Development of an Enterprise Quality Assurance and Embedded Analytics Platform

The client faces significant challenges due to an outdated software quality strategy, resulting in inefficient manual and automated QA processes, prolonged release cycles (up to three months), lack of comprehensive project health metrics, and suboptimal automation ROI. Additionally, they need a modern embedded analytics platform capable of integrating multiple data sources and delivering customizable, high-performance dashboards for data analysis and visualization.

A large-scale enterprise specializing in financial reporting, enterprise software, and analytics solutions within the financial sector.

• Increase test coverage to a minimum of 90% to improve software quality.
• Implement a comprehensive test management tool to enable metrics collection and traceability between test cases and features.
• Reduce total regression testing time from three months to approximately three weeks to accelerate release cycles.
• Enhance automation ROI by a factor of five through optimized automation frameworks and strategies.
• Develop and integrate a modern embedded analytics platform with advanced BI features, supporting data source integration and customizable dashboards.
• Ensure seamless migration to contemporary architecture for improved performance, scalability, and maintainability.

• Test coverage maximization to 90% via integrated automation frameworks.
• Test management module with metrics collection and traceability linking test cases to features.
• Automation framework supporting Selenium, Java, Maven, Selenide, TestNG, and Allure Reports.
• Migration to a modern architecture with scalable microservices and RESTful APIs.
• Embedded analytics components with interactive dashboards, BI features, and customizable visualizations.
• Public API for external application integration, report scheduling, and data visualization.
• Support for high-volume analysis and reporting with multi-source data integration.
• Binning, data staging, and database-agnostic access and expressions for flexible data processing.

• Snowflake
• Redshift
• BigQuery
• Salesforce
• Oracle
• MySQL
• SQL Server
• Various data sources via JDBC

• Achieve test automation coverage of at least 90%.
• Reduce regression testing time from 3 months to 3 weeks.
• Ensure high system availability and scalability for multiple enterprise users.
• Implement secure data handling and API access compliant with industry standards.
• Ensure smooth migration with minimal downtime and clear traceability.

The implementation of the comprehensive QA automation and embedded analytics platform is projected to significantly enhance software quality, reduce release cycle durations by up to 75%, improve automation ROI by over 500%, enable deeper data insights through customizable dashboards, and support faster, data-driven decision making for enterprise stakeholders.