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Systems Analyst and Design - Data Dictionary

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Systems Analyst and Design - Data Dictionary

  1. 1. Data DictionarySystems Analysis and Design Coquilla, Kimberly V.
  2. 2. The Data Dictionary • Is a reference work of data about data (metadata), one that is compiled by the systems analyst to guide them through the analysis and design. • It is where the systems analyst goes to define or look up information about entities, attributes and relationships on the ERD (Entity Relationship Design). Is the information you see in the data dictionary.
  3. 3. Importance of a Data Dictionary • Avoid duplication • Allows better communication between organizations who shares the same database. • Makes maintenance straightforward • It is valuable for their capacity to cross-referencing data items. Enables one description of a data item to be stored and accessed by all individuals so that definition for a data item is established and used.
  4. 4. Uses of Data Dictionary • Validates the date flow diagram for completeness and accuracy • Provides starting point for developing screen and reports. • Determine the contents of data stored files • Develop the logic for data flow diagram processes.
  5. 5. The Data Repository • Repository – it is a larger collection of project information. It contains the ff: • Information about the data maintained by the system. • Procedural logic • Screen and report design • Data relationships • Project requirements and the final system deliverables. • Project management information.
  6. 6. Sources of information Data Dictionary Data Flow Data Stores Data Processes
  7. 7. Data Processes Data Flow Data Stores
  8. 8. How data dictionaries relate to data flow diagrams ?
  9. 9. Four Categories of Data Dictionary • Data Flows • Data Structures • Data Elements • Data Stores
  10. 10. Defining the Data Flow • Data flow is a collection of data elements • It is the first component to be defined. • Elements / Fields – used to describe details of each data flow. • Data Structure – group of elements.
  11. 11. ID Description Source of the Data Flow Type of Data Flow Name of Data Structure Comments / Notations Destination of the Data Flow Volume per unit of time Name
  12. 12. Describing Data Structures • Data structures and usually described using algebraic notations. • An equal sign = means “is composed of”. • A plus sign + means “and”. • Braces { } indicates repetitive elements also called repeating groups or tables. • Brackets [ ] represent an either/or situation. • Parentheses ( ) represent an optional element. • Each structural record must be further defined until the entire set is broken down into its component elements.
  13. 13. How are the symbols used ? Repeating items Optional element “and” “is composed of ” “either/or” situation
  14. 14. Groups of elements / Structural Records
  15. 15. Logical and Physical Data Structures • Logical Data Structure – shows what data the business needs for its day- to-day operations. Ex. Name, Address, Orders. • Physical Data Structure – includes additional elements necessary for implementing the system. Examples of physical design elements: • Key fields used to locate records. • Codes to identify the status of master records. • Transaction codes • Repeating group entries containing a count of how many items are in the group. • Limits on the number of items in a repeated group. • A password
  16. 16. Other examples: •12{Monthly Sales} – indicates 12 months in a year. •Customer Master File = {Customer Records} – means indefinitely. • 5 1{Order Line} – both means as a structural record and a repeating item based on Figure.
  17. 17. Data Elements • Data elements definitions describe a data type. • Each element should also be defined to indicate specifically what it represents. It should be specific.
  18. 18. ID Description Name Aliases Default Value Comment / Remarks Length of the Element Data Type Base / Derive Validation Criteria Inputs and Outputs
  19. 19. ID Description Name Aliases Default Value Comment / Remarks Length of the Element Data Type Base / Derive Validation Criteria
  20. 20. Data Stores • Data Stores are created for each different data entity being stored.
  21. 21. ID Description Name Aliases Max., Ave., & Growth of Records Date Set Name File Format File Type Data Structure Primary & Secondary Keys Comments
  22. 22. Creating the Data Dictionary
  23. 23. Parent Processes DataDictionary DataFlowDiagram Child Diagram
  24. 24. Analyzing Input and Output an important step in creating the data dictionary is to identify and categorize system input and output data flow.
  25. 25. Different fields for Input and Output Analysis: 1. Descriptive Name 2. User Contact 3. File Type (is it an Input or Output?) 4. File Format 5. Sequencing Elements 6. List of Elements 7. Comments
  26. 26. Developing Data Stores Data flows represent data in motion data stores represent at rest Data stores contain information of a permanent or semi permanent nature. When data stores are created for only one report screen we refer them as “user views”
  27. 27. Conclusions The ideal data dictionary is automated, interactive, online and evolutionary. The data dictionary should be tied into a number of systems programs so that when an item is updated or deleted from the data dictionary, it is automatically updated or deleted from the data base. The data dictionary may also be used to create screens, reports and forms. -Fin.-

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