Typically, when someone talks about “mining,” it involves people wearing helmets with lamps attached to them, digging underground for natural resources. And while it could be funny picturing guys in tunnels mining for batches of zeroes and ones, that doesn't exactly answer “what is data mining.”
Data mining is the process of analyzing enormous amounts of information and datasets, extracting (or “mining”) useful intelligence to help organizations solve problems, predict trends, mitigate risks, and find new opportunities. Data mining is like actual mining because, in both cases, the miners are sifting through mountains of material to find valuable resources and elements.
Data mining also includes establishing relationships and finding patterns, anomalies, and correlations to tackle issues, creating actionable information in the process. Data mining is a wide-ranging and varied process that includes many different components, some of which are even confused for data mining itself. For instance, statistics is a portion of the overall data mining process, as explained in this article.
Additionally, both data mining and machine learning fall under the general heading of data science, and though they have some similarities, each process works with data in a different way. If you want to know more about their relationship, read up on