sharda_dss10_ppt_03_GE-211566.pptx000000000

Business intelligence ppt

1. Chapter 3: Data Warehousing (3.1 to 3.5 only)

2. Learning Objectives (Continued…) Understand the basic definitions and concepts of data warehouses Learn different types of data warehousing architectures; their comparative advantages and disadvantages Describe the processes used in developing and managing data warehouses Explain data warehousing operations …

3. Learning Objectives Explain the role of data warehouses in decision support Explain data integration and the extraction, transformation, and load (ETL) processes Describe real-time (a.k.a. right-time and/or active) data warehousing Understand data warehouse administration and security issues

4. Opening Vignette… “Isle of Capri Casinos Is Winning with Enterprise Data Warehouse” Company background Problem description Proposed solution Results Answer & discuss the case questions.

5. Questions for the Opening Vignette Why is it important for Isle to have an EDW? What were the business challenges or opportunities that Isle was facing? What was the process Isle followed to realize EDW? Comment on the potential challenges Isle might have had going through the process of EDW development. What were the benefits of implementing an EDW at Isle? Can you think of other potential benefits that were not listed in the case? Why do you think large enterprises like Isle in the gaming industry can succeed without having a capable data warehouse/business intelligence infrastructure?

6. Main Data Warehousing Topics DW definition Characteristics of DW Data Marts ODS, EDW, Metadata DW Framework DW Architecture & ETL Process DW Development DW Issues

7. What is a Data Warehouse? A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format “The data warehouse is a collection of integrated , subject-oriented databases designed to support DSS functions, where each unit of data is non-volatile and relevant to some moment in time” (time variant)

8. A Historical Perspective to Data Warehousing

9. Characteristics of DWs Subject oriented Integrated Time-variant (time series) Nonvolatile Summarized Not normalized Metadata Web based, relational/multi-dimensional Client/server, real-time/right-time/active...

10. Data Mart A departmental small-scale “DW” that stores only limited/relevant data Dependent data mart A subset that is created directly from a data warehouse Independent data mart A small data warehouse designed for a strategic business unit or a department

11. Other DW Components Operational data stores (ODS) A type of database often used as an interim area for a data warehouse Oper marts - an operational data mart. Enterprise data warehouse (EDW) A data warehouse for the enterprise. Metadata: Data about data. In a data warehouse, metadata describe the contents of a data warehouse and the manner of its acquisition and use

12. META DATA Pattern is another way to view metadata . According to the pattern view, we can differentiate between syntactic metadata (i.e., data describing the syntax of data), structural metadata (i.e., data describing the structure of the data), and semantic metadata (i.e., data describing the meaning of the data in a specific domain).

13. The primary purpose of metadata should be to provide context to the reported data; that is, it provides enriching information that leads to the creation of knowledge. M etadata assist in the conversion of data and information into knowledge. F ive levels of metadata management maturity: (1) adhoc , (2) discovered, (3) managed, ( 4) optimized, and (5) automated. These levels help in understanding where an organization is in terms of how and how well it uses its metadata

14. Application Case 3.1 A Better Data Plan: Well-Established TELCOs Leverage Data Warehousing and Analytics to Stay on Top in a Competitive Industry Questions for Discussion What are the main challenges for TELCOs? How can data warehousing and data analytics help TELCOs in overcoming their challenges? Why do you think TELCOs are well suited to take full advantage of data analytics?

15. A Generic DW Framework

16. Application Case 3.2 Data Warehousing Helps MultiCare Save More Lives Questions for Discussion What do you think is the role of data warehousing in healthcare systems? How did MultiCare use data warehousing to improve health outcomes?

17. DW Architecture Three-tier architecture Data acquisition software (back-end) The data warehouse that contains the data & software Client (front-end) software that allows users to access and analyze data from the warehouse Two-tier architecture First two tiers in three-tier architecture is combined into one … sometimes there is only one tier?

18. DW Architectures 3-tier architecture 2-tier architecture 1-tier Architecture ?

19. Data Warehousing Architectures Issues to consider when deciding which architecture to use: Which database management system (DBMS) should be used? Will parallel processing and/or partitioning be used? Will data migration tools be used to load the data warehouse? What tools will be used to support data retrieval and analysis?

20. A Web-Based DW Architecture

21. Alternative DW Architectures

22. Alternative DW Architectures Each architecture has advantages and disadvantages! Which architecture is the best?

23. Ten factors that potentially affect the architecture selection decision Information interdependence between organizational units Upper management’s information needs Urgency of need for a data warehouse Nature of end-user tasks Constraints on resources Strategic view of the data warehouse prior to implementation Compatibility with existing systems Perceived ability of the in-house IT staff Technical issues Social/political factors

24. Teradata Corp. DW Architecture

25. Data Integration and the Extraction, Transformation, and Load Process ETL = E xtract T ransform L oad Data integration Integration that comprises three major processes: data access, data federation, and change capture. Enterprise application integration (EAI) A technology that provides a vehicle for pushing data from source systems into a data warehouse Enterprise information integration (EII) An evolving tool space that promises real-time data integration from a variety of sources, such as relational or multidimensional databases, Web services, etc.

26. Data Integration and the Extraction, Transformation, and Load Process

27. ETL (Extract, Transform, Load) Issues affecting the purchase of an ETL tool Data transformation tools are expensive Data transformation tools may have a long learning curve Important criteria in selecting an ETL tool Ability to read from and write to an unlimited number of data sources/architectures Automatic capturing and delivery of metadata A history of conforming to open standards An easy-to-use interface for the developer and the functional user