Paul K Chen

1. Data Warehouse Fundamentals Chapter 1 Introduction to Data Warehouse Paul K Chen 1

2. Introduction to Data Warehouse Portions of the Materials at this website subject-Data Warehouse Fundamentals -are drawn from the Textbooks below: Data Warehouse Fundamentals Author: Paulraj Ponniah Publisher: John Wiley & Sons, Inc. 2001 Database Systems Authors: Thomas Connolly and Carolyn Begg Publisher: Wesley Longman, Inc. Second Edition

3. Road Map for Learning By Subject DW Overview Chapters 1 Chapter 2 DW Architecture/Components/Building Blocks Chapters 3 Trends DW Project Planning and Management Chapter 4 Chapter 5 Analyzing DW Business Requirements Chapters 6,7 Relational & Dimensional Modeling-DW DB Design Chapters 8, 9, 10 Chapter 11 DW Information Delivery/Data Retrieval by OLAP and Data Mining via Web Physical Design Process and Data Quality

4. Chapter 1 - Objectives • Understand the differences between data and information and the information crisis • Recognize the information crisis at every enterprise • Understand the various ways of organizing and managing information for decision making use • Review the history of decision support systems • Learn briefly what is data warehouse and see why data warehousing is the viable solution

5. Data and Information • We’re told we live in the “information age”. • People often talk about data and information as if there were the same. They are, in many regards, opposite. • A datum is just a fact—your name is a fact, your phone number is a fact. • Information is data that is presented in a meaningful, understandable and. beneficial format. Information is data that has been organized , sequenced, correlated and summarized, such as a phone book.

6. Data and Information • A phone book is information. It not only contains names and phone numbers, but it correctly associates each person’s phone number with their names. It presents this list of correlated names and phone numbers in alphabetical sequence, so that we find the phone number from the name. In addition, it divides the phone numbers into two types; personal and business. • It is the function of the computer to convert data to information.

7. Definitions • Database: The database is a place where you put your data; data that you wish to convert to information at some future time. • Database Management System: A DBMS is the software that converts the data in your database to information. It is the DBMS that provides you the capability for cross-referencing, correlating, sorting, summarizing, etc.

8. Information as A Competitive Weapon Information technology and quality information are not the goals, but merely to support organizations to reach goals of • Superior products and services • Greater productivity • Eventually success

9. Data Resource Management (DRM) MIS (OLTP) & OOAD KM (Knowledge Mgt), KWS (Knowledge Work Systems) DSS; ESS, EIS (Executive Information Systems) Data Warehousing/Data Mart/Data Mining/OLAP (Executive, Collaborative and individual levels) Data, Information, and Decision • Data • Information (Data + Process) • Knowledge • Decision (Information + Knowledge) • Data/Information/Decision

10. Data, Information, and Decision bySubject • Data Data processing + Processing System Analysis/Design • Information MIS, Database Systems • Object (Data+Processing) Object-Oriented SD/DA • Knowledge Artificial Intelligence + Information Expert system • Decision (executive level) DSS, EIS • Decision (all levels, sophisticated)Data warehousing Data Mining

11. The Information Crisis • Integrated: Must have a single, enterprise-wide view. • Data Integrity: Information must be accurate and must conform to business rules. • Accessible: Easily accessible with intuitive access paths, and responsive for analysis. • Credible: Every business factor must have one and one value. • Timely: Information must be available within the stipulated time frame.

12. The Era of Information-Based Management—Five Themes • A Single Information Source (E-Business) • Distributed Information Availability (XML) • Information In A Business Context (Decision Support Systems) • Automated Information Delivery (for ex., Trigger) • Information Quality and Ownership (for ex., DRM)

13. Complete E-Business Suite ERP EAI Marketing Sales Projects Financial Services One Database Order Mgt Procurement Human Resources Customer Relationship(CRM) Manufacturing Supply Chain (SCM)

14. What is EAI? • What is EAI? EAI refers to Enterprise Application Integration. EAI is the merging of applications and data from various new and legacy systems within a business. Various means are employed to accomplish EAI, including middleware, in order to unify IT resources, maximize new ERP investments, diminish errors and get everyone on the same page. EAI enables companies to link their existing software applications with each other and with portals. EAI provides the ability to get their applications to exchange critical data. EAI is usually close to the top of any CIO's list of concerns. There are different approaches to EAI. Some rely on linking specific applications with tailored code, but most rely on generic solutions, typically called middleware. XML,combined with SOAP and UDDI, is a kind of middleware.

