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 Logical and Physical Data Modeling. 

Also available in eLearning at firstplacelearning.com.

At this seminar you will learn the secrets of successful logical and physical data modeling. You will gain valuable insights into the job and responsibilities of the data modeler as well as the responsibilities of other project contributors. This seminar helps to bridge the gap between data modelers and database administrators (DBAs). 

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 logical and physical 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. 

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. 

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 and transition those models into effective database designs. 

  • HOW TO use data modeling to meet business and performance requirements 
  • HOW TO apply data modeling to client/server projects and data warehouse projects 
  • HOW TO develop logical and physical data models 
  • HOW TO manage and coordinate large data models 
  • HOW TO lead your team through the data modeling process 
  • HOW TO effectively translate data models into into databases 
  • HOW TO avoid data modeling traps, problems, and time wasters 
  • HOW TO improve database performance through data modeling 
  • HOW TO make databases easier to use through data modeling 
  • HOW TO select data modeling software 
  • HOW TO effectively communicate data models and database designs to others 

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.  

  • Business Analysts
  • Data Architects
  • Data Modelers
  • Data Modeling Team Members
  • Information Technology Managers
  • Database Administrators
  • Database Developers

    I. Data Modeling Concepts  
    • What is Data Modeling?
    • Where does Data Modeling fit into Information Technology Management?
    • Information Architecture
    • Benefits and Objectives of Data Modeling
    • Three Schema Architecture
    II. Conceptual Data Modeling Review  
    • Preparing for Logical and Physical Modeling
    • Entities
    • Relationships
    • Attributes
    • Supertypes and Subtypes
    • Derived Data
    III. Defining The Data  
    • Discovering and Identifying Entities
    • Naming Data Elements
    • Standardizing Data Elements
    • Re-using and Sharing Data Elements
    • Business Definitions
    • Using The Data Dictionary
    IV. Normalizing The Data  
    • Normalizing Data
    • Handling Derived Data
    • Identifying Keys
    • Relating the Entities
    • Reviewing and Validating the Model
    • Data Warehousing vs Transaction Processing Requirements
    V. Physical Data Modeling and Database Administration Tools   
    • Physical Data Modeling Tools
    • DBA Tools
    • Data Re-engineering Tools
    • Evaluating Tools
    • Managing Tools and Metadata
    VI. Developing the First Cut Physical Design   
    • Physical Data Modeling Steps 
    • Entity Transformation 
    • Relationship Transformation 
    • Normalization and De-Normalization 
    • Defining Primary and Foreign Keys 
    VII. Tuning the Physical Design   
    • Improving Ease of Use
    • Prototyping
    • Ad Hoc Queries
    • Transaction Modeling
    VIII. Database Advanced Considerations  
    • Indexing
    • Relational Views
    • Stored Procedures
    • Triggers
    • Distribution of Data
    • Client/Server Considerations
    • Two Tier and Three Tier Architectures
    • Replication of Data
    • Security
    • Design Tradeoffs
    • Standards and Controls
  • The Analytical Puzzle

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