L6

• Data Warehouse is a repository of enterprise or business databases which provides a clear picture of current and historical operations of organizations.
• Data Warehouse experts consider that the various stores of data are connected and related to each other conceptually as well as physically. A business’s data is usually stored across a number of Databases.
• A great example of data warehousing is what Facebook does. They gather all your data such as your friends, your likes, your groups etc. All these data are stored into one central repository.
• RELEVANCE OF DATA WAREHOUSE

  Data warehouse provides a complete and consistent data store from multiple sources which can be easily understood and used in business applications. Some of the application areas include:

  • Integration of data across the enterprise
  • Quick decisions on current & historical data
  • Provide ad-hoc information for loosely-defined system
  • Manage and control businesses
  • Solving what-if analysis
• It is necessary to choose adequate Data Mining algorithms for making Data Warehouse more useful. Data Mining algorithms are used for transforming data into business information and thereby improving decision making process.
• Data Mining is a set of methods used for data analysis, created with the aim to find out specific dependence, relations and rules related to data and making them out in the new higher level quality information.
• Data Mining gives results that show the interdependence and relations of data.
• Data are collected from internal database and converted into various documents, reports, list etc. which can be further used in decision making processes.
• After selecting the data for analysis, Data Mining is applied to the appropriate rules of behavior and patterns.
• Data Mining is also known as “extraction of knowledge”, “data archeology” or “pattern analysis”.
• Example of data mining is credit card companies that will alert you when they think your credit card is fraudulently used by someone other than you.
• DATA MINING PROCESS:

  • Exploration
  • Model building and validation
  • Dependent
• Exploration


  Data preparation, cleaning and transformations   are involved in this stage.
• Model building and validation


  In this stage the best model will be taken based   on their predictive performance.
• Dependent

  In this final stage the best model is selected and it is applied to the new data sets to generate predictions of the expected outcome.
• APPLICATION AREAS OF DATA WAREHOUSE AND DATA MINING IN BUSINESS:

  • Government
  • Finance
  • Banking
  • Direct Marketing
  • Medicine
  • Manufacturing
  • Churn Analysis
  • Marketing segmentation
  • Trend Analysis
  • Fraud Detection
  • Trend Analysis