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DEVELOPING HIGH QUALITY DATA MODELS:

Applying Quality Principles to Information Architectur

Why this Course?

In the information age, the success of an organization will be driven by its effective use of its information resource. This seminar does not just describe the mechanics of data modeling, it describes how to design quality in to data models. This seminar applies the quality principles of Kaizen, Quality Function Deployment (QFD) and Deming’s 14 Points of Quality to the process of data and information modeling. Quality characteristics of an effective information architecture (both conceptual and physical) include:

  • Stability to allow new applications without changing the existing architecture—only adding

  • Flexibility to allow changes to the business processes with minimal architecture change

  • Reusability to maximize the value of the information and reduce information float

  • “Consistently meet knowledge workers’ expectations” to represent and house the information they must know to perform their work and accomplish enterprise objectives

This seminar provides an integrated approach to information architecture and data model development that leads to stable, flexible and reusable 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
  • Describe the steps in the data modeling process required to “design quality in” rather than “inspect quality out”
  • Develop a quality entity-relationship model that meets all information customer’s needs
  • Describe how to analyze the correct relationships between entities
  • Determine how to actively engage business people in data modeling
  • Describe how to apply Quality Function Deployment to develop data models
  • Describe and apply normalization from a business viewpoint
  • Develop information views to validate the quality of conceptual data models
  • Describe stewardship accountabilities in data modeling
  • List the critical success factors in effective information architecture
Audience: Data Administrators, Database Administrators, Data Analysts and Architects, Database Designers, Business Analysts, Systems Analysts and Designers involved in data modeling; Quality Assurance staff
Format: Lecture with numerous exercises and case study
Duration:
 3-4 Days
Pre-requisites: Basic understanding of data processing or business. This is a basic data modeling course using quality principles to design quality in.

 

 

Course Outline:

 

1. What Data Modeling Is

  • Data model as business model
  • Data model as mind map and abstraction of real world objects
  • Data model as architecture
  • Data definition as information product specifcation
  • Quality principles for data modeling

 

2. Data Modeling Concepts

  • Entities, attributes and relationships
  • Static and dynamic entity types
  • Identifier, descriptive and relationship attribute types

 

3. Information Architecture: Strategic Planning and Data Modeling

  • Enterprise-wide information architecture
  • Zachman’s Enterprise Architecture Framework
  • Business resources and subject areas
  • Developing a subject approach to information modeling
  • Information Architecture principles

 

4. Conceptual Data Modeling: An Integrated Approach

  • The data development life cycle as a business value chain
  • Business resource approach to data modeling
  • Deriving a detailed data model within an architecture framework
  • Fundamental, associative and attributive entity types
  • Data definition quality

 

5. Normalization and Data Integrity

  • A non-technical approach to normalization
  • Data integrity through data structure

 

 

 

Kaizen: A Japanese word meaning "improvement," including continuous improvement in all aspects of life, personal, social, professional, and in work. In work, kaizen means continuous improvement involving everyone in the organization, both managers and workers.

Quality Function Deployment (QFD): The involvement of customers in the design of products and services for the purpose of better understanding customer requirements, and the subsequent design of products and services that better meet their needs on initial product delivery.

 

6. Data Analysis: Understanding Information Customer Requirements

  • Discovering the meaning of data
  • Discovering and modeling business rules
  • Verifying the placement of data within the model
  • Information architecture quality

 

7. Case Study

8. Data Modeling Guidelines: Handling Special Cases

  • Identifier integrity
  • Recursive relationships
  • Modeling entity types and subtypes
  • Generic (metadata) entity types

 

9. Model Analysis: Assuring stability

  • Quality assurance before physical design
  • Stability and flexibility analysis
  • Supporting multiple business views
  • Data model walkthroughs
  • Reconciling the data model

 

10. Quality Function Deployment and Data model development

  • Customer-driven data modeling
  • QFD 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 and involvement
  • Creating competitive advantage through quality data modeling

 

 

 

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