Data Warehousing and Data Mining goes hand in hand An Overview of Data Warehousing

 In Data Warehousing

Data Warehousing and Data Mining goes hand in hand

An Overview of Data Warehousing and Data Warehouse

A Data Warehouse possesses consolidated historical data, which helps the organization analyze its business activities and predict the business’s future growth. A data warehouse also helps executives organize, understand, and use their data to make strategic decisions.

Data warehouse systems can help different applications to integrate. It provides consolidated historical data in the multidimensional view. Thus it helps to provide Online Analytical Processing (OLAP) tools. These tools help us in the interactive and effective analysis of data. This analysis then helps in data mining.

Data mining functions such as association, classification, prediction, etc., can be integrated with OLAP operations to enhance knowledge at multiple levels of abstraction. Because of this reason, the data warehouse has now become an important platform for online analytical processing of data.

Data warehousing is the process of creating and using a data warehouse. We can create a data warehouse by integrating data from multiple heterogeneous sources. This compiled data supports analytical reporting, structured and ad hoc queries, and decision making. The complete process of data warehousing involves data cleaning, data integration, and data consolidations.

Example: Facebook

The best example of a data warehouse with whom everyone can relate easily is Facebook. Facebook collects all of your data and then stores that data into one central repository. They want to make sure that you see the most relevant ads.

There are many reasons behind that. But the ultimate goal is to increase the reach of the people to a particular page. They have an objective to extract the most meaningful data and patterns from the aggregated data.

Types of Data Warehousing

  • Information Processing − A data warehouse allows the process of the data stored in it. It can process the data using querying, basic statistical analysis, reporting using crosstabs, tables, charts, or graphs.
  • Analytical Processing − A data warehouse supports the analytical processing of the information stored in it. The data can be analyzed using basic OLAP operations, including slice-and-dice, drill down, drill up, and pivoting.
  • Data Mining − Data mining supports knowledge discovery by finding hidden patterns and associations, constructing analytical models, performing classification and prediction. These mining results can be presented using visualization tools.

Features of a Data Warehouse

  • Subject Oriented − A data warehouse is subject-oriented. It provides information around specific subjects such as product, customers, sales, etc., rather than ongoing operations. It focuses on modelling and analysis of data for decision making.
  • Integrated − A data warehouse integrates data from heterogeneous sources such as relational databases, etc. The effective analysis of data becomes easier because of this integration property.
  • Time-Variant − The data gathered in a data warehouse is identified with a definite period. With this time-variant data can provide information from the historical point of view, which we can use later for predictive analysis.
  • Non-Volatile − Non-volatile means there is no fear of losing the previous data when new data is added to the data warehouse. There is a separate data warehouse and a separate operational database; this secures the data warehouse from minor changes in the operational database.

Applications of Data Warehouses

  • Financial services: These are the economic services provided by the finance industry. Here, data warehousing helps to manage money and other financial activities of businesses and government-sponsored enterprises.
  • Banking services: Data warehouses combine global capabilities with deep local knowledge to provide innovative products and services. Its major objective is to meet the needs of its customers and clients.
  • Consumer goods: Data warehouses help the businesses that deal in consumer goods like Amazon. Data warehouses help e-commerce companies to analyze and manage consumer needs.
  • Retail sectors: Retail is the process of selling consumer goods. Data warehouse helps in managing and analyzing all the data related activities of the businesses. It will manage all the sales and purchase activities.
  • Controlled manufacturing: Data warehouses specializes in the precision machining of the complex component. Also, it will take care of data handling related to raw material.

Data Warehouses and Data Mining

Data is a valuable asset for an organization. Databases play a major role in almost all areas, including education, science, finance, medicine, etc. The storage capacity and the computing speed of computers have increased a lot in recent times. For resolving these problems, big data is being handled by computers using different techniques.

Data mining is the process in which the system finds similar patterns in a given data set. Data mining is also used in the context of fraud detection as an aid in different business processes. At the same time, the data warehouse provides generalized and consolidated data. Along with this, data warehouses also supply us with analytical tools.

Data cleaning and data transformation are important steps in improving the quality of the data mining process. Overall, data mining and data warehousing work together and offer a knowledge source to the businesses. These two aspects of data analytics also help businesses in predictive analysis.

Conclusion 

References – 

  1. https://theintactone.com/2019/03/27/dbms-u4-topic-2-data-warehousing/
  2. https://www.tutorialspoint.com/dwh/dwh_data_warehousing.htm
  3. https://propathshala.com/data-warehouse-2/
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