Simply put, data mining is the process that companies use to turn raw data into useful information. They utilize software to look for patterns in large batches of data so they can learn more about customers. It pulls out information from data sets and compares it to help the business make decisions. This eventually helps them to develop strategies, increase sales, market effectively, and more.
Data mining sometimes gets confused with machine learning and data analysis, but these terms are all very different and unique.
While both data mining and machine learning use patterns and analytics, data mining looks for patterns that already exist in data, while machine learning goes beyond to predict future outcomes based on the data. In data mining, the “rules” or patterns aren’t known from the start. In many cases of machine learning, the machine is given a rule or variable to understand the data. Additionally data mining relies on human intervention and decisions, but machine learning is meant to be started by a human and then learn on its own. There is quite a bit of overlap between data mining and machine learning, machine learning processes are often utilized in data mining in order to automate those processes.
Similarly data analysis and data mining aren’t interchangeable terms. Data mining is used in data analytics, but they aren’t the same. Data mining is the process of getting the information from large data sets, and data analytics is when companies take this information and dive into it to learn more. Data analysis involves inspecting, cleaning, transforming, and modeling data. The ultimate goal of analysis is discovering useful information, informing conclusions, and making decisions.
Data mining, data analysis, artificial intelligence, machine learning, and many other terms are all combined in business intelligence processes that help a company or organization make decisions and learn more about their customers and potential outcomes.