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Dataware Housing  &  Business Intelligence An Overview By Shivmohan Purohit
Agenda ,[object Object],[object Object],[object Object],[object Object],[object Object]
What a firm/ Organization want to know…. Which are our  lowest/highest margin  customers ? Who are my customers  and what products  are they buying? Which customers  are most likely to go  to the competition ?   What impact will  new products/services  have on revenue  and margins? What product prom- -otions have the biggest  impact on revenue? What is the most  effective distribution  channel?
What is a Data Warehouse? ,[object Object]
What is Data Warehousing? ,[object Object],Data Information
Data Warehousing --  It is a process ,[object Object],[object Object]
Data Warehousing ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
  ,[object Object],[object Object],Data Warehousing
Type of DW Users Explorers :  Seek out the unknown and previously unsuspected rewards hiding in the detailed data Farmers :  Harvest information from known access paths Tourists:  Browse information
Application-Orientation vs. Subject-Orientation Application-Orientation Operational Database Loans Credit  Card Trust Savings Subject-Orientation Data Warehouse Customer Vendor Product Activity
Functioning of Data warehousing Data Source Cleaning Transformation Data Warehouse New Update
Data Warehouse Architecture Data Warehouse  Engine Optimized Loader Extraction Cleansing Analyze Query Metadata Repository Relational Databases Legacy Data Purchased  Data ERP Systems
Star Schema ,[object Object],[object Object],T i m e p r o d c u s t c i t y f a c t date, custno, prodno, cityname,  ...
Snowflake schema ,[object Object],[object Object],T i m e p r o d c u s t c i t y f a c t date, custno, prodno, cityname,  ... r e g i o n
OLAP(Online analytical processing) ,[object Object],[object Object]
OLAP OPERATION on the Multidimensional data ,[object Object],[object Object],[object Object],[object Object]
Multi-dimensional Data ,[object Object],Dimensions:  Product, Region, Time Hierarchical summarization paths Product  Region  Time Industry  Country  Year Category  Region  Quarter  Product  City  Month  Week   Office  Day Month 1  2 3  4  7 6  5  Product Toothpaste  Juice Cola Milk  Cream Soap  Region W S  N
“ Slicing and Dicing” Product Sales Channel Regions Retail Direct Special Household Telecomm Video Audio India Far East Europe The Telecomm Slice
Roll-up and Drill Down ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Roll Up Higher Level of Aggregation Low-level Details Drill-Down
Nature of OLAP Analysis ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Mining ,[object Object]
Data Mining (cont.)
Data Mining works with Warehouse Data ,[object Object],[object Object]
Data Mining Process Cleaning and Integration Databases Data Warehouse Flat Files Patterns Knowledge Selection and transformation Data Mining
Thanks Shivmohan Purohit Q &A  Discussion

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Datawarehouse & bi introduction

  • 1. Dataware Housing & Business Intelligence An Overview By Shivmohan Purohit
  • 2.
  • 3. What a firm/ Organization want to know…. Which are our lowest/highest margin customers ? Who are my customers and what products are they buying? Which customers are most likely to go to the competition ? What impact will new products/services have on revenue and margins? What product prom- -otions have the biggest impact on revenue? What is the most effective distribution channel?
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9. Type of DW Users Explorers : Seek out the unknown and previously unsuspected rewards hiding in the detailed data Farmers : Harvest information from known access paths Tourists: Browse information
  • 10. Application-Orientation vs. Subject-Orientation Application-Orientation Operational Database Loans Credit Card Trust Savings Subject-Orientation Data Warehouse Customer Vendor Product Activity
  • 11. Functioning of Data warehousing Data Source Cleaning Transformation Data Warehouse New Update
  • 12. Data Warehouse Architecture Data Warehouse Engine Optimized Loader Extraction Cleansing Analyze Query Metadata Repository Relational Databases Legacy Data Purchased Data ERP Systems
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18. “ Slicing and Dicing” Product Sales Channel Regions Retail Direct Special Household Telecomm Video Audio India Far East Europe The Telecomm Slice
  • 19.
  • 20.
  • 21.
  • 23.
  • 24. Data Mining Process Cleaning and Integration Databases Data Warehouse Flat Files Patterns Knowledge Selection and transformation Data Mining
  • 25. Thanks Shivmohan Purohit Q &A Discussion