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Development of Advanced Big Data Analytics and Visualization Platform for Media-Driven Customer Engagement

digiteum.com

Media

Advertising & marketing

Information technology

eCommerce

Challenges in Real-Time Data Interpretation and Actionable Insights

Feed.fm collects 7 million daily events and maintains 3.5 billion records, but struggles to transform raw playback data into actionable business insights. Legacy systems lack scalability for real-time analytics, hindering the ability to optimize music curation, measure engagement impact, and enable client-side data-driven decisions in competitive digital experience markets.

About the Client

A media technology company specializing in integrating contextual music experiences into digital platforms to enhance user engagement and conversion rates.

Strategic Goals for Data-Driven Platform Enhancement

Process 7+ million daily events with real-time analytics capabilities
Create interactive visualization dashboards for client-specific insights
Implement machine learning algorithms for predictive engagement patterns
Ensure horizontal scalability for growing data volumes
Enable cross-client comparative analytics for editorial decision-making

Core System Capabilities

Real-time data ingestion pipeline with ELK Stack integration
Client-specific admin dashboard with login protection
Machine learning-driven pattern recognition for music engagement trends
Aggregated analytics dashboard for platform-wide performance monitoring
Time-series data storage with HBase optimization

Technology Stack Requirements

Elastic Stack (Elasticsearch, Logstash, Kibana)

Apache Spark

Apache Hadoop HBase

Machine Learning algorithms (regression analysis, clustering)

Time-series data optimization frameworks

System Integration Needs

Third-party client application APIs
Web platform analytics tools
Payment processing systems for client billing
User authentication services (OAuth 2.0)

Operational Constraints

Horizontal scalability to handle 10x data volume growth
Real-time processing latency <500ms
99.99% system availability with failover mechanisms
Role-based access control for sensitive data
Cross-browser responsive visualization rendering

Anticipated Business Outcomes

Implementation will enable Feed.fm to process 100% of playback data in real-time, increasing client conversion rates by 20-30% through optimized music curation. The platform will reduce data interpretation time by 75% for editorial teams while supporting 10x growth in connected applications, maintaining competitive differentiation in the $30B digital experience market.