Despite the rapid advancement of AI in various fields, there remains a gap in research concerning its suitability and evaluation for general data analysis across diverse domains. The application of AI tools to process, clean, and improve complex, incomplete, or noisy datasets has not been thoroughly explored. Key challenges include determining how effectively these tools can detect and correct errors, augment data, and ensure improvements in data quality. This research not only evaluates the performance of AI tools in these tasks but also develops a structured framework for testing their capabilities in error detection, correction, and supplementation. Using the open-source Canadian Wind Turbine Database as a case study, this study introduces intentional errors to create controlled scenarios for evaluation. The framework offers insights into the effectiveness, limitations, and best practices for applying AI tools like ChatGPT to real-world data analysis challenges.