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CONCEPTUAL DATA MODELING:

Designing the Information Resource
From the Business Perspective

Why this Course?

In the information age, the success of an organization will be driven by its effective use of its information resource. Characteristics of an effective information resource include flexibility to allow changes to the business processes, stability to allow new applications, and sharability to maximize the value of the information and reduce information float.

This seminar provides an integrated approach to data modeling that leads to flexible, stable and sharable database designs. This balanced and pragmatic approach enables you to maximize data sharing, minimize data redundancy, and make a positive impact on the effectiveness and bottom line of your organization.

Learning Outcomes:

Upon completion of this seminar, you will be able to:
  • Describe what conceptual data modeling is and why it is required
  • List the steps in the data modeling process and describe how they modify the application development life cycle
  • Develop a business-driven entity-relationship model that reflects the enterprise view of its data
  • Describe how to analyze the correct relationships between entities
  • Name and define entity types and attributes clearly
  • Describe and apply normalization from a business viewpoint
  • Develop information views to validate the stability of the conceptual data model
  • Describe the components of effective data model workshops
  • Describe how to conduct accelerated data modeling workshops
  • Describe the roles and responsibilities of the organization's personnel in data modeling
  • List the critical success factors in effective data modeling
Audience: Data Administrators, Database Administrators, Data Analysts and Architects, Database Designers, Business Analysts, Systems Analysts and Designers involved in data modeling
Format: Lecture with numerous exercises and case study
Duration:
 3-4 Days
Pre-requisites: Basic understanding of data processing or business

 

 

Course Outline:
1. What Data Modeling Really Is
  • Data model as business model
  • Data model as mind map and abstraction of real world objects
  • Data model as architecture
  • Data modeling objectives

    •
    2. Data Modeling Concepts
  • Entities, attributes and relationships
  • Static and dynamic entity types
  • Identifier, descriptive and relationship attribute types
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    3. Information Architecture: Strategic Planning and Data Modeling
  • Enterprise-wide information architecture
  • The business view of information
  • Subject areas as business resources
  • Developing a subject approach to information modeling


    • 4.  Conceptual Data Modeling: An Integrated Approach
  • The resource life cycle and the data development life cycle (DDLC)
  • Subject areas: a business resource approach to data modeling
  • Deriving a detailed data model
  • Fundamental, associative and attributive entity types
  • Semantic data modeling


    • 5.  Normalization: Principles Restated from a Business Perspective
  • A pragmatic approach to normalization
  • Data integrity through data structure

    •  

    6.  Data Analysis: the Key to Understanding Data Requirements

  • The meaning of data
  • Discovering and modeling business rules
  • Verifying the placement of data within the model
  • A semantic approach to data analysis
    •


    7. Case Study

    8. Data Modeling Guidelines: Handling Special Cases
  • Primary key structure for stability
  • Recursive relationships
  • Modeling entity types and subtypes
  • Generic (metadata) entity types
    •

    9.     Model Analysis: Assuring stability
  • Stability and flexibility analysis
  • Supporting multiple business views
  • Data model walkthroughs
  • Reconciling the data model

    •

    10.  The RADD (RApid Data Development) Workshop
  • Expediting data modeling
  • Workshop objectives, participants, and tasks
  • Techniques for effective workshops
    •


    11.   Integrated Data Modeling: Keys to Success
  • How to succeed and avoid pitfalls
  • Gaining and sustaining management commitment
  • Enabling the information age through data modeling
    •

     

     

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