1. Datawarehouse A sneak preview

2. 2 Data Warehouse Approach An old idea with a new interest: Cheap Computing Power Special Purpose Hardware New Data Structures Intelligent Software Heightened Business Competition

3. 3 Data Warehouse “Queryable source of data in the enterprise” o Common source of consistent organizational information o Identify problems and opportunities o User focused o Retrieval focused

4. 4 What is a data warehouse? A database filled with large volumes of cross-indexed historical business information that users can access with various query tools. The warehouse usually resides on its own server and is separate from the transaction- processing or “run- the-business” systems.

5. 5 Purpose of a data warehouse Provides an architecture for the flow of data from operational systems to decision support systems DW involves a many record analysis, during which all data has to be locked Used to discover trends and patterns Present opportunities Identify problems

6. 6 ROI of data warehouses New insights into Customer habits Developing new products Selling more products Cost savings and revenue increases Cross-selling of products Less mainframe computer storage Identify and target most profitable customers Capital outlay and development/training time can be extraordinary. Quality of system output Levels of risk Intangibles Cio.com (middle ground)‏

7. 7 Data Warehouse Bill Inmon's paradigm: Data warehouse is one part of the overall business intelligence system. An enterprise has one data warehouse, and data marts source their information from the data warehouse. In the data warehouse, information is stored in 3rd normal form. Ralph Kimball's paradigm: Data warehouse is the conglomerate of all data marts within the enterprise. Information is always stored in the dimensional model.

8. 8 Characteristics of DW/BI High profile and high impact High risk Highly political Requires sophisticated and complex data gathering Requires intensive user access, training and support Will be high maintenance

9. 9 DW Lifecycle Principles Focus on the business Build an information infrastructure Deliver in meaningful increments: six to twelve month timeframes Deliver the entire solution: query and display tools in addition to the database

10. 10 DW Lifecycle  Dimensional  Modeling  Project  Plannin g  Business  Requirement s  Definition  Physical  Design  ETL Design &  Development  Deployment  Growth  Maintenance  BI  Application  Specification  BI  Application  Development  Technical  Architecture  Design  Product  Selection &  Installation  Project Management

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12. 12 Key Terms Data warehouse Dimensional model Normalized model Relational database OLAP (online analytical processing)‏ ETL (extraction, transformation, load)‏ Business Intelligence (BI) application Data mining model Ad hoc query

13. 13 Project Roles Business Sponsor* – approves and pays for the project DW/BI manager – organizational DW sponsor Project manager – day to day leader Business project lead – business community interface Business systems analyst – business requirements Data modeler – detailed data analysis Systems Architect – system components

14. 14 Specialized Roles Data warehouse DBA OLAP designer ETL system developer DW/BI management tools developer BI applications developer

15. 15 General IT roles Data steward Security manager BI portal content manager DW/BI educator Relational database administrator OLAP DBA Compliance manager Metadata manager Data mining analyst User support personnel

16. Course Overview

17. 17 What shall we cover ? Relational Databases Basic SQL Advance SQL MySQL software  Install  Create Tables  Execute SQL The role of the DBA Data warehousing Architecture  Technology  Business Dimensional Modelling Prism Software  Install  Customise  Explore Examples of actual DW in various sectors Project management © Prithwis Mukerjee