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Home > Training > Data Warehousing > Logical Data Modeling Training

Logical Data Modeling Training

Course#: WA1822

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.

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 of WA1822 Logical Data Modeling Training

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

We regularly offer classes in these and other cities. Atlanta, Austin, Baltimore, Calgary, Chicago, Cleveland, Dallas, Denver, Detroit, Houston, Jacksonville, Miami, Montreal, New York City, Orlando, Ottawa, Philadelphia, Phoenix, Pittsburgh, Seattle, Toronto, Vancouver, Washington DC.
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