Development of an Advanced Cloud-Based Data Analytics Platform for Educational Systems

The organization faces increasing volumes of user interaction and transactional data generated from its educational platforms, which include web, tablet, and mobile applications. The current data infrastructure is insufficient for extracting comprehensive insights to inform marketing, sales, and product development strategies, leading to limited visibility into user engagement, subscription metrics, and application utilization for targeted enhancements.

A large educational institution or EdTech provider offering 3D visualization content and remote learning solutions to higher education and professional training markets.

• Implement a scalable, cloud-based data storage and processing solution to handle increasing data volumes.
• Develop an integrated BI dashboard system offering real-time and scheduled analytics on user activity, subscription trends, and engagement metrics.
• Improve data quality and processing efficiency to ensure accurate, actionable insights for marketing and product teams.
• Enable detailed filtering, comparison, and drill-down capabilities within dashboards to support strategic decision-making.
• Expand data collection to include additional applications and data sources to provide a holistic view of platform usage.
• Automate alerting and monitoring of data pipelines to maintain system reliability and timely insights.

• Automated data ingestion from operational databases, cloud storage, and third-party services using modern data streaming and migration tools.
• Data processing pipelines that anonymize PII and normalize data structures before storage.
• Centralized data modeling and warehousing in a cloud-based, scalable platform.
• Interactive dashboards enabling filtering, period comparison, and cross-sectional analysis.
• Automated monitoring and alerting mechanisms for data pipeline health and data quality anomalies.
• Support for both scheduled batch processing and real-time analytics.

• Operational databases for user activity and transactional data
• Third-party systems for customer and subscription information
• Email and messaging streaming platforms for engagement analytics
• Cloud storage solutions for raw data ingestion
• Monitoring and alerting systems for pipeline health

• System scalability to accommodate data growth, supporting increasing data volumes with minimal latency
• High availability and fault tolerance for continuous operations
• Security and data privacy, especially regarding PII, with compliance to relevant standards
• Automated monitoring, alerting, and logging for operations visibility
• Ease of maintenance and reusability of data pipeline components

The implementation of the cloud-based data analytics platform is expected to enhance decision-making capabilities by providing comprehensive, accurate, and timely insights into user engagement and platform performance. This will enable targeted marketing strategies, identify underutilized features for improvement, and support expansion efforts. Anticipated outcomes include improved data processing efficiency, expanded metric sets for analysis, and a scalable architecture suited for future growth.