Data mapping, a crucial process in modern data projects, involves aligning fields and records across different systems to preserve the meaning of information as it moves between databases and applications. Traditionally a slow and error-prone task, data mapping has been significantly enhanced by AI technologies, such as machine learning and natural language processing, which automate and streamline the process, reducing manual effort and improving accuracy. AI-powered mapping tools can efficiently handle large, complex datasets by analyzing schemas, recognizing field names, and proposing accurate matches, thereby reducing the time required from hours to minutes. These tools also adapt to changes in data formats and improve over time by learning from user feedback. The success of AI data mapping is heavily reliant on the quality of the source data, prompting the use of platforms like Bright Data to extract and prepare clean, structured web data before mapping. This ensures that mapping projects are more efficient and accurate, allowing organizations to focus on integrating web data into their analytics and AI workflows.