The article discusses the emerging field of Knowledge Discovery in Databases (KDD) and data mining, which aims to extract useful knowledge from large volumes of digital data. It highlights the challenges of data overload and the need for computational techniques to support the extraction of valuable insights. The article outlines the KDD process, which includes learning the application domain, data preparation, data mining, interpretation, and using the discovered knowledge. Data mining involves fitting models to data, with common model functions including classification, regression, clustering, and dependency modeling. The article also addresses the challenges of massive datasets, high dimensionality, and the need for efficient algorithms and prior knowledge incorporation. Despite its rapid growth, KDD faces significant research and practical challenges, and the field must balance the potential benefits with realistic expectations to avoid false promises.The article discusses the emerging field of Knowledge Discovery in Databases (KDD) and data mining, which aims to extract useful knowledge from large volumes of digital data. It highlights the challenges of data overload and the need for computational techniques to support the extraction of valuable insights. The article outlines the KDD process, which includes learning the application domain, data preparation, data mining, interpretation, and using the discovered knowledge. Data mining involves fitting models to data, with common model functions including classification, regression, clustering, and dependency modeling. The article also addresses the challenges of massive datasets, high dimensionality, and the need for efficient algorithms and prior knowledge incorporation. Despite its rapid growth, KDD faces significant research and practical challenges, and the field must balance the potential benefits with realistic expectations to avoid false promises.