During my CPT experience at an automotive software company, I have primarily worked on ECU testing, including power systems, CAN communication, and Ethernet networking, where data is essential for diagnosing system performance and reliability. For example, when issues such as unstable communication or unexpected system behavior occur, engineers rely on log data, signal traces, and performance metrics to identify root causes. In some cases, initial assumptions may focus on hardware faults, but deeper data analysis can reveal issues related to configuration, timing, or system load.
This experience has shown me that while data provides valuable evidence, effective decision-making still depends on human judgment. Engineers must interpret results within the broader system context and remain aware of biases, such as favoring familiar explanations or overlooking less obvious factors.
Looking forward, as I aim to work in robot fleet management, data-informed decision-making becomes even more critical. Managing multiple robots requires analyzing large-scale operational data, including system health, network performance, and task efficiency. To make effective decisions, it is important to combine accurate data collection with critical thinking, cross-functional collaboration, and continuous validation. This integration of data and human judgment supports more reliable and scalable robotic systems.
