We're all familiar with the phrase "sifting through the data." what exactly does that mean? Like social media posts, emails and other web content. It's not that we don't know how to use the data; we want to know what it means, like social media posts, emails and other web content. Unstructured data is typically harder to work with because it does not have a set structure, so it only sometimes conforms to structured data rules. In data mining, unstructured data is usually problematic because it does not conform to a specific format, making it easier for computers to read and process. With unstructured data, you must rely on algorithms to make sense of it rather than structured data, making it easier for computers to read. Data mining is a process that picks up patterns in unstructured data and turns it into something organized and valuable. Data mining is the only way to get helpful information if a data set is large and unstructured. Data mining typically works best with large, data-driven businesses like social media platforms or ecommerce sites. In the most basic sense, data mining is sifting through data to discover patterns, correlations, and trends that Can be used to improve business processes. Data mining is a vital part of business intelligence and business analytics, as it is discovering patterns in large amounts of data. Let's say you want to mine your unstructured data. You could do that by looking at letters between two or more people. Suppose you're using unstructured data mining. In that case, you can break down those letters and find specific identifiers like names, dates, businesses involved, amounts of currency and other quantifying bits of information. You can then put these in a format that companies or other parties can use for quick reference or developed business intelligence applications.