1. Data Warehouse – Your Key to Success

2. Data Warehouse A data warehouse is a  subject-oriented  Integrated  Time-variant  Non-volatile  Restructure the data  Data quality collection of data in support of management's decision making process. A data warehouse is a  subject-oriented  Integrated  Time-variant  Non-volatile  Restructure the data  Data quality collection of data in support of management's decision making process.

3. Subject Oriented A data warehouse can be used to analyze a particular subject area. For example  Sales  Finance  Marketing  Manufacturing  Distribution  Etc. A data warehouse can be used to analyze a particular subject area. For example  Sales  Finance  Marketing  Manufacturing  Distribution  Etc.

4. Integrated A data warehouse integrates data from multiple data sources. For example, source A and source B may have different ways of identifying a product, but in a data warehouse, there will be only a single way of identifying a product. A data warehouse integrates data from multiple data sources. For example, source A and source B may have different ways of identifying a product, but in a data warehouse, there will be only a single way of identifying a product.

5. Time-variant Historical data is kept in a data warehouse. For example, one can retrieve data from  3 months  6 months  12 months  2 years  N years Historical data is kept in a data warehouse. For example, one can retrieve data from  3 months  6 months  12 months  2 years  N years

6. Non-volatile Once data is in the data warehouse, it will not change. So, historical data in a data warehouse should never be altered.

7. Restructure the data Data Restructuring is the process to restructure the source data to the target data during data transformation. Data Restructuring is an integral part in data warehousing. A very common set of processes is used in running large data warehouses. This set of process is called Extract, Transform, and Load (ETL).

8. Data Quality Data quality tools are emerging as a way to correct and clean data at many stages in building and maintaining a data warehouse.  Auditing  Cleansing  Migration Data quality tools are emerging as a way to correct and clean data at many stages in building and maintaining a data warehouse.  Auditing  Cleansing  Migration

9. Data Warehousing Maintain data history, even if the source transaction systems do not. Integrate data from multiple source systems, enabling a central view across the enterprise. This benefit is always valuable, but particularly so when the organization has grown by merger. Improve data quality Information consistently. Data Integrity Restructure the data Maintain data history, even if the source transaction systems do not. Integrate data from multiple source systems, enabling a central view across the enterprise. This benefit is always valuable, but particularly so when the organization has grown by merger. Improve data quality Information consistently. Data Integrity Restructure the data

10. Business Challenges  Common View Of Company Data  KPI from individual Sales Person to top level  Improve the Business  Day-to-day Business questions  Historical view of Business  Common View Of Company Data  KPI from individual Sales Person to top level  Improve the Business  Day-to-day Business questions  Historical view of Business

11. Common View Of Company Data  To build Enterprise Data Warehouse from heterogeneous sources  To Build Subject Oriented  To Build relationship between different subject areas called Integration data  Non-volatile  To build Enterprise Data Warehouse from heterogeneous sources  To Build Subject Oriented  To Build relationship between different subject areas called Integration data  Non-volatile

12. KPI from individual Sales Person to top level  Setup goals and track through individual vs top level  Timely tracking the actuals vs goals set  Role up to top level and finding gaps  Setup goals and track through individual vs top level  Timely tracking the actuals vs goals set  Role up to top level and finding gaps

13. Improve the Business  Identifying the open opportunities using Data Warehousing  Timely closing the Opportunities  Readily available data in Data Warehouse  Identifying the open opportunities using Data Warehousing  Timely closing the Opportunities  Readily available data in Data Warehouse

14. Data Warehousing Architecture Metadata repository Serves Extract Clean Transform Load Refresh BI Data Warehouse External Data Sources OLTP Visualisation Data Mining Decision makers OLTP