Difference between Data Warehousing and Data Mining

A data warehouse is built to support management functions whereas data mining is used to extract useful information and patterns from data. Data warehousing is the process of compiling information into a data warehouse.

Data Warehousing:

It is a technology that aggregates structured data from one or more sources so that it can be compared and analyzed rather than transaction processing. A data warehouse is designed to support the management decision-making process by providing a platform for data cleaning, data integration, and data consolidation. A data warehouse contains subject-oriented, integrated, time-variant, and non-volatile data. The Data warehouse consolidates data from many sources while ensuring data quality, consistency, and accuracy. Data warehouse improves system performance by separating analytics processing from transactional databases. Data flows into a data warehouse from the various databases. A data warehouse works by organizing data into a schema that describes the layout and type of data. Query tools analyze the data tables using schema.

Figure: Data Warehousing process

Advantages of Data Warehousing:

The data warehouse’s job is to make any form of corporate data easier to understand. The majority of the user’s job will consist of inputting raw data.
The capacity to update continuously and frequently is the key benefit of this technology. As a result, data warehouses are perfect for organizations and entrepreneurs who want to stay current with their target audience and customers.
It makes data more accessible to businesses and organizations.
A data warehouse holds a large volume of historical data that users can use to evaluate different periods and trends in order to create predictions for the future.

Disadvantages of Data Warehousing:

There is a great risk of accumulating irrelevant and useless data. Data loss and erasure are other potential issues.
Data is gathered from various sources in a data warehouse. Cleansing and transformation of the data are required. This could be a difficult task.

Data Mining:

It is the process of finding patterns and correlations within large data sets to identify relationships between data. Data mining tools allow a business organization to predict customer behavior. Data mining tools are used to build risk models and detect fraud. Data mining is used in market analysis and management, fraud detection, corporate analysis, and risk management.

Figure: Data Mining process

Advantages of Data Mining:

Data mining aids in a variety of data analysis and sorting procedures. The identification and detection of any undesired fault in a system is one of the best implementations here. This method permits any dangers to be eliminated sooner.
In comparison to other statistical data applications, data mining methods are both cost-effective and efficient.
Companies can take advantage of this analytical tool by providing appropriate and easily accessible knowledge-based data.
The detection and identification of undesirable faults that occur in the system are one of the most astonishing data mining techniques.

Disadvantages of Data Mining:

Data mining isn’t always 100 percent accurate, and if done incorrectly, it can lead to data breaches.
Organizations must devote a significant amount of resources to training and implementation. Furthermore, the algorithms used in the creation of data mining tools cause them to work in different ways.

Comparison between Data Mining and Data Warehousing:

S. No.	Basis of Comparison	Data Warehousing	Data Mining
1.	Definition	A data warehouse is a database system that is designed for analytical analysis instead of transactional work.	Data mining is the process of analyzing data patterns.
2.	Process	Data is stored periodically.	Data is analyzed regularly.
3.	Purpose	Data warehousing is the process of extracting and storing data to allow easier reporting.	Data mining is the use of pattern recognition logic to identify patterns.
4.	Managing Authorities	Data warehousing is solely carried out by engineers.	Data mining is carried out by business users with the help of engineers.
5.	Data Handling	Data warehousing is the process of pooling all relevant data together.	Data mining is considered as a process of extracting data from large data sets.
6.	Functionality	Subject-oriented, integrated, time-varying and non-volatile constitute data warehouses.	AI, statistics, databases, and machine learning systems are all used in data mining technologies.
7.	Task	Data warehousing is the process of extracting and storing data in order to make reporting more efficient.	Pattern recognition logic is used in data mining to find patterns.
8.	Uses	It extracts data and stores it in an orderly format, making reporting easier and faster.	This procedure employs pattern recognition tools to aid in the identification of access patterns.
9.	Examples	When a data warehouse is connected with operational business systems like CRM (Customer Relationship Management) systems, it adds value.	Data mining aids in the creation of suggestive patterns of key parameters. Customer purchasing behavior, items, and sales are examples. As a result, businesses will be able to make the required adjustments to their operations and production.