Difference between Data Warehousing & Data Mining

Before discussing difference between Data Warehousing and Data Mining, let’s understand the two terms first.

Data Warehousing

Data Warehousing refers to a collective place for holding or storing data which is gathered from a range of different sources to derive constructive and valuable data for business or other functions. It is a large storage space of data wherein huge amounts of data is stored by any organization for further analysis, reporting or processing of data and not mere transaction of it. Transaction processing means adding, removing or updating any data on a database. Data warehousing is not done for transactional purposes but for storing large quantities of related data for further processing or mining. It is basically making that data relatable and meaningful for analysis by users or experts.

Data warehouse possess a remarkably large storage capacity for data which is stored there from different databases of an enterprise which is there to access or update any information in it. Since data is gathered and aggregated from varied sources, it is accurate and of a consistent quality.

Data warehouses are optimized for carrying out analytics and not transactional functions which is why the analytics response time is also enhanced. The data in a warehouse is arranged in a manner in which derivation of a meaning or relation is easily possible. They are laid out into different patterns or formats so that a useful meaning can be derived of it. Automatic query tools can be used on the data in a warehouse.

The warehouse uses Online Analytical Processing (OLAP) for handling analytical queries. The warehouse data tells about a subject i.e. any customer or product, etc. Data of a specific time period is integrated from different sources and is non-changeable.

Data Mining

The stored data if arranged in a specific pattern can be able to derive useful insights and meanings from it for the purpose of devising business strategies. This is very much possible by data mining. The sets of data are subjected to analysis which can be further used for making any kinds of detection, discoveries or marketing strategies and more.

Data mining is a computer process that performs through statistics, artificial intelligence and other database technologies. In this process, the analysis is done by the computer itself using above mentioned techniques to draw out relevant insights and inputs from it. Predictive analysis can also be carried out with data mining in order to analyze what the future consequences of any current happening might be. Without data mining, it is not possible to even see or think of such analysis or relations between the different sets of data. Hence, data mining is majorly used for identifying and drawing out hidden relationships between the data.

Data mining is also referred to as Knowledge Discover in Database (KDD) where diverse data mining tools and machine learning systems are used for deriving something useful out of just a data. Data mining foretells the anticipated consequences and gives a clear idea of what kind of actions to take. It deals with large sets of data and huge databases. Trend analysis, market analysis, fraud detection, financial analysis, existing trends, all can be gathered from data mining. It helps businesses to drive to well informed decisions and strategies so that further losses or errors can be prevented. Data mining is an economical way of generating analysis or reviews rather than other statistical data application techniques.

Comparison of Data Warehousing and Data Mining

BASIS	DATA WAREHOUSING	DATA MINING
Definition	A huge database which is designed to carry out analytical processes and not transactional application	It is a process of determining hidden relationships an patterns between different sets of data
Meaning	It combines huge sets of related data	It derives useful meaning and insights from a large set of data
Application	Extremely large quantities of data of any organization can be easily stored	It is carried out for the purpose of identifying patterns, relationships and frauds in an organization
Implementation	Before data mining as the data is compiled and stored here in a common database	After warehousing in order to withdraw useful insights
Benefits	Timely data access, enhanced response time and provides consistent data for easy access	Helpful to predict trends, market analysis, financial analysis and recognizing fraudulent
Performed by	Can be performed by engineers	Performed by businessmen with the help of engineers

Download the difference table.

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