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DM 201 – Conceptual Data Modeling – Resisting the Urge to go Physical

Duration: 3 – 5 days

This seminar will demonstrate the problems caused by starting the data modeling process with a physical or logical model, and how following a phased data modeling approach, starting with a Conceptual Data Model (CDM), will result in systems that meet business expectations for quality data and information.

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Description

This seminar will demonstrate the problems caused by starting the data modeling process with a physical or logical model, and how following a phased data modeling approach, starting with a Conceptual Data Model (CDM), will result in systems that meet business expectations for quality data and information.

Conceptual Data Models, as part of the requirements gathering phase, will help set better expectations, identify resource requirements, and reduce costs because of the business understanding the CDM provides to IT and business. When proper entities and relationships are identified at the start of the project, costly mistakes are avoided in downstream models and implemented systems. This course will teach participants how to develop and use CDMs in the context of a phased modeling approach.

This course is workshop-focused since design concepts are best learned through practice. The course workshops are oriented to solving problems in participants’ current projects. These exercises allow participants to learn how the concepts are applied and to develop skills in all phases of modeling for all types of systems.

Objectives

  • Understand what a Conceptual Data Model (CDM) is and how it is used
  • Understand the problems caused by foregoing conceptual data modeling
  • Understand how conceptual data models will improve data and information quality and improve project success
  • Learn how to develop CDM’s
  • Learn how to use CDM’s to understand business requirements and validate understanding with the business

You will:

  • Analyze a real-life example of a problematic data model and understand the impact of not identifying proper relationships and cardinality
  • Learn how a Conceptual Data Model differs from other types of data models
  • Understand best practices for a phased modeling approach
  • Learn a common modeling notation and develop CDM’s for a class exercise in a small team environment
  • Learn how to utilize CDM’s to validate business understanding and to develop downstream logical and physical models
  • Learn about the different types of Conceptual Data Models
  • Learn why an Enterprise Conceptual Data Model is a critical enabler of enterprise-wide endeavors, e.g. data governance and stewardship, ERP, SOA, DW/BI, etc.

Seminar Content

  • Introduction
    • Enterprise Data Modeling as Part of Enterprise Data Management
    • Enterprise Data Modeling Concepts
    • Challenges with starting at Physical Model level
    • Analyzing Conceptual and Logical Data Models
      1. Define what a conceptual data model (CDM) is and is not
      2. Conceptual data models and data/information quality
      3. Top-down, bottom-up methodology for developing conceptual data models
  • Information Engineering Notation Review
    • Entity types
    • Relationship notation
    • Cardinality notation
    • Sub-typing
  • Data Model Types
    • Subject Area Model
    • Types of Conceptual Data Models
      1. Scoping CDM
      2. Business CDM
      3. Application CDM
      4. Enterprise CDM
    • Logical Data Model (LDM)
    • Physical Data Model (PDM)
  • Modeling Topics, Techniques
    • Converting a Conceptual Data Model into a Logical Data Model
    • CDM and LDM nomenclature, including discussion of ISO11179 Part 5
    • Review of ISO11179 Part 4 regarding data definition
    • Metadata and conceptual data models
    • Phased modeling approach
    • Data model presentations for different types of audiences
  • Class Exercises
  • Workshop Summary, Additional Exercises and Reference Materials

About the Course Designer

This training was designed by David Marco, PhD, an internationally recognized authority on data and AI governance, to help teams succeed in real organizational conditions. The curriculum equips participants with practical judgment, shared language, and decision clarity that hold under scale, risk, and executive accountability.

David Marco PHD EWSolutions

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