Difference Between Data Warehousing and Data Mining
By BYJU'S Exam Prep
Updated on: September 25th, 2023
Difference Between Data Warehousing and Data Mining: Data warehousing and data mining are types of data analyzing and storing techniques. The data mining process relies on the data warehousing process. The major difference between data warehousing and data mining is that data warehousing is a technique to compile information inside the data warehouse and data mining is used for the extraction of the data.
Here, we will first discuss the difference between data warehousing and data mining based on various aspects thereafter we will discuss what is data warehousing and data mining in brief.
Table of content
Difference Between Data Warehousing and Data Mining
The main difference between data warehousing and data mining is that data mining is preferred by business analysts whereas data warehousing is a general data analysis as well. Let us now see the differences based on various aspects in the table provided below:
Key Differences Between Data Warehousing and Data Mining
| Difference Between Data Warehousing and Data Mining | |
| Data Warehousing | Data Mining |
| Data warehousing is using a database system for the analysis of data. | It is a process performed to predict or analyze unknown patterns. |
| It is used to centralize data. | Comparing large amounts of data. |
| Preferred always before data mining. | Preferred by business analysts. |
| Used to keep the system updated. | Used to detect errors. |
| It is used to improve the integrity of the business. | Keeps track of volatile factors such as buying habits of customers, sales records, etc. |
| Highly accurate. | Not always 100% correct. |
| Data is periodically updated. | Data is regularly updated. |
What is Data Warehousing?
Data warehousing is a process of comparing and analysing data from various sources into one database known as a data warehouse. It is designed to provide a platform for data integration, data cleaning, and data consolidation. It is used to maintain data consistency, data accuracy, and data quality.
The historical information is stored in the data warehouse and actions can be performed quickly. The process of a data warehouse can be of the following manner:
Source 🡪 Extract 🡪Transform 🡪 Load 🡪 Target.
The features of a data warehouse are:
- Time-Variant
- Nonvolatile
- Subject Oriented
- Integrated
What is Data Mining?
Data mining is the process of analysing the extracted data for information. Important and required information is fetched using data mining. Using this process, the future behaviour of the data is predicted. There are various important aspects that are used in data mining such as artificial intelligence, statistics, machine learning systems, etc.
The most important feature of data mining is that it is used for fraud detection and trend analysis. It is also used to build risk models as well. Let us now see the difference between data warehousing and data mining in the coming section.
