Chapter I Introduction to Data Warehousing

1. Chapter I Introduction to Data Warehousing Presented by: Hongying lian Date: 11/07/2000 Course: CSSE 541

2. Overview • Describe the four levels of analytical processing in modern organizations that will drive the evolution of the data warehousing process • Describe the overall architecture for the information data superstore (IDSS), which can also be called as super data warehouse • Define basic terminologies for the data warehouse technology

3. Objectives of Today’s Businesses • Access and combine data from a variety of data stores • Perform complex data analysis across these date stores • Create multidimensional views of data and its metadata • Easily summarize and roll up the information across subject areas and business dimensions

4. These objectives cannot be met easily • Data is scattered in many types of incompatible structures. • Lack of documentation has prevented from integration older legacy systems with newer systems • Internet software like searching engine needs to be improved • Accurate and accessible metadata across multiple organizations is hard to get

5. Four Levels of Analytical Processing • In modern organization, at least four levels of analytical processing should be supported by information systems • First level: Consists of simple queries and reports against current and historical data • Second level: Goes deeper and requires the ability to do “what if” processing across data store dimensions

6. Four Levels of Analytical Processing (Con’t) • Third level: Needs to step back and analyze what has previously occurred to bring about the current stat of the data • Fourth level: Analyzes what has happened in the past and what needs to be done in the future in order to bring some specific change

7. Information Data Superstore (IDSS) • Also named Super Data Warehouse • Introduced in a paper by Bischoff and Yevich • Define the architecture needed to support the four levels of analytical processing

8. Information Data Superstore (IDSS) (Con’t) User’s perspective of the IDSS

9. Information Data Superstore (IDSS) (Con’t) • Unfortunately, IDSS can’t be fully implemented by today’s technology • Lack of effective product, which can join data on fields with a common meaning • Lack of product with a dynamic, active and unstructured data directory that will support cross-organizational data access • Lack of administration tools that will ease the burden of both the data administration and database administration staffs in metadata maintenance

10. Data Warehouse Technology • A strategy to build the basic constructs of the IDSS with today’s technologies • Definition given by W.H.Inmon • The data warehouse is a collection of integrated, subject-oriented databases designed to support the DSS (decision support) function, where each unit of data is relevant to some moment in time

11. Data Warehouse Technology (Con’t) • The data should be well-defined, consistent, and nonvolatile in nature. • The quantity of data should be large enough to support data analysis, querying, reporting, and comparisons of historical data over a longer period of time. • The data warehouse must be user driven.

12. Operational Data Store vs. Data Warehouse Technology

13. Operational Data Store vs. Data Warehouse Technology

14. Data Flow in a Single Organization