Datawarehouse & bi introduction

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 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

Datawarehouse & bi introduction

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