What is the Difference Between Data Mining and Data Warehousing? with Proper Definition and Brief Explanation

The main difference between data mining and data warehousing is that data mining is the process of identifying patterns from a large amount of data whereas data warehousing is the process of integrating data from multiple data sources. data in a central location.

Data mining is the process of discovering patterns in large data sets. It uses various techniques like classification, regression, etc. to make business decisions. On the other hand, data warehousing is the process of extracting, transforming, and loading data from multiple data sources to the data warehouse. Data mining techniques can be applied to a data warehouse to discover useful patterns.

Key Areas Covered

1. What is data mining?
     – Definition, Functionality
2. What is data storage?
     – Definition, Functionality
3. Difference Between Data Mining and Data Warehousing?
    – Key Differences Comparison

Key terms

Data Mining, Data Warehousing, Data

What is data mining?

Data mining is the process of discovering patterns in a large set of data. In other words, data mining extracts new patterns, relationships between data entities. The extracted data must be new, correct and must have a potential use.

The process of extracting useful information from data involves several steps. The first step is data selection. The data comes from multiple sources and has multiple formats. Therefore, all data is integrated and stored in a single location called a data warehouse. The second step is preprocessing. It involves summarizing, normalizing, and aggregating. These transformations help make the data suitable for data mining. The third step is data mining. It uses techniques or algorithms such as clustering, regression, classification to extract patterns from data. The fourth step is pattern evaluation. Check the accuracy of the output obtained. The final step is to represent the results using graphs.

                                                            Figure 1: Data Mining

The main techniques for performing data extraction are anomaly detection, association rule extraction, clustering, classification, and regression. First, anomaly detection helps identify unusual patterns to understand variation in data. Second, association rule mining helps to find interesting association patterns between variables. Third, clustering identifies classes in data that are similar to each other. Fourth, the classification identifies the classes to which an observation belongs. Finally, regressions help to find the relationship between the variables. These are the main techniques used in data mining.

What is data storage?

In a business organization, data is in multiple databases. First, data from multiple sources is extracted and transformed. They are then uploaded to a central location called a data store. Data warehousing is the process of loading data from various data sources into a data warehouse. Various strategies can then be applied to analyze the data to help end users make business decisions. Also, the data in the data store can be divided into data stores. These data stores hold data for a particular set of users. For example, the human resources department may use your data center. The sales department can use the sales market and so on.  

                                                           Figure 2: Data Warehouse

The data stores are subject-oriented, integrated, time-varying, and non-volatile. A data warehouse is subject-oriented. Gives knowledge on a subject that operations in progress. It is integrated because it consolidates data from multiple data sources. Warehouse data provides information regarding a specific time period. Therefore, it is the time variant. Finally, it provides non-volatility because after the data is loaded into the store, the data does not have to be deleted or updated. In short, data storage is beneficial in making decisions for the organization.

Difference Between Data Mining and Data Warehousing

Definition

Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data warehousing is the process of extracting, transforming, and loading data from multiple data sources to a central location called a data warehouse.  

Process

In data mining, data is analyzed regularly. The data is periodically stored in the data storage.

Data

Data mining analyzes a sample of data while data warehousing stores a large amount of data.

Use

Data mining uncovers patterns in data for better decision making. On the other hand, data warehousing provides a mechanism for an organization to store a large amount of data.

Conclusion

The difference between data mining and data warehousing is that data mining is the process of identifying patterns from a large amount of data, while data warehousing is the process of integrating data from multiple data sources. data in a central location. Usually, data warehousing is done by engineers and data mining is done by business users with the help of engineers.

Reference:

1. Data mining using R | Data Mining Tutorial for Beginners | R tutorial for beginners | Edureka, Edureka!, November 8, 2017, available here.
2. Data Storage Tutorial for Beginners | Data Storage Concepts | Data storage | Edureka, Edureka!, June 22, 2017, available here.

Courtesy image:

1. “Data Mining” By Arbeck – Own work (CC BY 3.0) via Commons Wikimedia
2. “Data Warehouse Overview” By Hhultgren – Own work (Public Domain) via Commons Wikimedia