15. Data Warehouse & ERP • ERP = Enterprise Resource Planning – A software solution that addresses enterprise needs taking the process view of an organization to meet the organization goals. -- It integrates all the departments and functions across a company into a single computer system that can serve all those different departments’ particular needs.

16. Information System Categories

17. Information System Categories

18. DATA RESOURCE MANAGEMENT (DRM) • DEFINITION DATA RESOURCE MANAGEMENT (DRM) IS THE BUSINESS DISCIPLINE WHICH FOCUSES ON HOW DATA CAN BE MANAGED TO MOST EFFICIENTLY SUPPORT THE BUSINESS ENTERPRISE. DRM ADDRESSES THE MANAGEMENT OF ALL ENTERPRISE DATA. WHEN COMBINED WITH OTHER ENTERPRISE PROCESSES, DRM PROVIDES INFORMATION WHEN NEEDED, WHERE NEEDED, IN THE FORM NEEDED, WITH DESIRED ACCURACY AND AT MINIMUM COST FOR BUSINESS ENTERPRISE.

19. DATA RESOURCE MANAGEMENT (DRM) DATA RESOURCE MANAGEMENT BECOMES INCREASINGLY CRITICAL TO THE SUCCESS OF THE CORPORATION IN THE MARKETPLACE DUE TO THESE NEW REALITIES: • THE COMPETITIVE, GLOBAL ENVIRONMENT THAT BUSINESS IS FACING • EXPLOSIVE GROWTH OF THE WEB OVER THE INTERNET • INCREASING USE OF DATA WAREHOUSE SYSTEMS TO MAKE BETTER DECISIONS

20. DATA RESOURCE MANAGEMENT (DRM) WHAT IT IS: • PROVIDING A UNIFIED AND INTEGRATED APPROACH FOR PLANNING, CONTROL AND INTEGRATION OF OUR DATA ASSETS IN SUPPORT OF ENTERPRISE’S BUSINESS • ENCOURAGING THE REDUCTION OF UNNECESSARY DATA DUPLICATION • ENCOURAGING THE REUSE AND SHARING OF HIGH QUALITY DATA • DONE RIGHT, THE INVESTMENT CAN BE PAID BACK MANY TIMES OVER.

21. DRM PRINCIPLES THE FOLLOWING PRINCIPLES SERVE AS GUIDELINES FOR MANAGING DATA AS AN ENTERPRISE DATA: • STRATEGICALLY AND TECHNICALLY DRIVEN: THE EXISTENCE OF EACH DATA ITEM MUST BE JUSTIFIED BY A BUSINESS PROCESS REQUIRED OF EITHER SHORT-TERM OR LONG-TERM GOALS.

22. DRM PRINCIPLES (Continued) • DATA LIFE CYCLE ASSESSMENT DATA LIFE CYCLE FROM ACQUISITION OR CREATION TO PRODUCTION OR DELETION MUST BE PERIODICALLY ASSESSED BASED ON BUSINESS NEEDS AND CLIMATES.

23. DRM PRINCIPLES (Continued) • DATA DEFINED DATA MUST BE UNIQUELY DEFINED AND ASSIGNED PRECISE MEANING PER ORGANIZATION VOCABULARY.

24. DRM PRINCIPLES (Continued) • INTEGRITY DATA INTEGRITY RULES MUST BE MAINTAINED TO ASSURE CONSISTENCY AND TO CONTROL REDUNDANCY.

25. DRM PRINCIPLES (Continued) • SECURITY/CONFIDENTIALITY DATA MUST BE PROTECTED FROM UNAUTHORIZED AND INADVERTENT ACCESS, MODIFICATION, DESTRUCTION AND DISCLOSURE.

26. DRM PRINCIPLES (Continued) • ACCESSIBILITY DATA MUST BE MADE AVAILABLE WHEN AND WHERE NEEDED FOR SHARING AND REUSE.

27. DRM PRINCIPLES (Continued) • DATA STEWARDSHIP DATA SUBJECT AREAS WILL BE MANAGED BY A TEAM OF PEOPLE KNOWN AS DATA OWNERS AND CUSTODIANS. THE GROUP IS RESPONSIBLE FOR ASSURING THAT DATA STRUCTURE REFLECTS BUSINESS POLICIES AND RULES.

