Building Test Analytics Navigator Solution to Improve the Testing Team Productivity and Transform the Organization into Data-driven

Abstract

This thesis introduces the Test Analytics Navigator (TAN), an automated analytics solution that improves the defect management process of an elevator manufacturing company’s R&D division. TAN was designed to address the challenges of extracting defect data scattered into different tools and the absence of a unified view of defect metrics and transforming the organization into a data-driven one. The objective was to build an automated and centralized test analytics solution to facilitate the data integration, transformation, and visualization of defect data and, thereby, improve the productivity of the R&D organization. AWS cloud services, data engineering techniques like ETL, and Power BI data visualization tools were used to implement the solution. The TAN uses custom Python libraries and scripts executed in serverless AWS Lambda service to automate data extraction and transformation. Extensive insights into defect trends were provided using the Power BI data visualization tool. The design principles like low maintenance, cost-efficiency, high security, and in-house competency were considered while building the solution. A unified dashboard view of defect trends and metrics extracted from multiple data sources, Power BI report automation, reduced manual workload, and enhanced team productivity were some benefits the stakeholders witnessed. Clear improvements in data consolidation, accuracy, and the ability to support real-time, data-driven decision-making resulted from incorporating TAN in the defect management process. Future research can be extended by incorporating predictive analytics capabilities to improve the efficiency of defect management further.