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Onsite Seminar

Advanced Data Modeling


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 highly participative workshop provides approaches for many difficult data modeling situations, as well as techniques for improving communication between data modelers and subject matter experts. Topics will be covered with a discussion of the issue, a review of guidelines and examples, a workshop exercise, and a group solution and debriefing.

Three main themes will be explored:

  • The technical side of data modeling - getting better at modeling difficult situations
  • The human side of data modeling - improving processes and communication skills
  • Developing and using data models in new ways

Objective
On workshop completion, participants will be able to spot various advanced situations (listed below in “Course Outline/Topics”) as they arise in their own modeling assignments, and deal with them efficiently and effectively.

Prerequisites
Practical experience with data modeling, for instance, Data Modeling and/or six months or more of applying the techniques

Who Should Attend
Business analysts, application developers, data modeling specialists, database administrators, and anyone else with substantial data modeling experience who needs additional skills.

Course Topics

  • Level-setting on terms, concepts, conventions, and structures
    • Conventions for the essential components: entities, relationships, attributes, and identifiers
    • Effective naming and definition
    • Basic attribute patterns – handling multi-valued, redundant, and constrained attributes
    • More attribute patterns – non-atomic, semantically overloaded, and derived attributes
    • Three attributes that always need a qualifier
    • Four entity names to avoid
    • E-R Diagramming – symbol sets and their problems,  rules for readability and comprehension
    • Three types of data models before the physical database – contextual, conceptual, and logical
    • The four Ds of data modeling – definition, dependency, detail, and demonstration
  • Interesting structures
    • Generalization (subtyping) – when to use it, and when not to
    • Generalization with and without specification
    • Guidelines for using recursive relationships
    • Generalization and recursion working hand-in-hand as a cure for literalism and difficult rules
    • Recognizing lists, trees, and networks, and modeling them with recursive relationships
    • Staying clear on generalization vs. roles, states, and aggregation
    • Dealing with reference data, the “types vs. instances” problem, and generic reference structures
    • Vector modeling – entity or attribute?
  • Consistency for data modelers
    • “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
  • Developing data models from non-traditional sources
    • Developing a first-cut data model from business artifacts (forms, reports, screens, etc.)
    • Living with legacy – the role of reverse-engineering and data profiling
    • Developing a conceptual model from the current physical using reverse engineering and simplification
    •  “Shock and dismay” – highlighting features of the current data model, and their impact
  • Preparing and delivering a data model review presentation
    • Context – your audience, and why the model matters to them
    • It’s a story, not a data model!
    • Building a storyboard
    • Five key techniques for presenting data models or other technical subjects
    • The mechanics of the data model review presentation
    • A demonstration
  • 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
    • Seven 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
    • Dealing with time in a global, 24x7 world
  • 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
    • Associative entities – circular relationships, shared parentage, and other issues
    • 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
  • 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
    • 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
  • 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 guerilla 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
    • Where and how data modeling fits into selecting and implementing packaged applications

Duration
3 days

Course Format
Lecture, group discussion and exercises

Instructor
Alec Sharp

To request a quote for this in-house seminar
Please call (561) 218-4752 or email info@debtechint.com

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