David Haertzen

Business Process Engineering
Business Process Modeling
Business Process Engineering

Data Managmenent
Conceptual Data Modeling
Logical/Physical Data Modeling
Data Resource Management
Rapid Modeling with Teams
Modeling Fast Start

Data Warehousing
Dimensional Modeling
Extract Transform Load (ETL)

Project Management


Call: 612-281-2998


"Excellent presentation -  very crisp and effective ..." 

"The seminar was very practical and addressed our needs ..." 

"I came away with some new ideas and ways of thinking ..." 

The intensive two-day course that teaches you the secrets
of successful Conceptual Data Modeling. 

Also available in eLearning at firstplacelearning.com.

At this seminar you will learn the secrets of successful data modeling. You will gain valuable insights into the job and responsibilities of the data modeler as well as the responsibilities of other data modeling contributors. 

Conceptual Data Modeling also known as Entity/Relationship (E-R) Modeling is a key method for getting a handle on the data requirements of an organization. Effective E-R modeling results in maximum benefits from information assets by increasing shared use and avoiding redundancy. Data that is relevant, timely, consistent, and accessible has increased value to the organization. 

This seminar includes an effective mix of exercises and presentations. Each step of the way the student learns through doing. Three levels of data modeling are included: conceptual, logical, and physical. The focus is on conceptual data modeling. Students will learn about data element analysis, standardization, naming, and normalization. They will learn how to create a single model that supports multiple user views. In addition, they will learn how to select and use modeling tools. 

There are proven approaches, methodologies, templates, and checklists which you will learn about that can dramatically increase the data modeling success rate. You will learn through many effective hands on workshops and case studies. Our trainers know how to create effective data models. 

  • HOW TO use data modeling to capture business requirements
  • HOW TO apply data modeling to client/server projects and data warehouse projects
  • HOW TO develop enterprise level data models
  • HOW TO develop conceptual data models
  • HOW TO manage and coordinate large data models
  • HOW TO lead your team through the data modeling process
  • HOW TO create data models that translate effectively into databases
  • HOW TO avoid data modeling traps, problems, and time wasters
  • HOW TO reuse data models
  • HOW TO select data modeling software
  • HOW TO effectively communicate data models to others
  • HOW TO name and define data using a data dictionary /repository

You will receive a comprehensive manual and Data Modeling toolkit that provides useful checklists, examples and reference material that will help you in your data modeling efforts. The usefulness of the course continues long after the class sessions.  

You will receive a Certificate of Completion upon successful completion of the course.  

  • Database Administrators
  • Data Modelers
  • Data Administrators
  • Information Technology Managers
  • Project Managers
  • Project Team Members

    I. Introduction to Conceptual Data Modeling  
    • What is Data Modeling?
    • Where does Data Modeling fit into Information Technology Management?
    • Information Architecture
    • Benefits and Objectives of Data Modeling
    II. II. Key Conceptual Data Modeling Ideas  
    • Three Schema Architecture
    • Entities
    • Relationships
    • Attributes
    • Supertypes and Subtypes
    III. Planning, Organizing, and Staffing for Effective Data Modeling   
    • Information Technology Planning
    • Organizing the Modeling Function
    • Staffing the Modeling Function
    • Conducting a Successful Pilot Project
    • How the Data Modeler Can Contribute to Successful Projects
    IV. Aligning Data Modeling with Enterprise Goals  
    • Enterprise and Conceptual Modeling
    • Information Architecture
    • Developing Multi-level Models
    • Subject Area based Models
    V. Data Modeling Rules and Guidelines  
    • Determining the Level of Detail (Granularity)
    • Discovering and Identifying Entities
    • Naming, Standardization, and Re-use
    • Business Definitions
    • Normalizing Data (First Normal through Fifth Normal)
    • Handling Derived Data
    • Identifying Keys
    • Relating the Entities
    • Identifying Business Rules
    • Reviewing and Validating the Model
    VI. Data Modeling Advanced Considerations  
    • Managing Large Models
    • Creating Views of Large Models
    • Consolidating Smaller Models into Larger Models
    • Coordinating Application Level Models with Enterprise Level Models
    • Creating the Requirements Document
    • Creating Reports
    • Presenting the Data Model
    VII. Data Modeling Tools  
    • Data Dictionaries and Repositories
    • CASE Tools
    • Re-engineering Tools
    • Evaluating Tools
    • Managed Tools and Metadata
  • The Analytical Puzzle

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