Logical Data Modeling Training

This course is about taking knowledge of the business and its rules and converting these into a stable data model. The data model is a representation of the objects that the business uses, the characteristics of those objects and the rules that govern their relationship.
Course Details


3 days


No prior knowledge is presumed.

Target Audience

  • Business and Systems Managers
  • Business and Systems Users
  • Business Systems Analysts
  • Systems Analysts
  • Project Managers
  • Project Team Members
  • Data/Database Administrators

Skills Gained

Be able to produce models that are:
  • Independent of implementation and organizational structure
  • Accurate representation of the business
  • Stable
  • Simple (because they use refinement)
  • Appropriately scoped
  • Based on sound theoretical principles
  • Easy to understand.
Course Outline
  • Introduction
    • What is Data Modeling
    • Why use Data Modeling
    • The benefits of Data Modeling
    • Overall development framework
      • Stages of development
      • The kinds of projects
    • Data driven development
    • Modeling concepts
      • Data modeling
      • Process modeling
      • Usage modeling (model interaction)
    • Characteristics of good models
  • High Level Data Modeling
    • Introduction to data modeling
    • Brainstorming business rules, entities and relationships
    • Rules for the High Level Data Model
    • Explanation of major objects
      • Entities, Attributes, Relationships
      • Business rules
      • Multiple and recursive relationships
    • Purpose of high level: Scope, management review, top-down framework
    • Finding primary entities
    • Defining relationships
    • Validating entities
    • Identifying keys
  • Detailed Data Modeling
    • Model expansion
    • Detailed modeling constructs
    • Methods of Model Expansion
    • Types of Data
    • Types of Keys
    • Types of Entities
  • Normalization
    • What normalization is
    • What normalization is not
    • Rules and steps of normalization
    • Practical tips for normalization
  • View Analysis
    • Definition of a data view
    • Sources of data views of data
    • Importance of views
    • Results of views analysis
  • Current Systems Analysis
    • Reasons for doing current systems analysis
    • Analyzing current data
    • Problems in current data analysis
    • Analyzing current processes
    • Importance of current systems analysis
  • Model Consolidation
    • Reality of separate model development
    • Importance of integration
    • Rules for integration
    • Conflict resolution
  • Data Model Refinement
    • Abstraction:  generalization and aggregation
    • Subtyping
    • Aggregation
    • Bill of materials
    • Derived data
    • Change data
    • Modeling goals
    • Modeling time
    • Final model stabilization
  • Model Interaction
    • The importance of model interaction
    • Issues in model interaction
    • Integrating models via matrices
    • Integrating models via maps
    • Integrating models via views
    • Other validations and cross-checks
  • Preparing for Design
    • Phase review
    • Review participants
    • Goals of phase review
    • Introduction to design
    • Purpose of design
    • Steps of design
    • Safe data design trade-offs
    • Aggressive data design trade-offs
  • Conclusion