1. E. Wainright Martin Carol V. Brown Daniel W. DeHayes Jeffrey A. Hoffer William C. Perkins MANAGINGINFORMATIONTECHNOLOGY FIFTH EDITION CHAPTER 5 T HE D ATA R ESOURCE

2. © 2005 Pearson Prentice-Hall Chapter 5 - 2 Organizations could not function long without critical business data Cost to replace data would be very high Time to reconcile inconsistent data may be too long Data often needs to be accessed quickly W HY M ANAGE D ATA? Page 135

3. © 2005 Pearson Prentice-Hall Chapter 5 - 3 Data should be:  Cataloged  Named in standard ways  Protected  Accessible to those with a need to know  Maintained with high quality W HY M ANAGE D ATA? Page 135

4. © 2005 Pearson Prentice-Hall Chapter 5 - 4 T ECHNICAL A SPECTS OF M ANAGING THE D ATA R ESOURCE Page 135 Data model – overall map for business data needed to effectively manage the data The Data Model

5. © 2005 Pearson Prentice-Hall Chapter 5 - 5 Page 135 Data modeling involves:   Methodology, or steps followed to identify and describe data entities   Notation, or a way to illustrate data entities graphically The Data Model T ECHNICAL A SPECTS OF M ANAGING THE D ATA R ESOURCE

6. © 2005 Pearson Prentice-Hall Chapter 5 - 6 Page 135 Entity-relationship diagram (ERD)   Most common method for representing a data model and organizational data needs   Captures entities and their relationships   Entities – things about which data are collected   Attributes – actual elements of data that are to be collected T ECHNICAL A SPECTS OF M ANAGING THE D ATA R ESOURCE The Data Model

7. © 2005 Pearson Prentice-Hall Chapter 5 - 7 Page 135 Figure 5.1 Entity-Relationship Diagram NOTE: Entities are Customer, Order, and Product. Attributes of the Customer entity could be customer last name, first name, street, city, … T ECHNICAL A SPECTS OF M ANAGING THE D ATA R ESOURCE The Data Model

8. © 2005 Pearson Prentice-Hall Chapter 5 - 8 Page 136 Enterprise modeling   Top-down approach   Describes organization and data requirements at high level, independent of reports, screens, or detailed specifications   Not biased by how business operates today T ECHNICAL A SPECTS OF M ANAGING THE D ATA R ESOURCE Data Modeling

9. © 2005 Pearson Prentice-Hall Chapter 5 - 9 Page 136 Enterprise Modeling Steps: Divide work into major functions Divide each function into processes Divide processes into activities List data entities assigned to each activity Identify relationships between entities T ECHNICAL A SPECTS OF M ANAGING THE D ATA R ESOURCE Data Modeling Figure 5.2 Enterprise Decomposition for Data Modeling

10. © 2005 Pearson Prentice-Hall Chapter 5 - 10 Page 136 View integration   Bottom-up approach   Each report, screen, form, document produced from databases first … each called a user view T ECHNICAL A SPECTS OF M ANAGING THE D ATA R ESOURCE Data Modeling

11. © 2005 Pearson Prentice-Hall Chapter 5 - 11 Page 136 View Integration Steps: Create user views Identify data elements in each user view and put into a structure called a normal form Normalize user views Integrate set of entities from normalization into one description T ECHNICAL A SPECTS OF M ANAGING THE D ATA R ESOURCE Data Modeling Normalization – process of creating simple data structures from more complex ones

12. © 2005 Pearson Prentice-Hall Chapter 5 - 12 Page 136-137 Data modeling guidelines:   Objective – effort must be justified by need   Scope – broader scope, more chance of failure   Outcome – uncertainty leads to failure   Timing – consider an evolutionary approach T ECHNICAL A SPECTS OF M ANAGING THE D ATA R ESOURCE Data Modeling

13. © 2005 Pearson Prentice-Hall Chapter 5 - 13 Page 137 T ECHNICAL A SPECTS OF M ANAGING THE D ATA R ESOURCE Database Architecture Database – shared collection of logically related data, organized to meet needs of an organization Database Architecture – way in which the data are structured and stored in the database

14. © 2005 Pearson Prentice-Hall Chapter 5 - 14 Page 137 Figure 5.3 The Data Pyramid

15. © 2005 Pearson Prentice-Hall Chapter 5 - 15 Page 138 T ECHNICAL A SPECTS OF M ANAGING THE D ATA R ESOURCE Six basic database architectures: 1. Hierarchical (top-down organization) 2. Network (high-volume transaction processing) 3. Relational (data arranged in simple tables) 4. Object-oriented (data and methods encapsulated in object classes) 5. Object-relational (hybrid of relational and object- oriented) 6. Multidimensional (used by data warehouses) Database Architecture

