Data mining is an interdisciplinary process focused on extracting valuable, previously unknown patterns and insights from massive datasets, leveraging methods from machine learning, statistics, and database systems. As the core analysis step in "knowledge discovery in databases" (KDD), it distinguishes itself from traditional data analysis by actively uncovering hidden patterns rather than merely testing existing hypotheses. While considered a "misnomer" for extracting knowledge instead of raw data, the term has an interesting etymology. It was initially used critically for "data dredging" by statisticians in the 1960s and by economist Michael Lovell in 1983. However, "data mining" gained positive connotations within the database community around 1990, after "database mining" was briefly trademarked. Ultimately, this powerful technique enables the semi-automatic identification of significant structures like clusters, anomalies, and dependencies, which are vital for further analysis and predictive modeling.