WILLIAM G. SMITH & ASSOCIATES INFORMATION RESOURCE MANAGEMENT SEMINARS AND CONSULTING "CLEANING UP THE WORLD'S DATA MESSES"

CONCEPTUAL DATA MODELING: THE ENTITY/RELATIONSHIP MODEL

By far our most popular skill-building seminar, CDM is an intensive course in the discipline of Conceptual Data Modeling (entity/relationship level of data modeling). It is one of the most powerful and effective analytical techniques ever developed for understanding and properly organizing the information required to support any enterprise.

This form of modeling can focus on the overall enterprise or any smaller subset, but generally gives greatest benefit when used to integrate the model across the entire enterprise, ensuring that the same work is never done twice. Applied on an enterprise-wide basis, CDM focuses on the big picture, and surfaces everything the business must know and keep data about in order to operate effectively. Data subjects (entities) are shared across functional, process, and organizational boundaries in the business, and so this model (and the shared databases which will be physically built from it) are the keys to removing unnecessary time and cost in the conduct of the business processes. CDM is also useful in conceptualizing data to be shared directly with customers and suppliers to further collapse time and cost. Building a conceptual data model surfaces conflicts and ambiguities in business terminology, rules, and practices which inevitably cause quality problems for the customer of the enterprise, legal risks, and errors and inconsistencies in the business which are costly and troublesome to correct. Building a CDM gives the enterprise a golden opportunity to "clear up a few things around here" in a very organized and methodical manner. Extensive workshops fully develop modeling skills. The seminar material is completely independent of any vendor's CASE tool.

This is by far the most complete and advanced CDM seminar available anywhere, and gives the student a complete set of tools, rules, and constructs to properly model any complex requirement they will encounter in a real-life modeling effort. Our instructors teach in plain, jargon-free English, so no previous data modeling knowledge or experience is required.

TOPICAL OUTLINE

• Background Concepts
  • The Information Resource Management (IRM) Environment Defined
  • Traditional Disintegrated "Systems" Approach
  • Process and Data Inter-relationship
  • Information Resource Categories
  • The IRM Approach - Big Picture
  • IRM Critical Success Factors
  • The Conceptual Data Model in Context of Other Models
  • Time and Data
  • Data Modeling Sessions - Logistics and Success Factors
  • Definitions of Conceptual Data Modeling Terms
• Conceptual Data Modeling
  • Purpose, Form, and Content of Conceptual Data Model
  • Conceptual Data Modeling Steps
  • 1. Detecting and qualifying entities
    • Rules for Qualifying a Candidate Entity
    • Entity Genus
    • Example Entities
  • 2. Diagramming Entities and Relationships
  • • E/R Diagram Symbols
    • Different E/R Diagram Styles
    • Detecting Candidate Relationships
    • Rules for Qualifying a Candidate Relationship
    • Relationship Naming Conventions
    • Cardinality Notations
    • Reading the E/R Diagram
    • Instance Diagram
    • The Normal Forms in the E/R Model
    • Modeler's Best-Fit Decisions
    • Merging/Synthesising Multiple E/R Diagrams
    • Tips and Example Questions for Discovering/Qualifying Entities and Relationships
    • Special E/R Constructs
      • N-ary relationships
      • Recursive Relationship
      • Subtype/SupertypeConstruct
      • Characteristic/Attributive Entity
      • Associative Entity
      • Relationship Roles
      • Role Entity
      • Exclusive/Alternative Relationships
  • 3. Analyzing and Defining E/R States
    • Multiple Entities vs. States Decision
    • Multiple Relationships vs. States Decision
    • State/Transition Analysis
    • State/Transition Diagram
    • State Variations
    • General State Rules
    • Building a State/Transition Diagram - Questions
    • Capturing State Metadata in the Dictionary - Options
    • State/Transition Analysis Tips
    • Example Questions for State/Transition Analysis
  • 4. Fully Defining Entities and Relationships
    • Preparing and Presenting Business Issues for Decision
    • Entity Definition Pro Forma
      • Example Entity Definition
      • Primary Key Rules
    • Relationship Definition Pro Forma
      • Example Relationship Definition
    • Recording "In-Betweener" Business Rules
    • Example Questions for Writing E/R Definitions
  • 5. Reviewing and Stabilizing the E/R Model
    • Functional/Conceptual Data Model Cross-Validation
    • Stability Review
    • Example Questions for Reviewing and Stabilizing Model
  • The Meta-E/R (Repository) Model to Support Conceptual Data Modeling
  • Course Summary
• Workshops
  • Candidate Entity Discovery and Qualification
  • E/R Diagramming
  • State/Transition Analysis
  • Writing E/R Definitions
  • Stability Analysis

DURATION: 5 days

TARGETED AUDIENCES: (recommended maximum number of attendees - 25)

• CIO, IS/IRM management
• Data and Database Administrators
• Conceptual, Logical and Physical Data Modelers
• Process Analysts/Modelers
• Development Project Managers
• Business persons who will participate in E/R modeling sessions

PREREQUISITE: none