We have a huge amount of data everywhere but from that data, we can’t get the data we require because we are unable to understand the huge data available to us. With the help of Data Warehousing and Data Mining, we extract and understand the Data and use it according to our needs.


Data Warehousing


It is a process of transforming data into information and making it available to the users in a timely manner to make a difference.


It is a technique which is used for assembling and managing data from various sources for the purpose of answering business questions.


Data Warehouse


Data Warehouse is;


·      Subject Oriented: Data that gives information about a particular subject instead of giving information about a company’s ongoing process.


·        Integrated: It is constructed by integrating multiple heterogeneous resources.


·        Time Variant: Provides details from a historical perspective.


·        Non Volatile: Data once recorded cannot be updated.


Components of the Data Warehousing:


·        Data Extraction and Loading


·        Analysis and Query


·        Metadata


·        Data Mining Tools


Need for Data Warehousing


The industry has huge amount of operational data and the professional user wants to turn this data into useful information. This information is used by the users to support strategic decision making. Data Warehousing stores data of good quality so that the professional user can make a correct decision. From a business perspective, Data Warehousing is the latest marketing weapon which helps to keep the customer by learning more about their needs and thus has become a valuable tool in today’s competitive fast-evolving world.


Data Mining


Data Mining is the process of discovering interesting patterns (knowledge) from a large amount of data.


Data Mining works with Warehouse Data


·        Data Warehousing provides the Enterprise with a memory.


·        Data Mining provides the Enterprise with intelligence.



Application Areas


  Industry      Application
  Finance   Credit card Analysis
  Insurance      Claims, Fraud Analysis
  Telecommunication   Claims, Fraud Analysis
  Transport     Logistic management
  Consumer goods   Promotion Analysis
  Data Service Provider   Value Added Data
  Utilities    Power Usage Analysis