Development of a Cloud-Native and Offline-First Data Reporting and Analysis System

The client currently relies on manually maintained workflows involving complex, lengthy spreadsheets with macros written entirely in Japanese, which are undocumented and only operable on-premises. These workflows result in unstructured data pipelines, difficulty in downstream analysis, and limited ability to adapt or scale the reporting processes, especially as data sources evolve or expand.

A mid to large-sized medical diagnostics organization that handles lab data collection, reporting, and internal analysis workflows, seeking to modernize legacy data processing systems.

• Implement a cloud-native platform to replace legacy, unmaintainable workflows, enabling scalable and automated data processing.
• Develop an offline-first application that synchronizes with the cloud platform, supporting field personnel or locations with limited internet connectivity.
• Reduce manual intervention and streamline report generation for internal stakeholders.
• Enhance data transparency, analysis capability, and downstream compatibility with modern analytical tools.
• Achieve a system that is robust, maintainable, and adaptable for future data integration needs.

• A web-based frontend built with a modern JavaScript framework for intuitive user interaction and report visualization.
• A backend system capable of processing CSV lab data inputs, transforming them into standardized report formats.
• Offline-first functionality through a Windows Installer app that stores data locally and synchronizes with the cloud when connectivity is available.
• A synchronization module to seamlessly sync data and reports bidirectionally between local devices and the cloud platform.
• A database system in the cloud (e.g., NoSQL like DynamoDB) for scalable storage of processed data and reports.
• Automation of report generation workflows and system alerts for status updates or errors.

• Lab reporting tools to ingest CSV data files
• External authentication systems for user management
• Reporting and analytical tools for downstream data analysis

• System should support scalable data processing for large CSV inputs
• Offline functionality must enable data access and report generation without internet for at least 24 hours
• Synchronization latency should be less than 5 minutes during data sync events
• System must adhere to security best practices to secure sensitive lab data
• Applications should be performant, with average load times under 2 seconds

The new platform is expected to replace unmaintained, error-prone workflows, resulting in improved data processing efficiency and report accuracy. It aims to reduce manual effort, decrease report generation time, and improve data analysis capabilities, enabling better decision-making across the organization. The offline-first design ensures operational continuity in remote locations, and the scalable, maintainable architecture prepares the organization for future data expansion and integration needs.