WA1822
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
Duration
3 days
Prerequisites
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
