Data Mining vs Data Warehousing

How are data mining and data warehousing different from each other? Or how they are related? In today’s blog post on “Data Mining vs Data Warehousing, we will find out what exactly do these two terms mean and what differentiates them.

What is Data Warehousing?

Data warehousing is a technology or process of compiling data from multiple sources (operational as well as external databases) into a common place. Though the concept is called data warehousing, the place where the data is compiled is called the data warehouse. It organizes the data into a schema that represents the data type and layout.

A data warehouse helps enterprises analyze and derive significant insights from the available datasets. The data can be compared and used to improve existing business operations, marketing strategies, and the business’ bottom line. Data in a data warehouse is subject-oriented, time-variant, integrated, and non-volatile.

What is Data Mining?

The process of extracting data from large datasets and discovering patterns and correlations within them is called data mining. It helps analyze large sets of data and paves the way for improved business intelligence by mitigating risks and solving problems for companies. The data mining process uses certain tools and techniques to discover useful patterns.

The system, while analyzing the data, looks for the hidden patterns within the datasets and tries to predict future behavior or you can say data mining allows businesses to make data-driven decisions for the future.

Let’s now understand the difference between data mining and data warehousing.

Difference Between Data Mining and Data Warehousing

Data mining and data warehousing complement each other. Data mining cannot be performed without a data warehouse in place. Once the latter is set up, the former is used to recognize meaningful patterns in the data.

Businesses adopt data warehousing to get seamless and quick access to the required data efficiently. It is designed to allow them to take better business-related decisions based on the collected data insights. Additionally, a data warehouse not only encompasses data integration or data consolidation but also data deletion. It ensures quality, consistency, and accuracy in the data.

Data mining, on the other hand, is used to extract valuable information and patterns from the data available in the data warehouse or the databases.

Both a data warehouse and a database are relative data systems but serve distinct purposes. While the former is used to aggregate data from varied sources and uses Online Analytical Processing (OLAP) for faster processing of data requests, the latter stores current transactions and uses Online Transaction Processing (OLTP) to enable quick transaction requests. Put simply, the data warehousing concept revolves around query and analysis rather than transaction processing. Data is transformed into information and made available for analysis.

A data warehouse can also be defined as a combination of components and technologies that allow data to be used strategically whereas data mining is about recognizing meaningful patterns and finding the relationship amongst the data. It leverages machine learning, artificial intelligence, statistics, and database technology to carry out the needed tasks which can be used for fraud detection, marketing, and more.

For your better understanding, we have showcased the difference between data mining and data warehousing in tabular form below.

Key Differences: Data Mining vs Data Warehousing

Conclusion

Data warehousing and data mining are closely related to each other. The latter can’t be operated without the former. Similarly, the information stored in the former comes to life because of the latter.

We hope this blog post helped you understand the difference between the data warehouse and data mining concepts. If you want to gain more insights into these two terms, you can take a look at each individually and learn about their features, benefits, and how do businesses make use of the two.