Data warehousing for Profit Analysis By A Sai Krishna Geethika Lokanadham Mithun Rajanna KV Kumar

Business Needs A specialized OLAP system for information on a product profitability

Why Datawarehouse? Faster and more efficient decisions-Provide timely access to vast amount of data available across the bank Current and Historical information on a customer risk information Optimize cost per customer-User based Access Improve decision making through standardized reporting and definitions

Objective Profit from a particular customer at any given point of time Aggregate Profit from wholesale/retail customers at any given point of time Profit for any given country’s customer Profit for a particular product customer

Star Schema More effective for handling simpler queries for users to write, and databases to process. Queries are written with simple inner joins between the facts and a small number of dimensions. Star joins are simpler than possible in snowflake schema. Where conditions need only to filter on the attributes desired, and aggregations are fast. To optimize user ease-of-use and retrieval performance by minimizing the number of tables to join to materialize a transaction

Fact Table PROFIT table

Dimension Table

Star Schema

Measures A measure is a property on which calculations (e.g., sum, count, average, minimum, maximum) can be made using pre-computed aggregates.

Product-Profit-Time: In the second quarter of 2011 year the profit of a product p13

Reports Profit for a particular bank during a course of an year/month/which week of the month/whether if it’s a holiday or weekday

Profit for a particular bank during a course of an year/month/which week of the month/whether if it’s a holiday or weekday and whether if the customer is retail or wholesale customer.

Profit based on location for a wholesale or retail customer

Profit from retail or wholesale customers for a particular country for a year » quarter » month

For BOFA for a year->month and for a particular country » state

Thank you …….