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

  • Learn via: Classroom / Virtual Classroom / Online
  • Duration: 2 Days
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  • We can host this training at your preferred location. Contact us!

Business intelligence and data warehousing systems challenge the proven data modeling techniques of the past. From requirements to implementation, new roles, uses, and kinds of data demand updated modeling skills. The data modeler’s toolbox must address relational data, dimensional data, unstructured data, and master data. For those with data modeling experience, this course extends their skills to meet today’s modeling challenges. Those new to data modeling are introduced to the broad range of modeling skills needed for BI/DW systems. Those who need to understand data models, but not necessarily develop them, will learn about the various forms of models and what they are intended to communicate.

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


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