Mining in general consists of going deep into the earth to search, among regular stones, for the special minerals that occur in it. In other words, it consists of searching for treasures scattered in the earth, trying to separate what is valuable from what is not so valuable. With that, an everyday environment is used to turn it into something that gives value to the person. Data mining works in exactly the same way: in the everyday medium of information, treasures are searched for, which, in the case of business, are the relevant data that can be transformed into knowledge. This is the use of data mining in business.

Just as in the physical mine, in data mining we have a large amount of information and, dispersed in that data, other important information that can be used for decision making or to better understand the behavior of the business. But, to access that information, you need the right tools, just as miners have their picks and flashlights, and all the right technology.

These specific technologies for data mining are another of the many information technologies that have followed a rapid evolutionary development and improvement in recent decades, perhaps because of the increase in the number of data that are handled today in businesses and institutions of all kinds:

Data mining, also known as knowledge discovery in data (KDD), is the process of uncovering patterns and other valuable information from large data sets. Given the evolution of data warehousing technology and the growth of big data, adoption of data mining techniques has rapidly accelerated over the last couple of decades, assisting companies by transforming their raw data into useful knowledge. However, despite the fact that that technology continuously evolves to handle data at a large-scale, leaders still face challenges with scalability and automation. (ibm.com)

As with artificial intelligence, robotics or predictive data analytics, it is necessary to understand how businesses can benefit from data mining: “Data mining has improved organizational decision-making through insightful data analyses. The data mining techniques that underpin these analyses can be divided into two main purposes; they can either describe the target dataset or they can predict outcomes through the use of machine learning algorithms” (ibm.com).