Data discovery is a comprehensive process involving the collection, preparation, and analysis of data from diverse sources to extract actionable insights that can aid in decision-making across various business functions such as fraud detection and risk assessment. As the volume of data continues to grow, reaching an estimated 181 zettabytes by 2025, data discovery becomes crucial in navigating and harnessing this information effectively. The process is iterative and involves steps like defining objectives, collecting and preparing data, visualizing and analyzing it, and finally interpreting the results for actionable insights. Data discovery can be conducted manually, requiring a specific skill set, or automated using AI tools, each with its own pros and cons. Additionally, the article emphasizes the importance of data classification and security compliance in managing data, highlighting that data discovery aids in identifying potential security risks and compliance gaps. It also mentions tools such as Bright Data's web scraper API and datasets, which facilitate the data collection component of the discovery process.