Definition of Data Mining and Data Warehouse

Data mining as a process for obtaining useful information from large database warehouses. data mining can also be interpreted as extracting new information derived from large data chunks that aid in decision making. the term data mining is sometimes called knowledge discovery.

One technique created in data mining is how to trace existing data to build a model, then use that model to be able to recognize other data patterns that are not in the stored data base. the need for prediction can also utilize this technique. In data mining, data grouping can also be done. The goal is that we can know the universal pattern of existing data. The anomalies of traction data also need to be detected in order to find out the next follow-up that can be taken. All these aims to support the company's operational activities so that the company's ultimate goal is expected to be achieved.

Definition of Data Mining and Data Warehouse

Differences in Data Mining and Data Warehouse

What is the difference between data mining with data warehouse?

Data mining is a field that fully uses what is generated by data warehouses, along with fields that handle reporting and data management issues. Data warehouses are tasked with pulling data queries from raw databases to provide data that will be used by fields that handle management, reporting, and data mining. with this data mining, extracting new information can be done with stock of raw data provided by Data warehouse. The results provided by these three areas are useful for supporting smart business activities.

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