IntroductionCourse Outline
What you will learn

By attending this course you will 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.
Audience

Business and Systems Managers, Business and Systems Users, Business Systems Analysts, Systems Analysts, Project Managers, Project Team Members, Data/Database Administrators. 
Prerequisites
There are no pre-requisites. Everything you need to know about data modeling is taught during the course itself.
Duration
3 day

Outline for Logical Data Modeling

1. 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

2. 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
  • EXERCISE:  High level data modeling

3. DETAILED DATA MODELING

  • Model expansion
  • Detailed modeling constructs
  • Methods of Model Expansion
  • Types of Data
  • Types of Keys
  • Types of Entities
  • EXERCISE:  Model expansion

4. NORMALIZATION

  • What normalization is
  • What normalization is not
  • Rules and steps of normalization
  • Practical tips for normalization
  • EXERCISE: Mini-exercise
  • EXERCISE: Case study

5. VIEWS ANALYSIS

  • Definition of a data view
  • Sources of data views of data
  • Importance of views
  • Results of views analysis
  • EXERCISE:  Data views for case study

6. 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

7. MODEL CONSOLIDATION

  • Reality of separate model development
  • Importance of integration
  • Rules for integration
  • Conflict resolution

8. DATA MODEL REFINEMENT

  • Abstraction:  generalization and aggregation
  • Subtyping
  • Aggregation
  • Bill of materials
  • Derived data
  • Change data
  • Modeling goals
  • Modeling time
  • Final model stabilization
  • EXERCISE: Model refinement in case study

9. 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
  • EXERCISE:  Data usage mapping

10. 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

11. CONCLUSION

  • Success factors in implementing data modeling
  • General Review

12. GLOSSARY OF TERMS