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Data Modeling Principle and Practices
Data Analysis for Enterprise & Data Warehouse Applications

Data Modeling Principles and Practices teaches how to gather business information needs and business rules and how to create Logical Data Models using visual Entity/Relationship (E/R) Diagrams.  In this intensive and highly practical 2-day workshop, participants learn approaches for developing logical data models that define and capture enterprise information requirements…the information structures, rules and relationships that enable enterprise operation.

Data Modeling Principles and Practices shows participants pragmatic methods for applying logical data models to strategic planning and architecture development, application development, re-engineering of applications, database design, and systems integration.  Further, the workshop teaches how to utilize data models to communicate effectively with non-technical people about data requirements.

Data Modeling Principles and Practices uses a comprehensive case study that closely simulates actual work experience to give learners realistic practice in applying the techniques they learn.  Working in teams, participants join forces to discover and define data subjects, the relationships between them, and the metadata that describes them.  The case study also teaches learners how to test their models for validity, correctness, and completeness.

Data Modeling Principles and Practices demonstrates techniques for transforming logical data models into database designs.  Participants learn how to gather the information required to develop an effective and efficient database structure - a vital factor for working effectively with Systems Architects, Data and Database Administrators.

What You Will Learn

• Effective approaches for specifying information requirements

  • Business transaction information needs

  • Management reporting needs

  • Decision Support information needs

• Practical and effective Data Modeling

  • Data gathering

  • Developing the ER Diagram and definitions

  • Model refinement (including Normalization)

  • Testing the Data Model

• Managing Enterprise Data Models

  • Using Subject Areas to simplify complex models

  • Merging Data Models

• How to use Data Models in developing:

  • Data Architecture

  • Data Base Design

  • Data Segmentation & Distribution Planning

  • Decision Support/Executive Information Systems

  • Data Warehouses & Data Marts

  • Portals

Who Should Attend

• Business Analysts

• Systems Designer & Developers

• I/S & Business Managers sponsoring Information Intensive projects, including:

  • Large Databases

  • Data Warehousing

  • Decision Support

  • Distributed Database

Seminar Outline

• Why Model Data?

• Data Model Overview

  • Definitions & Components

  • Semantic Interpretation

  • Comparative Methodologies

• Scope Definition

  • Setting bounds on a data modeling project

• Enterprise Modeling

  • Enterprise Modeling Issues

  • Subject Area Analysis

• Business Information Modeling

  • Entity Analysis - Defining Business Subjects

  • Relationship Analysis - Capturing Business Rules

  • Common Relationship Patterns & Anomalies

  • Entity Attribution

• Refining the Data Model

  • Entity Type Identifiers

  • Data Model Normalization

  • Sub Types and Partitioning

• Integrating Data Models with Process Models

  • Data Usage Mapping

  • Entity Life-Cycle Analysis

  • Defining Process Views

  • Event Analysis

• Current Systems Analysis

• Analysis Confirmation

• Transforming the Model into a Design

• Summary Review of Class