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COURSE Title:
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DEVELOPING HIGH QUALITY DATA MODELS:
Applying Quality Principles to Information Architecture |
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Why This Course?
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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.
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Learning Outcomes:
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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
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Audience:
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Data Administrators, Database Administrators, Data Analysts and Architects, Database Designers, Business Analysts, Systems Analysts and Designers involved in data modeling; Quality Assurance staff
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Format:
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Lecture with numerous exercises and case study
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Duration:
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3-4 Days
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Pre-requisites:
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Basic understanding of data processing or business. This is a basic data modeling course using quality principles to design quality in.
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Abstract:
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ABSTRACT: 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
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What Data Modeling Is
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- Data model as business model
- Data model as mind map and abstraction of real world objects
- Data model as architecture
- Quality principles for data modeling
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Data Modeling Concepts
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- Entities, attributes and relationships
- Static and dynamic entity types
- Identifier, descriptive and relationship attribute types
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Information Architecture: Strategic Planning and Data Modeling
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- Enterprise-wide information architecture
- Zachman?s Enterprise Architecture Framework
- Business resources and subject areas
- Developing a subject approach to information modeling
- Information Architecture principles
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Conceptual Data Modeling: An Integrated Approach
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- 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
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Normalization and Data Integrity
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- A non-technical approach to normalization
- Data integrity through data structure
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Data Analysis: Understanding
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- Discovering the meaning of data
- Discovering and modeling business rules
- Verifying the placement of data within the model
- Information architecture quality
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Data Modeling Guidelines: Handling Special Cases
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- Identifier integrity
- Recursive relationships
- Modeling entity types and subtypes
- Generic (metadata) entity types
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Model Analysis: Assuring stability
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- Quality assurance before physical design
- Stability and flexibility analysis
- Supporting multiple business views
- Data model walkthroughs
- Reconciling the data model
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Quality Function Deployment and Data model development
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- Customer-driven data modeling
- QFD objectives, participants, and tasks
- Techniques for effective workshops
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