Days 1 & 2 – Data Modeling Masterclass
The course uses Data Modeling Essentials as a reference, but summarizes some topics which are readily learned directly from the book, allowing more time for discussion of deeper issues of the role of data modeling in today’s organizations, how the professional data modeler can best contribute, and how to work most effectively with business people and other information systems professionals.

Part 1: Key Concepts and Issues

1. Data Modeling in the new world

• The value of data modeling
• Understanding the different types of tools and what they do for you.
• Comparing and contrasting the different vendors and what they have to offer
• Conclusions
• Data modeling in context – what are the boundaries?
• Description or design?
• Understanding differences in models
• Data model quality criteria and trade-offs
• Data modeling in a packaged-software environment

2. The Professional Data Modeler

• The need for specialist data modelers
• Differences between modelers – and why they matter
• Learning from other professions
• Positioning the role
• What data modelers need to know
• Recruiting and training modelers

3. Generalization and its Implications

• Alternative levels of generalization
• Trading stability and representation of rules
• Using subtypes and supertypes effectively
• Table-driven designs
• Implementation options and implications
• Generalization of attributes
• Generalization of relationships
• Using generalization to communicate complex models

• Making the most of the attribute / column distinction
• Common problems with attribute and column definition
• Attribute types and domains
• Using complex attributes
• Handling derivable attributes
• Multi-valued attributes
• Category attributes – a consistent approach
• Dealing with optional attributes and nulls
• Leveraging DBMS extensions

• Criteria for primary keys
• Common design errors
• The concept of identity and its relationship to primary keys
• The importance of stability
• Surrogate keys – when to use
• Weak and regular keys
• Multiple candidate keys

6. Language and Conventions

• Choice of conventions – and the implications
• Extensions to the E-R model
• Using UML – issues and tactics
• Transferability

Part 2: Data Modeling from End-to-End

1. Tasks, Phases and Responsibilities

• Building data modeling into the project plan
• Checklist for project plan review
• Defining boundaries and roles
• Clarifying the stages
• Conceptual, Logical and Physical Modeling
• Selecting and using modeling tools
• Key deliverables
• Managing Change

2. Understanding and verifying user requirements

• Establishing the right relationships
• Strategies for requirements gathering
• Working with Requirements analysts and process modelers
• Effective interviews and workshops
• Capturing requirements using an object hierarchy

3. Conceptual data modeling

• Clarifying expectations and roles
• Conceptual modeling as a design activity
• Getting started
• Using patterns
• Techniques for generating alternatives
• Developing good definitions
• Using subtypes and supertypes
• Dealing with recursive structures
• One-to-one relationships
• Representing categories
• Communicating the model
• Using assertions to verify the model

4. Logical data modeling

• Overview of transformations and decisions
• Implementing subtypes and supertypes
• Implementing attributes
• Primary and Foreign keys – and some tricky situations
• Implementing one-to-one relationships
• Derivable relationships

• Review of responsibilities
• Understanding physical design tools
• Classification of physical design decisions
• The politics and practicalities of compromise

A process for managing physical design

Part 3: Advanced Topics

1. Advanced normalization

• Normalization Revisited
• Normalization myths and misunderstandings
• Recognizing Boyce-Codd Normal Form issues
• Making sense of 4th and 5th Normal Forms
• Limits to normalization

2. The time dimension

• Overview of issues
• Approach to the time dimension
• The “audit trail” approach
• The “snapshot” approach
• Time-related business rules
• Common traps
• Normalization issues – 6th Normal Form

3. Data warehouse and mart structures

• Warehouse and mart architecture
• The modeling challenge
• Design objectives re-visited
• Working with star schemas

4. Business rules

• Business rules and the data modeler
• Types of data rules
• Representing data rules

5. Enterprise modeling and data management

• Roles of the enterprise model
• Traditional approaches to data management
• Tactical approaches – data management “re-invented”
• Lessons of experience

Day 3: Consulting Skills Workshop - Delivering Value
Practical Approaches to Working With the Business

This is a heavily interactive workshop, with time set aside for discussion of case studies and issues raised by attendees.

1. The challenge of consulting
• Why consulting is difficult
• Understanding the client perspective
2. Consulting tools and techniques
• Negotiation
• Selling services – and ideas
• Looking after yourself
3. Assignment initiation – establishing expectations
• Identifying the stakeholders
• Understanding expectations
• What managers want
• Estimating and pricing
• The assignment plan
4. Staying on track – managing expectations
• An effective review process
• An approach to problem management
• Dealing with difficult people
• Acting ethically – and keeping your job
5. A successful conclusion
• Avoiding overruns
• Writing effective reports
• Assignment review
• Ensuring the client stays happy when you’ve gone
• Securing follow-up work
• An approach to problem management