TDWI Data Modeling: Data Analysis and Design for BI and Data Warehousing Systems Training in New Zealand

This course assumes basic understanding of data warehousing fundamentals.

Data architects; data modelers; BI program and project managers; BI/DW system developers

• Differences in modeling techniques for business transactions, business events, and business metrics
• Different types of data and their implications
• Application of business context to modeling activities
• The role of business requirements in BI data modeling
• The role of source data analysis in data modeling
• Use of normalized modeling techniques for data warehouse analysis and design
• Use of dimensional modeling techniques for data warehouse analysis and design
• The roles of generalization and abstraction in data warehouse design
• The roles of identity and hierarchy management in data warehouse design
• How time-variant data is represented in data models
• Implementation and optimization considerations for warehousing data stores

Module One: Data Modeling Concepts

The Data Modeling Life Cycle 

• Where Data Modeling Begins And Ends
• Between Business Needs And Implemented Data

Kinds Of Data Systems

• Business Uses Of Data

Data Taxonomies

• Data Properties
• Data Characteristics

Data Modeling Framework For BI

• Where And What To Model

Module Two: Business Data Models

Business Context

• Business Drivers, Goals, And Strategies
• Business Information Needs
• Business Domains
• Business Subjects

Business Data Model Development

• Top-Down – Incremental And Iterative

Gathering Business Questions

• The Modeling Process
• Working With The Business
• An Example

Analyzing Business Questions

• The Modeling Process
• Mapping Facts And Qualifiers – Finding The Facts
• Mapping Facts And Qualifiers – Fact/Qualifier Associations
• An Example

Fact Analysis And Refinement

• Removing Redundancy
• An Example

Qualifier Analysis And Refinement

• Finding Hierarchies
• An Example

Business Dimensional Modeling

• The Modeling Process
• An Example

Module Three: Logical Data Models

What To Model

• The Data And Information Pipeline

Understanding Data Structures

• Why Sources Matter
• Extracting Source Data Structure
• Source Data Profiling

Logical Relational Modeling

• The Modeling Process
• Logical Models For Data Warehouse And Ods
• A Data Warehouse Example
• Logical Models For Marts And Reporting

Logical Dimensional Modeling

• Data Structure Of Business Metrics
• The Modeling Process
• Modeling Meters And Measures
• Adding The Dimensions
• Refining And Enriching The Dimensions
• Declaring The Grain
• Refining And Enriching The Measures

Logical Models And Business Metrics

• Creating A Catalog Of Metrics
• Classifying Metrics
• An Example

Logical Models And Business Analytics

• Analytics Applications
• Data Mining Applications

Logical Models And Master Data Management

• Identity Management
• Hierarchy Management

Logical Models And Unstructured Data

• Unstructured Data And Content Management
• Unstructured Data And Text Analytics
• Big Data

Module Four: Implementation Data Models

Data Structure In Transaction Systems

• Extracting The Structure Of Existing Data

Structural Modeling And Data Integration

• From Business Models To Technology Models
• Normalization
• The Normalization Process
• A Normalization Example
• Time-Variant Data Structures
• A Snapshot Example
• An Audit Trail Example
• An Example Of States
• Access, Navigation, Security, And Distribution
• Access And Navigation Examples
• Security And Distribution Examples

Structural Modeling And Business Analytics

• From Metrics Models To Technology Models
• Star-Schema Design
• Star-Schema Design Process
• Star-Schema Design - Modeling Dimension Tables
• Star-Schema Design - Dimension Table Key
• Star-Schema Design – Considering The Facts
• Star-Schema Design – Fact Table Key
• Analytic Application And Data Structures
• Data Mining Data Structures

Physical Design Overview

• The Results Of Physical Design And Implementation

Some Optimization Techniques

• Derivation
• Aggregation
• Summarization
• Horizontal Partitioning
• Vertical Partitioning
• Optimization Summary

Physical Design And Implementation

• Implementing Relational Data
• Implementing Business Analytics
• Implementing Olap

Module Five: Summary And Conclusion

Appendices

• Appendix A - Entity-Relationship Modeling Basics
• Relational Data Design
• Introduction To Entity/Relationship Modeling
• E/R Model Components
• Entities And Attributes
• Relationships
• Subtypes And Supertypes
• Reading E/R Models: E/R Models For Communication

Appendix B – Case Study

Appendix C – Exercises

• Exercise One – Business Domains
• Exercise Two – Business Subjects
• Exercise Three – Fact Qualifier Matrix
• Exercise Four – Fact Qualifier Matrix Refinement
• Exercise Five – Logical Dimensional Model
• Exercise Six – Star Schema