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 Data Resource Management. 

The Data Resource Management Seminar will give you an understandable picture of the approach and an appreciation of the benefits that it holds for your organization. You will learn what makes data resource management different from traditional data processing / information systems. Step by step methods for planning and controlling data resource management projects will be presented which will greatly increase the probability of success. We believe that involving the right people (sponsor, business area experts, project leader, and technical people) is a critical success factor.

The seminar will provide insights to data resource management questions including:

  • What are the functions and benefits of Data Resource Management?
  • How do we assure that the result is what the business really needs?
  • How can resources and time be accurately estimated?
  • How can risks be quantified and reduced?
  • How can scope, objectives, and requirements be defined?
  • What are the major mistakes to avoid?
  • How can the function be tracked and controlled?

    Data Resource Management must be supported by a technical architecture, the pieces that must fit together to support goals. Students will learn about each major component and how to make successful choices in putting those components together into a data resource.

    The seminar will address design and implementation questions including:

  • What components are needed to implement Data Resource Management?
  • What are the trade offs between risk and benefit?
  • How should data resources be modeled and organized?
  • What is metadata and where should it come from?
  • How can we grow the data resource?
  • How can we document the data resource?

    This seminar includes an effective mix of exercises and presentations. Our Tricks and Traps sections will show you how to avoid problems and make good choices. In addition, you will learn how to select and use data resource management components.

    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.  

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

    I. Overview of Data Resource Management  
    • What is Data Resource Management?
    • History
    • Problems
    • Solutions
    • Mission and Goals
    • How Data Resource Management Contributes to the Bottom Line
    II. Managing the Data Asset Portfolio  
    • Determining Data Asset Valuation
    • Inventorying
    • Planning the Portfolio
    • Reducing Duplication and Carrying Cost
    • Increasing Quality
    • Extending Productive Life
    • Leveraging the Investment
    III. Improved Customer Information  
    • Lifetime Customer Value
    • Integrated Customer Information
    • Improved Customer Service
    • Increased Sales
    • Improved Decision Making
    IV. Managing Data Resource Management  
    • Information Technology Planning
    • Organizing the Data Resource Management Function
    • Roles: steward, trustee, custodian, subscriber
    • CIO : Chief Information Officer
    • Data Architect
    • Data Standization Analyst
    • Data Modeler
    V. Aligning Data Modeling with Enterprise Goals  
    • Enterprise and Conceptual Modeling
    • Information Architecture
    • Developing Multi-level Models
    • Subject Area based Models
    VI. Data Dictionaries and Repositories  
    • Metadata Management
    • Data Dictionary Administration
    • Metadata Loading
    • Metadata Publishing
    VII. Data Element Naming and Standardization  
    • Discovering and Identifying Data Elements
    • Naming
    • Business Names
    • Technical Names
    • Class Words
    • Prime Words
    • Modifier Words
    • Standardizing
    • Re-using Data Elements
    • Business Definitions
    • Identifying Business Rules
    VIII. Logical Data Modeling  
    • What is Data Modeling?
    • Determining the Level of Detail (Granularity)
    • Discovering and Identifying Entities
    • Normalizing Data
    • Handling Derived Data
    • Identifying Keys
    • Relating the Entities
    • Reviewing and Validating the Model
    IX. Data Reengineering Projects  
    • Consolidating Data Elements
    • Improving Data Organization
    • Improving Data Quality
    X. Subject Area Databases  
    • Sharing Data
    • Avoiding Duplication
    • Improving Coordination Between Groups
    • The Enterprise Data Model
    XI. Data Warehousing and Decision Support  
    • What is Data Warehousing?
    • Benefits of Data Warehousing
    • How Data Resource Management Contributes to Data Warehousing
    XII. Additional Data Resources  
    • Code Sets
    • Units of Measure
    • Parameters
    • Business Rules
    • Locations
    • Organizations
    • Applications
  • The Analytical Puzzle

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