16. © 2005 Pearson Prentice-Hall Chapter 5 - 16 Page 138 T ECHNICAL A SPECTS OF M ANAGING THE D ATA R ESOURCE Tools for Managing Data Database Management System (DBMS) – support software used to create, manage, and protect organizational data

17. © 2005 Pearson Prentice-Hall Chapter 5 - 17 Page 139 T ECHNICAL A SPECTS OF M ANAGING THE D ATA R ESOURCE A DBMS helps manage data by providing seven functions: 1. Data storage, retrieval, update 2. Backup 3. Recovery 4. Integrity control 5. Security control 6. Concurrency control 7. Transaction control Tools for Managing Data

18. © 2005 Pearson Prentice-Hall Chapter 5 - 18 Page 139 T ECHNICAL A SPECTS OF M ANAGING THE D ATA R ESOURCE Most popular type of database architecture is relational Not all relational systems are identical. Best effort to date for standardizing relational databases is SQL Tools for Managing Data Important Notes:

19. © 2005 Pearson Prentice-Hall Chapter 5 - 19 Page 139-140 T ECHNICAL A SPECTS OF M ANAGING THE D ATA R ESOURCE Contains:  Definition of each entity, relationship, and data element  Display formats  Integrity rules  Security restrictions  Volume and sizes  List of applications that use the data Tools for Managing Data Data Dictionary/Directory (DD/D) – central encyclopedia of data definitions and usage information … a database about data

20. © 2005 Pearson Prentice-Hall Chapter 5 - 20 Page 140 T ECHNICAL A SPECTS OF M ANAGING THE D ATA R ESOURCE Database Programming Query language – a 4 GL, nonprocedural programming language to obtain data from a database, often provided by the DBMS SQL query language example: SELECT ORDER#, CUSTOMER#, CUSTNAME, ORDER-DATE FROM CUSTOMER, ORDER WHERE ORDER-DATE > ’04/12/05’ AND CUSTOMER.CUSTOMER# = ORDER.CUSTOMER#

21. © 2005 Pearson Prentice-Hall Chapter 5 - 21 The need to manage data is permanent Data can exist at several levels Application software should be separate from the database Application software can be classified by how they treat data 1. Data capture 2. Data transfer 3. Data analysis and presentation M ANAGERIAL I SSUES IN M ANAGING D ATA Page 140 Principles in Managing Data

22. © 2005 Pearson Prentice-Hall Chapter 5 - 22 Page 142 Figure 5.4

23. © 2005 Pearson Prentice-Hall Chapter 5 - 23 Application software should be considered disposable Data should be captured once There should be strict data standards M ANAGERIAL I SSUES IN M ANAGING D ATA Page 143 Principles in Managing Data

24. © 2005 Pearson Prentice-Hall Chapter 5 - 24 M ANAGERIAL I SSUES IN M ANAGING D ATA Page 143 Principles in Managing Data Figure 5.5 Types of Data Standards

25. © 2005 Pearson Prentice-Hall Chapter 5 - 25 M ANAGERIAL I SSUES IN M ANAGING D ATA Page 144 The Data Management Process Figure 5.6 Asset Management Functions

26. © 2005 Pearson Prentice-Hall Chapter 5 - 26 Page 146 Figure 5.7 The Data Warehouse

27. © 2005 Pearson Prentice-Hall Chapter 5 - 27 M ANAGERIAL I SSUES IN M ANAGING D ATA Organizations should have policies regarding:  Data ownership  Data administration Page 148 Data Management Policies

28. © 2005 Pearson Prentice-Hall Chapter 5 - 28 M ANAGERIAL I SSUES IN M ANAGING D ATA Page 148 Data Ownership Corporate information policy – foundation for managing the ownership of data

29. © 2005 Pearson Prentice-Hall Chapter 5 - 29 Page 149 Figure 5.8 Example Data Access Policy

30. © 2005 Pearson Prentice-Hall Chapter 5 - 30 Data Administration Page 150 Key functions of the data administration group: Promote and control data sharing Analyze the impact of changes to application systems when data definitions change Maintain the data dictionary Reduce redundant data and processing Reduce system maintenance costs and improve system development productivity Improve quality and security of data Insure data integrity M ANAGERIAL I SSUES IN M ANAGING D ATA

31. © 2005 Pearson Prentice-Hall Chapter 5 - 31 Data Administration Page 150-151 Key functions of the database administrator (DBA): Tuning database management systems. Selection and evaluation of and training on database technology. Physical database design. Design of methods to recover from damage to databases. Physical placement of databases on specific computers and storage devices. The interface of databases with telecommunications and other technologies. M ANAGERIAL I SSUES IN M ANAGING D ATA