Approaches

React for frontend development
Python for backend and automation scripts
R Shiny for rapid analytics dashboard creation
Interoperability frameworks between R and Python
Cloud-based infrastructure for scalability and security

Integration Requirements with External Systems

  • Data sources such as enterprise databases and APIs
  • Analytics dashboards and reporting tools
  • User authentication and authorization systems
  • License management and enterprise platform services

Key Non-Functional Requirements for System Performance and Security

  • Scalability to support over 700 concurrent users
  • High performance to enable rapid data processing and dashboard loading
  • Security compliance aligned with enterprise standards
  • Reliable operation with minimal downtime
  • Ease of maintenance and support for continuous development

Anticipated Business Impact and Value of the Analytics Platform

The new platform is expected to significantly enhance the client’s ability to rapidly develop and showcase analytics solutions, resulting in increased stakeholder confidence and quicker budget approvals. Monitoring tools will enable data-driven strategies for user engagement, driving wider adoption of analytics applications across departments. The scalable infrastructure will facilitate deployment of over 10 analytics applications, empowering multiple teams and automating workflows, ultimately leading to improved operational efficiencies and strategic decision-making capabilities.

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