In the pursuit of improving cold-start performance in alternate fuel vehicles, data analysis has become a cornerstone for driving informed calibration decisions. Cold start is very critical for flex fuel vehicles since it has higher latent heat of vaporization and lower vapor pressure compared to gasoline. Ethanol blend fuels deteriorate engine starting at low ambient temp. 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 concentrated 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 segmented using data analysis based on parameters such as crank RPM, battery voltage during cranking, fuel blend, phase detection status, throttle input, and spark advance, and start success. Further analysis confirmed that start performance notably improves when crank RPM exceeds thresholds and battery voltage remains above the 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 enabled calibration engineers to fine-tune 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. Ultimately, this case underscores the value of integrating data analytics into flex fuel development workflows enabling faster convergence, less number of iterations, improved calibration accuracy and more reliable vehicle behavior under flex fuel operating conditions.