1. 1 Reviewing Data Warehouse Basics

2. Lessons 1.Reviewing Data Warehouse Basics 2.Defining the Business and Logical Models 3.Creating the Dimensional Model 4.Creating the Physical Model 5.Storage Considerations for the Physical Model 6.Strategies for Extracting, Transforming, and Loading 7.Summary Management 8.Analytical Capabilities

3. Definition of a Data Warehouse “A data warehouse is a subject-oriented, integrated, nonvolatile, time-variant collection of data in support of management’s decisions.” - Bill Inmon “A system that extracts cleans, conforms, and delivers source data into a dimensional data store and then supports and implements querying and analysis for the purpose of decision making.” - Ralph Kimball

4. Basic Elements of the Data Warehouse Source: Source database or other source form Data staging area: Intermediate area Target: Presentation server for the new data warehouse or data mart SourceTarget Data staging area

5. Diagram of a Data Warehouse System

6. Basic Form of the Data Warehouse Star schema (Dimensional model) CustomerLocation Sales SupplierProduct

7. Data Warehouse and OLTP Database Design Differences Unlike an OLTP database design, a warehouse database design must: Focus on queries Allow incremental development Be a nonvolatile structure Provide historical data

8. Data Warehouse Features A data warehouse: Is a repository for information Improves access to integrated data Ensures integrity and quality Provides an historical perspective Records results Is used by a broad spectrum of end users for a variety of purposes Reduces the reporting and analysis impact on operational systems Requires a major systems integration effort

9. Exploring Data Warehouse Characteristics Subject-oriented Integrated Nonvolatile Time-variant

10. Subject-Oriented Data is categorized and stored by business subject rather than by application. OLTP applications Customer financial information Data warehouse subject Equity plans Shares Insurance Loans Savings

11. Integrated Data on a given subject is integrated. Savings Current account Loans Customer

12. Nonvolatile Warehouse ReadInsert Update Delete Load Operational Read

13. Time-Variant Data warehouse January TimeData 01/01January 02/01February 03/01March

14. Load from Many Sources Nonrelational systems Relational databases External data External formats Archive data Internal data

15. Decision Support System (DSS) Profile of DSS Queries StorageAnalytic DSS ODSDW OLAP DM

16. DDS Data Warehousing Process Extraction RDBMS ETL Federated Data Warehouse Transformation/Load Transformations Publish Data marts DDS Subscribe Portal Access layer(s) Metadata Repository Flat files Operational External Server log files NDS ETL Staging area(s)

17. Comparing Warehouses and Data Marts Data warehouse Data mart Versus PropertyData WarehouseData Mart ScopeEnterpriseDepartment SubjectsMultipleSingle, LOB Data sourceManyFew Implementation timeMonths to yearsMonths

18. Flow of Data StoreFeed Operational data External data Access Relational tools Applications OLAP tools Metadata Summary data Raw data

19. Dependent Data Mart Model Data mart Systems Legacy Operational Internal External Enterprise ODS Data mart

20. Independent Data Mart Model Enterprise ODS Systems Legacy Operational Internal External Data mart

21. Data Warehousing Today Business Intelligence – To help business users understand their business better – To help them make better operational, tactical, and strategic business decisions – To help them improve business performance

22. Data Warehousing Today Customer Relationship Management – Consists of applications that support CRM activities – Single customer view – Campaign segmentation – Customer analysis – Personalization – Customer loyalty scheme

23. Data Warehousing Today Data Mining – Known as Knowledge Discovery – Trying to find meaningful and useful information from a large amount of data – Interactive or automated process to find patterns describing the data and to predict the future behavior of the data based on these patterns Usage – Analyzing the shopping data – Finding out the pattern between crime and location – Customer scoring in CRM in terms of loyalty – Credit Scoring in the credit card industry

24. Data Warehousing Today Master Data Management (MDM) – Consolidates the master data and processes the data through predefined data quality rules. – Any changes on master data in OLTP are sent to MDM – Publishes data to other systems Customer Data Integration – Is a MDM for customer data – The process of retrieving, cleaning, storing, maintaining and distributing customer data

25. Future Trends in Data Warehousing Unstructured Data – Documents, images, audio, video, e-mails Search – Search engine Service-Oriented Architecture (SOA) Real-Time Data Warehouse