What you will learn | |
By attending this course you will be able to produce models that are:
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Audience | |
Business and Systems Managers, Business and Systems Users, Business Systems Analysts, Systems Analysts, Project Managers, Project Team Members, Data/Database Administrators. |
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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 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