Difference Between Data Warehousing and Data Mining

By Mohit Uniyal|Updated : July 11th, 2022

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 WarehousingData 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.

☛ Related Questions:

Difference Between OLTP and OLAPDifference between Java and Core Java
 Difference between CD and DVD
Difference between 3G and 4GDifference Between Linear and Non-Linear Data Structures
Difference Between List and Tuple in PythonDifference Between MAC Address and IP Address

Comments

write a comment

FAQs on Data Warehousing vs Data Mining

  • The major difference between data warehousing and data mining is that the data warehouse is used for collecting and comparing the data whereas data mining is a process used to predict the nature of the data.

  • Data Warehousing is a process of creating data warehouse for the comparison and analysis of the data. It is used to maintain the consistency of the data. The features of a data warehouse are Time-Variant, Subject Oriented, Integrated, and Non-volatile.

  • Data mining process is used to analyse and extract data for information. It is used for fraud detection, future behaviour of data, growth prediction and lot more. It is used in artificial intelligence as well.

  • As per the accuracy level, the difference between data warehousing and data mining is that data warehousing is highly accurate whereas data mining is not always correct. There can be seen a few incorrect prediction on the data mining predictions.

  • The relationship between data mining and data housing is that data warehousing is required for data mining. Data mining can not be done if data warehousing is not done.

Follow us for latest updates