Welcome to Debtech International


Onsite Seminar

Data Modeling Master Class


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.

This is a pragmatic workshop. There are many exercises and one continuous case study. All the exercises have been derived from real modeling situations.

This course offers comprehensive coverage of mainstream data modeling concepts. You learn a rigorous method for defining data. You also learn how to gather the data, define and analyze business rules, perform normalization, and use the results to create a stable model of the data within a business area. You learn state-of- the-art refinement techniques like subtyping and recursive relationships. Above all, you learn how to do data modeling rapidly.

Objectives
Upon completion of this course attendees will:

  • Be able to spot various advanced situations as they arise in their own modeling assignments, and deal with them efficiently and effectively
  • Know how to apply leading edge data modeling principles to practical and difficult modeling situations.

Prerequisite
Experience with logical data modeling or formal training in logical data modeling

Course Outline

Data Pre-assessment (optional)

  • Presentation of the pre-assessment
  • Scoring of the pre-assessment
  • Evaluation of audience skills

Introduction and Overview

  • 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
  • EXERCISE:  High level data modeling

Metadata

  • What it is
  • Who uses it
  • Creating good definitions
  • Storing and retrieving metadata
  • EXERCISE: Metadata

Detailed Data Modeling

  • Model expansion
  • Detailed modeling constructs
  • Methods of Model Expansion
  • Types of Data
  • Types of Keys
  • Types of Entities
  • EXERCISE:  Model expansion

Normalization

  • What normalization is
  • What normalization is not
  • Rules and steps of normalization
  • Practical tips for normalization
  • EXERCISE: Mini-exercise
  • EXERCISE: Case study

Business Rules

  • What are business rules?
  • Types of business rules
  • Placement of business rules in data and process models
  • How to document business rules
  • Examples of business rules

Information Gathering

  • Methods for Information Gathering
  • Brainstorming
  • Interviews
  • Group sessions
  • Asking the right questions

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

Current Systems Analysis

  • Reasons for doing current systems analysis
  • Analyzing current data
  • Reverse Engineering Data Models
  • 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

Advanced Data Model Topics

  • Abstraction:  generalization and aggregation
  • Generalization
  • Subtyping
  • Aggregation
  • Bill of materials
  • Generic modeling and type coding
  • Name-value pairs
  • Shadow tables
  • Derived data
  • Modeling changeable data
  • Modeling goals
  • Modeling time
  • Final model stabilization
  • EXERCISE: Model refinement in case study

Modeling Time and History

  • Definition of time, history and time series
  • Types of history
  • Designing for different history requirements
  • History vs. auditing

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

Data Modeling for the Data Warehouse

  • What is a data warehouse?
  • Differences between operational and informational data
  • The Gather-Store-Deliver model
  • What does a data warehouse look like?
  • On-line transaction processing (OLTP) vs. on-line analytical processing (OLAP)
  • The operational data model
  • The warehouse data model
  • The dimensional model
  • Data mart modeling
  • Summary of differences between OLTP and DW

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

  • Success factors in implementing data modeling
  • General review

Glossary Of Terms Advanced and Optional Topics Data Post-assessment (optional)

  • Presentation of the post-assessment
  • Scoring of the post-assessment
  • Evaluation of enhanced audience skills
  • Recommendations

Duration
4-5 days

Course Format
Lecture, group discussions and exercises

Instructor
Tom Haughey

To request a quote for this in-house seminar
Please call (561) 218-4752 or email info@debtechint.com

Return to Onsite Seminars Table of Contents