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Data Management Options

Data Management Options. Dr. Merle P. Martin MIS Department CSU Sacramento. Acknowledgments. Dr. Russell Ching ( MIS Dept ) Source Materiel / Graphics Edie Schmidt ( UMS ) - Graphic Design Prentice Hall Publishing (Permissions)

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Data Management Options

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  1. Data Management Options Dr. Merle P. Martin MIS Department CSU Sacramento

  2. Acknowledgments • Dr. Russell Ching (MIS Dept) Source Materiel / Graphics • Edie Schmidt (UMS) - Graphic Design • Prentice Hall Publishing (Permissions) • Martin, Analysis and Design ofBusiness Information Systems, 1995

  3. Agenda • Why manage data? • Definitions • Typical problems • Data Administrator • The DBMS • Distributing data

  4. Why Manage Data? • Delayed output (paycheck) • Locate a resource • Where is the stock item stored? • Where does the employee work?

  5. Why Manage Data? • Make resource decisions • Should we turn account over to collection agency? • Should we send customer letter asking why he / she hasn’t shopped here in 6 months? • Should we give employee overtime?

  6. Why Manage Data? • Determine resource status • Is there enough stock in warehouse to satisfy this customer’s order? • How much should I order? • What is the value of this resource? • balance sheet

  7. Definitions • File: resource inventory: • Material • People • Employees, customers • Funds • Customer balances • Accounts Payable

  8. Definitions • Data Organization • Bit / byte • Character • Field • Record • File • DBMS

  9. Database Data Hierarchy for Stereos to Go { 12345 Smith John A 123 Main Street Sacramento CA 95819 File 12345 Smith John A 123 Main Street Sacramento CA 95819 12345 Smith John A 123 Main Street Sacramento CA 95819 Record Field Smith Character 10110011 (Byte) Bit 1

  10. Definitions • Views: • Physical - how stored • Logical - how viewed and used • Volatility: - % records that change • Immediacy: rapidity of change

  11. Storage Problems • Redundancy • Accuracy • Security • Lack of data sharing • Report inflexibility • Inconsistent data definitions • Too much data • information overload

  12. Data Administrator • Clean up data definitions • Control shared data • Manage distributed data • Maintain data quality

  13. Clean Up Definitions • Synonyms / aliases • Standard data definitions • names and formats • Date of Birth (AJIS) • mm/dd/yy (courts) • dd/mm/yy (corrections) • Data Dictionary • COBOL

  14. Control Shared Data • Local - used by one unit • Shared - used by two or more activities • Impact of proposed program changes on shared data • Program-to-data element matrix • Control or clearinghouse?

  15. Manage Distributed Data • Geographically dispersed • whether shared data or not • Different levels of detail • different management levels

  16. Aggregate Infrequent Quite old External Future Wide Low StrategicPlanning Management Currency Frequency of Use Time Horizon Required Accuracy Source Scope Level of Aggregation Control Operational Control High Internal Detailed Historical Well defined Very frequent Highly current

  17. Maintain Data Quality • Put owners in charge of data • verify data accuracy and quality • Fairbanks Court example • Who owns the data?

  18. Issue Should the Data Administrator control ALL data, or just that data that crosses organizational boundaries? WHAT DO YOU THINK?

  19. The DBMS Data Base Management System: software that permits a firm to: • centralize data • manage them efficiently • provide accessto applications • such as payroll, inventory

  20. DBMS Components • Data Design Language (DDL) • Data Manipulation Language (DML) • Inquiry Language (IQL) • Teleprocessing Interface (TP) • Martin, Figure 16-5

  21. Designers Teleprocess DDL Database DML IQL Interface Update Retrieve Applic. Software Programmers End-Users

  22. IQL LANGUAGE IQL SELECT EMP-ID, EMP-FIRST-NAME, EMP-LAST-NAME, EMP-YTD-PAY FROM EMPLOYEE WHERE EMP-ID=1234 . Data Base

  23. 3-level Database Model • James Martin • Sprague / McNurlin, Fig. 7-2, pg. 207

  24. External Level (1) • User views (logical) • By application program • Each has unique view • Schema / subschema

  25. Schema and Subschemas Physical Database DBMS Software DBMS Overall View of the Data Schema Individual Views Subschema Subschema Subschema User User User User User User

  26. Enterprise Level (2) • Under control of Data Administrator • DBMS • Implementation data removed • passwords • report views

  27. Physical Level (3) • Schema • Pointers (e.g., next record) • Flags (e.g., record frozen)

  28. Traditional Data Models • Hierarchical - one parent • Network • more than one parent • student to course, major • Relational (tables)

  29. Hierarchical Model Project 1 Dept. A Dept. B Dept C 1 2 3 4 5 6 Employees

  30. Network Model John Smith Jane Smith Savings Mortgage Checking

  31. Account Number First Name Middle Initial Last Name . . . Credit Limit Relational Customer Order Number Order Date Account Number Date Shipped Orders Order Number Line Item Number Product Code Quantity Line Items Product Code Product Name Price Unit Manufacturer Code Products Manufacturer Code Manufacturer Name Manufac(turer)

  32. Object-oriented DBMS An object is: • a piece of data PLUS • procedures performed on data PLUS • attributes describing data PLUS • relationship between object and other objects

  33. Distributed Data • Goals: • move processing as close to users as possible • allow several applications to run simultaneously on same data

  34. Distributed Types • Fragmented • distribute data without duplication • users unaware of where data located • Segmented • data duplicated • one site has master file • problem with data synchronization

  35. Why Distribute? • Save money • offload DB processes to less expensive machines (PCs) • Lower telecommunications costs • DB closer to users • Decrease dependence on a single computer manufacturer

  36. Why Distribute • Move control closer to owner • Increased DBMS scope • more varied types of data • link at workstations • Permit storage of multimedia data

  37. True Distributed DB • Local autonomy (ownership) • No reliance on central site • Continuous operations • not affected by another site • Data transparency • Independence

  38. Independence • Fragmentation • Replication • Hardware • Software • Networks • Database

  39. Problems With Distributed Databases • Security • Shared data • simultaneous update • Complexity • Need telecommunications infrastructure

  40. Issue Is data in your organization totally distributed? • How? • Should it be? • Why or why not?

  41. Points to remember • Definition • Typical problems • Role of Data Administrator • The DBMS • Distributing data

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