Table of Contents:-
- Difference between Data Mining and Data Warehousing
- Major Difference between Data Warehousing and Data Mining
- Evolution of Data Warehousing
Difference between Data Mining and Data Warehousing
Data mining and data warehousing are two different elements of data management; each component has its own unique objectives and functions. The main difference between data mining and data warehousing is that data warehousing compiles and organizes data in a shared database, while data mining extracts essential data from databases. The table below outlines the differences between data mining and data warehousing.
| Basis of Comparision | Data Mining | Data Warehousing |
| Definition | Data mining is extracting relevant data from a compiled set of stored data. | Data warehousing compiles, organizes, and stores data groups in a commonly accessible database. |
| Use | It is used for analyzing and improving strategies chosen by the organization. | It is used to help management in making and implementing decisions. |
| Tasks | It involves the use of pattern recognition logic to identify patterns. | It includes storage and data extraction for easier reporting. |
| Functionality | Tools used for data mining include statistics, databases, artificial intelligence, and machine learning systems. | Data warehouses are integrated, topic-oriented, non-volatile and time-varying. |
| Objective | It is the process of determining data patterns. | It is a database system designed for analytics. |
| Process | Analysis of data is done regularly. | Storage of data is done periodically. |
Major Difference between Data Warehousing and Data Mining
1. Data warehousing extracts and stores data that makes reporting easier, whereas data mining uses pattern recognition techniques to identify patterns.
2. When connected with operational business systems like CRM, data warehousing deliberately adds value to them, whereas data mining helps in creating suggestive patterns of key parameters.
| Basis of Comparision | Data Mining | Data Warehousing |
| Advantages | It enables the analysis of information and data. | It helps to sort and upload essential data into databases. |
| Application | Entrepreneurs and business owners can conduct data mining with the help of data technicians. | The organizational data scientists and technical data collection teams perform this process. |
| Disadvantages | Data mining is only sometimes 100% accurate and can lead to data breaches and hacking if performed incorrectly. | There is a high possibility of accumulating irrelevant and useless data. Data loss and erasure can also pose problems. |
| Update Frequency | Data is regularly analyzed in small phases, although this may vary during crisis communication. | Data is loaded periodically, and stacking is standard for easy access during extraction. |
Evolution of Data Warehousing
Data Warehousing (DW) became very famous during the late 80’s when companies started building decision support systems primarily to support reporting. With rapid advancements in relational database performance during the late 1990s and early 2000s, Data Warehousing became a core part of the Information Technology group across large enterprises. Vendors like Teradata and Netezza began offering customized hardware to manage data warehouse architectures within state-of-the-art machines. Data warehousing has been at the top of the list of priorities since the mid-2000s. The data supply chain ecosystem has grown exponentially in the current world, and so has how enterprises architect their data warehouses.
A well-architected data warehouse serves as an extended vision for the enterprise, where multiple departments can gain actionable insights to manage critical business decisions that could drive operational excellence or revenue-generating opportunities for the enterprise.
What is Data Warehouse?
Bill Inmon states, “A warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management’s decision making process”.
According to Ralph Kimball, “Data Warehouse (DW) is the conglomerate of all data marts within the enterprise. Information is always stored in the dimensional model”.
Role of Data Warehousing
The Data Warehouse is utilized to collect and manage data from various sources, aiming to provide meaningful business insights. Typically, a data warehouse is employed for linking and analyzing heterogeneous sources of business data. It serves as the central hub of the data collection and reporting framework developed for the BI system. Data warehouse systems serve as real-time repositories of information, often tied to specific applications. They gather data from multiple sources, including databases, with a focus on storing, filtering, retrieving, and particularly analyzing vast quantities of organized data.
Operating within an information-rich environment, the data warehouse provides an overview of the company, making current and historical data available for decisions. It enables decision-support transactions without obstructing the operating system, maintains information consistency for the organization, and presents a flexible and interactive information source.
Need for Data Warehouse
Data warehouses are extensively used in the largest and most complex businesses worldwide. In demanding situations, good decision-making becomes critical. Significant and relevant data are required to make decisions, a feat made possible only with the help of a well-designed data warehouse. The following are some of the reasons for the need for Data Warehouses:
1. Enhancing the turnaround time for analysis and reporting: A data warehouse allows business users to access critical data from a single source, making quick decisions without wasting time retrieving data from multiple sources. Business executives can query the data themselves with minimal or no support from IT, saving money and time.
2. Improved Business Intelligence: A data warehouse helps managers and business executives achieve their vision achieve vision. Outcomes that affect the strategy and procedures of an organization will be based on reliable facts and supported by evidence and organizational data.
3. Benefit of historical data: While transactional data stores data on a day-to-day basis or for a very short period, a data warehouse stores large amounts of historical data, enabling the business to include trend analysis, time-period analysis, and trend forecasts.
4. Standardization of data: Data from heterogeneous sources are available in a single format in a data warehouse, simplifying the readability and accessibility of data. For example, gender may be denoted as Male/Female in Source 1 and M/F in Source 2, but in a data warehouse, gender is stored in a format common across all businesses (i.e., M/F).
5. Immense ROI (Return On Investment): Return On Investment refers to the additional revenues or reduced expenses a business will realize from any project.
Reference:-
- https://www.shiksha.com/online-courses/articles/difference-between-data-mining-and-data-warehousing/#:~:text=The%20main%20difference%20between%20data,extracting%20essential%20data%20from%20databases.
- https://www.javatpoint.com/data-mining-cluster-vs-data-warehousing
- https://www.egyankosh.ac.in/bitstream/123456789/89128/3/Block-1.pdf
- https://herovired.com/learning-hub/blogs/data-warehousing-and-data-mining/