28. DRM PRINCIPLES (Continued) • COST/BENEFIT OPTIMIZATION DATA MUST BE UTILIZED TO MAXIMIZE BUSINESS BENEFITS AT A MINIMUM COST.

29. Knowledge Management (KM) – Side Benefits of DRM • It is a systematic process for capturing, integrating, organizing, and communicating knowledge accumulated by employees. • It is a vehicle to share corporate knowledge so that employees may be more more effective and be productive in their work. • A knowledge management system must store all such knowledge in a knowledge repository.

30. What is AI? • What is intelligence? • The ways humansthink.. • The ways humans behave .. • The ways rational/intelligent things think.. • -The ways rational/intelligent things behave… • AI is the science of understanding intelligence and the art of making intelligent things

31. What does AI do? • Automation of problem solving • Learning • Memory (Knowledge Representation) • Reasoning • Acting • Study of mental faculty through computational models • Making computers do what people do better now (or did better at some point!)

32. History of Decision-Support Systems • Ad Hoc Reports • Special Extract Programs • Small Applications • Information Centers • Decision-Support Systems • Executive Information Systems

33. 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

34. Four Levels of Analytical Processing • 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

35. The Evolution of Data Warehousing • Since 1970s, organizations gained competitive advantage through systems that automate business processes to offer more efficient and cost-effective services to the customer. • This resulted in accumulation of growing amounts of data in operational databases.

36. The Evolution of Data Warehousing • Organizations now focus on ways to use operational data to support decision-making, as a means of gaining competitive advantage. • However, operational systems were never designed to support such business activities. • Businesses typically have numerous operational systems with overlapping and sometimes contradictory definitions.

37. The Evolution of Data Warehousing • Organizations need to turn their archives of data into a source of knowledge, so that a single integrated / consolidated view of the organization’s data is presented to the user. • A data warehouse was deemed the solution to meet the requirements of a system capable of supporting decision-making, receiving data from multiple operational data sources.

38. 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

39. 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

40. A New Type of System Environment • Data is designed for analytical tasks • Data from multiple applications • Easy to use and conductive to long interactive sessions by users • Read-intensive data usage • Direct interaction with the system by the users without IT assistance • Content updated periodically and stable • Content to include current and historical data • Ability for users to run queries and get results online • Ability for users to initiate reports

41. What is a Data Warehouse? Characteristics: 1. A central database that is loaded from multiple operational databases for the purpose of end-user access and decision support. Data Warehousing is a decision support system. It has the Following characteristics:

42. What is a Data Warehouse? - Continued 2.A data warehouse differs from an operational system in that the data it contains is normally static and updated in a scheduled manner through massive loading procedures.

43. What is a Data Warehouse? - Continued 3. A data warehouse is developed to accommodate random, ad hoc queries and to allow users to ‘drill down’ to minute levels of detail.

44. Definition Bill Inmon defines a central data warehouse as a database that is: 1. Subject Oriented Data naturally congregates around major categories within any corporation. These categories are called subject areas. For example, subject areas are bill of material, customer, product, and criminal profile. The subject area will be designed to contain only the data appropriate for decision support analysis.

45. Definition (Continued) 2. Integrated Data integration is displayed by consistence in the measurement of variables, naming conventions, physical data definitions across the data. There will be only one definition, identifier, etc., for each subject area.

46. Definition (Continued) 3. Time Variant Data in the DW is historical and accurate as of some point in time. Since DW data is extracted from operational systems, it must have an element of time as part of its key structure.

47. Definition (Continued) 4. Static Since the data in DW is a snap shot extracted from operational system, it must be static or non-updateable.

48. Definition (Continued) 5. Data Granularity • Data in the warehouse is summarized at different levels. • Granularity levels are based on the data types and the expected system performance for queries.

49. The Benefits of Data Warehouse • Enable workers to make better and wiser decisions A data warehouse is specifically developed to allow users the ability to explore data in an unlimited number of ways, accommodating essentially any query a manager could dream up and providing access to the data sources that are behind the results. For example, information gleaned from a data warehouse can change pricing information.

50. The Benefits of Data Warehouse • Identify hidden business opportunities A data warehouse performs a second, and very valuable function by searching data for trends and abnormalities which users may not know to look for. For example: Assisting companies in spotting sales trends, and detecting erroneous or fraudulent billings.