Difference Between Data Mining and Data Warehousing
Data Mining vs Data Warehousing
The terms “data mining” and “data warehousing” are related to the field of data management . These are data collection programs which are mainly used to study and analyze the statistics, patterns, and dimensions in a huge amount of data.
The term “data mining” is used for a process which involves the analysis of data in terms of a variety of perspectives and summing up that data into useful information. The data mining software processes the information so as to regulate the data in either cost cutting or for an increase in revenue or both.
Data mining procedures follow an in-depth study and collection of information by the identification of particular trends based on the data and queries which are generated by the user. The prime objective of data mining software is to identify unusual patterns, spot frauds related to finances in particular, and generate steered programs to enhance marketing.
The data mining software are mainly used due to the vast amount of data collected. The data pour in through scanners, direct mail response, ATM machines, Web server logs, demographic data, closed-circuit cameras, credit card transactions, and many additional sources. All this information must be validated and summarized before any analysis has to be done. This process is categorized as data warehousing. The next step is to sort this information out through various procedures integrated under data mining.
The data mining software makes use of various steps. The first step is the pre-processing of the data which involves: selection of data, cleaning of data, removal of noise, and transformation of data. After these common units of information are created, new fields are generated. The next step is the construction of a data mining model. Here a prospective model is generated to summarize useful information. The last step is the evaluation of the data mining model.
Data mining is necessary presently mainly due to the growing competition in business. The companies are competing in terms of services, personalization, security, and real-time enterprise.
Data warehousing is the process of collecting and storing data which can later be analyzed for data mining. A data warehouse is an elaborate computer system with a large storage capacity. Data from all the sources are directed to this source where the data is cleaned to remove conflicting and redundant information. The process of data warehousing enables centralized data access.
The elaborate and intricate data capturing and processing techniques are the main sources for organizations to establish an effective and efficient data warehousing facility. These are an essential asset for the companies to maintain their profitability, efficiency, and competitive advantages. The data collected is passed through a process called Data Life Cycle Management.
The data warehousing makes use of techniques for relative data base management systems as extraction, loading, transformation, and relational online application processing. There are four characteristics of data warehousing techniques. They are: subject-based design, integration with data, non-volatile image of states, data and time variant views of data.
- The data mining and data warehousing techniques are parts of a data management system.
- Data warehousing is mainly concerned with the collection of data while data mining is concerned with analyzing and summarizing the important information for the organization.
- The techniques of data mining and data warehousing processes are different.
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