Dimensional Modeling

Advanced Data Modeling
Alec Sharp
April 14-16, 2008, Orlando, Fl

After gaining some practical experience, data modelers encounter situations such as the enforcement of complex business rules, handling recurring patterns, dealing with existing databases or packaged applications, and other issues not covered in introductory data modeling classes. This intense, participative workshop provides approaches for many advanced data modeling situations, as well as techniques for improving communication between data modelers, business analysts, designer/developers, and subject matter experts.

Description:
There are experienced data modelers out there who somehow develop accurate and stable models that are actually used, often in non-typical or high-pressure situations. They get the job done without wasted effort, maintain the involvement and respect of the subject matter experts, and - worst of all! - make it look easy. Others modelers might have great technical skills, but fare poorly, maintaining tense relationships with content experts and developers who "just don't get it," and watching in dismay as their models are continually undone by "new" requirements.

What accounts for the difference? Magic? Luck? Better tools? No - it's having a concrete set of frameworks, methods, techniques, scripts, heuristics, and other tools that they draw on to keep the process moving, with everyone engaged, even when complex, difficult situations are encountered. And that's what we'll cover in this full, but fun, three-day workshop - specific, repeatable techniques that you can use to drive your data modeling skills to the next level.

Three main themes will be explored:
1. The technical side of data modeling - getting better at modeling difficult, complex situations
2. Developing and using data models in new ways, and in conjunction with other techniques
3. The human side of data modeling - improving processes and communication skills

Topics will be covered with a discussion of the issue, a review of techniques, guidelines and examples and workshop exercises. The emphasis is on maximizing the delivery of content while keeping everyone engaged - the workshop has recently been extensively redesigned to focus on the topics that data modeling professionals have continually rated as the most concrete and useful.

Course Topics:
A quick recap – level-setting on terms, concepts, conventions, and structures
• Conventions for the essential components: entities, relationships, attributes, and identifiers
• Effective naming and definition
• E-R Diagramming – symbol sets and their problems, rules for readability and comprehension
• Types of data models - contextual, conceptual, logical, and physical
• The four Ds of data modeling – definition, dependency, detail, and demonstration

Working with higher-level models
• Contextual, conceptual, logical models – what they are, who they’re for, when we need them
• Definitions for each type of model, and common sources of confusion
• How the different kinds of data models relate to process, use case, and service models
• Avoiding the “deep dive into detail” – a three-phase method for data modeling
• How to start a large project with a contextual data model
• Why conceptual modeling has become a “lost art” and its critical role in communication
• Guidelines for staying at the conceptual level, and how to tell when you’ve gone too far

Modeling time, history, and time-dependent business rules
• Historical vs. audit data, and when to show them on a data model
• “Do you need history?” – how to tell when your client is misleading you
• Four variations on capturing history in a data model
• Modeling time – special considerations for recording past, present, and future values
• Six questions you should always ask when a date range appears
• Thanks, Sarbanes-Oxley! Why we need “as-of reporting” and how to model data corrections
• Modeling in a global, 24x7 world

The lowly attribute
• The basic patterns – handling multi-valued, redundant, and constrained attributes
• Granularity – dealing with non-atomic and semantically overloaded attributes
• Modeling derived, complex, and optional attributes
• Dealing with reference data and the “classification vs. specification vs. instances” problem
• Three attributes that always need a qualifier
• Vector modeling – entity or attribute?
• The well-traveled attribute – dealing with international issues

Modeling rules on relationships and associations
• Using multi-way associations to handle complex rules
• “Use your words” – how assertions, scenarios, and other techniques will improve your modeling
• Questions to ask when you have
• Alternatives for modeling constraints across relationships
• Advanced normal forms – how to quickly recognize potential 4NF and 5NF issues
• A simpler view – why the five normal forms could be reduced to three

Interesting structures – generalization, recursion, and the two together
• Generalization (subtyping) – when to use it, and when not to
• Generalization with and without specification
• Options for implemention of subtypes
• Guidelines for using recursive relationships
• Generalization and recursion working hand-in-hand as a cure for literalism
• Recognizing lists, trees, and networks, and modeling them with recursive relationships
• Modeling difficult rules by combining generalization (subtyping) and recursion
• Staying clear on generalization vs. roles, states, and aggregation

Bridging the "E-R vs. Dimensional" divide – the world’s shortest course on dimensional modeling
• The perils of dimensional modeling without understanding the underlying E-R model
• Spotting facts and dimensions – the relationship between dimensional models and E-R models
• Saving time – building a first-cut dimensional model from an ER model

Better models through using data modeling in conjunction with other techniques
• Things, events, services, use cases, and processes – how they fit together and synergize
• The Weasel’s Guide to doing data modeling without anyone knowing it
• Event analysis as a rapid way to gather requirements
• Use Cases and Service Specifications, and their role in data modeling
• Process Modeling, and the vital role data models play
• Pulling it together and nailing down rules by using State Transition Diagramming
• Issues and approaches for modeling entity state history

Interesting approaches and uses
• Developing a first-cut data model from business artifacts (forms, reports, screens, etc.)
• Living with legacy – the role of reverse-engineering and data profiling
• “Shock and dismay” – showing the business their current data model, and what it’s doing to them
• Where and how data modeling fits into selecting and implementing packaged applications
• The role of generic data models

Effectiveness skills for data modelers – communication, facilitation, presentation and consistency
• Preparing and delivering a data model review presentation
• Facilitation techniques specifically for the data modeler
• “The Magical Number Seven” and what it has to do with modeling
• Repeatable methods for discovering, assessing, and meeting new requirements
• A consistent approach – “scripts” to use while building a data model
• “Challenges” to use when validating a data model
• “Future-proofing” – what you can do to improve the lifespan of your model
• Seven techniques for “humanizing” data modeling and making data models more accessible

Alec Sharp
With over 25 years of consulting experience, Alec has provided hands-on data modeling expertise throughout North America, Asia, and Europe - this workshop is based on real-world experience, not textbook theory. Alec has also delivered hundreds of Data Modeling and Advanced Data Modeling workshops, and top-rated presentations at international conferences, including "The Seven Deadly Sins of Data Modeling," "Data Modeling - New Uses for New Times," "The Lost Art of Conceptual Modeling," "Getting Traction for Data Modeling - Winning Over the Masses," and "The Human Side of Data Modeling." Alec is the principal author of "Workflow Modeling" (Artech House, 2001) which is a consistent best-seller in the field, and is widely used as an MBA text and consulting guide.

Tuition
$1695

EARLY BIRD DISCOUNT:
SAVE $100 if paid by March 14, 2008
Additional $100 discount for members of DAMA, MPO, IDMA, and ERwin User Groups

Group discounts are available

For registration please complete the registration form

To register by phone call (973) 379-7212
For additional information please email davida@debtechint.com

Seminar Location and Hotel Accommodations
Courtyard by Marriott Lake Buena Vista at Vista Centre
8501 Palm Parkway
Lake Buena Vista, FL, 32830

Seminar attendees are eligible for a discounted hotel rate of $129 per night.

This rate is available until March 25 or when the hotel block sells out.

Please call 1 866 790-2187 and mention Advanced Data Modeling in order to obtain the discount rate.