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Difference Between Data Warehousing and Data Mining

Both of these are processes to manage and maintain data, but there is a significant difference between data warehousing and data mining. A data warehouse typically supports the functions of management. Data mining, on the other hand, helps in extracting various patterns and useful information from the available data. In simpler words, data warehousing refers to the process in which we compile the available information and data into a data warehouse.

What is Data Warehousing?

Data warehousing refers to a technology that helps in aggregating structured data from various sources. This way, one can easily compare this data and analyze it instead of transaction processing. The technology of data warehousing provides a user with a platform to clean, integrate, and consolidate data- thus supporting the decision-making process by the management. Various types of data are present in a warehouse that may be non-volatile, integrated, subject-oriented, and time-variant.

A data warehouse consolidates the available data from various sources while still ensuring the accuracy, quality, and consistency of the contained information. A warehouse improves the overall performance of a system. The available data flows into it from a variety of databases, and it works by organizing this data into schemas. This schema must describe the type and layout of the contained data. The query tools in a warehouse analyze the tables of data using the available schema.

What is Data Mining?

Data mining is a process that identifies the correlations and patterns among large sets of data for identifying the overall relationship between them. Various tools are available for data mining. These tools allow various business organizations to understand, analyze, and predict the behavior of their customers. The data mining tools are pretty helpful in building the risk models in any organization for detecting fraud and preventing it from its core. Data mining also comes in handy during management and analysis of the market and corporate analysis- along with risk management and detection of an impending fraud.

Difference Between Data Warehousing and Data Mining

Here is a list of the differences between data warehousing and data mining.

Parameters Data Warehousing Data Mining
Meaning and Definition Data warehousing is a database system technology designed for data analysis. Data mining is a process that determines the patterns of available data.
Dealing with Data This process helps in combining all the relevant forms of data and information from the available ones. This process extracts only the useful set of data and information from large chunks of available data.
Controlling Authorities The engineers carry out this technology entirely. Business owners and entrepreneurs can learn to carry out this process, but they do need help from various engineers.
Rate of Dealing with Data It stores the available data periodically. It analyzes and examines the available data repeatedly every once in a while.
Uses It extracts data and stores them in an organized manner- thus allowing easier and faster reporting. This process uses techniques for pattern recognition that helps them in identifying the available patterns.
  • The primary advantage of this technology is its ability to update continually and frequently. As a result, data warehouses become ideal for businesses and entrepreneurs who want to stay updated about the recent trends among their target audience and customers.
  • It simplifies the available data for businesses and organizations.
  • Data mining helps in various processes of data analysis and sorting. One of the best implementations here is the identification and detection of any unwanted error in a system. This process allows for the earlier elimination of any risks.
  • They are more cost-efficient as compared to other data applications (statistical).
Disadvantages In this technology, there is a high chance that the available data may not get integrated into a warehouse. Thus, it may lead to data loss. This technique might not be cent percent accurate. As a result, it may lead to some very serious consequences under some specific conditions.
Practical Applications It stores huge chunks of data- thus helping various users analyze the available trends and periods for making any future predictions. Many companies get to benefit from the data mining analytic tools because they let users access all the knowledge-based data that might be suitable for them.

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