Data mining and Big Data are considered to be two different things but both are crucially important to understand in the realm of data analytics. Although both of these terms relate to the handling of large magnitudes of data for different recipients, but they are actually used in different context and for two different elements for this type of operations.
The term “Big Data” refers to the large sets of data that outgrow the databases which are simple in nature, and which were used in the times when technological advancement was a thing of the future and people used a less feasible and more expensive methodology or data handling architecture. For instance, the term “Big Data” can be used to address the large magnitude of data which is not easily handled in Microsoft Excel spreadsheet. Hence, it will be referred to as Big Data.
On the other hand, Data Mining refers to the process of analyzing and thoroughly looking through sets of “Big Data” in order to search for pertinent or important information. Putting the entire operation in simple words, we can say that the operation is similar to the phrase of “looking for a needle in the haystack”. The notion behind this is that the decision-makers in large corporations require access to more specific and smaller sets of data which have to extracted from the homogeneous large sets of “Big Data”. Therefore, “Data Mining” is used as a technique to elucidate the information which can assist businesses in chartering direction for their business.
Furthermore, different software packages including analytic tools can also be used in Data Mining, but generally the process of Data Mining include operations with intricate search operations which return results which are specific in nature. For instance, a tool used for Data Mining would look through years of accounting data in order to locate and provide a particular column of accounts needed by the user. Thus, we can simply say that Big Data is the primary asset, while Data Mining can be considered as the handler of this asset.