Higher latent heat of vaporization of ethanol deteriorates low ambient temperature starting of engines with ethanol blended fuels. In case of flex fuel vehicles, cold starting becomes very critical on account of higher ethanol content. This case study highlights how pivot table based analytics were effectively employed to enhance engine start strategy during the development of small commercial vehicle running on E20 and E85 fuel blends. The approach showcases how structured data interpretation can significantly support development work in Flex Fuel calibration. The analysis is focused on various critical engine start events such as first crank success, failure to start, battery voltage behavior, and post-start stability across a range of coolant temperatures, particularly below 20°C. Real world test data was categorized using data analysis based on parameters such as crank RPM, battery voltage during cranking, fuel, phase detection status, throttle input, and spark advance, and start success. Further analysis confirmed that start performance remarkably improves when crank RPM exceeds thresholds & battery voltage remains above threshold limit. This finding is consistent across temperature and fuel type variations. Additionally, updates in phase detection logic and flywheel learning significantly reduced first-crank failure rates and enhanced cold-start stability. This data driven approach helped calibration engineers to optimize ECU strategies by redefining cranking thresholds, optimizing battery management logic, and refining phase detection timing. These refinements translated into improved start robustness, minimized trial and error iterations, and reduced both development time and test facility usage. This case underlines the value of integrating data analytics into flex fuel development workflows enabling faster merging, less number of iterations, improved calibration accuracy and more reliable vehicle behavior under flex fuel operating conditions.