Advanced Data Modeling:

Communication, Consistency, and Complexity ~ 3 days


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:

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



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.


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


Target Audience:

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

Course Outline / Topics:

  • Recapping the basics: conventions, basic structures, and “the four Ds of data modeling”
  • Dealing with reference data and the “category vs. types vs. instances” problem
  • Vector modeling – entity or attribute?
  • Using multi-way associations and relationship constraints to handle complex rules
  • Advanced normal forms - resolving circular relationships and cyclic dependencies
  • Modeling time, history, corrections, and time-dependent business rules
  • Analytic data structures – building star schema or dimensional models from ER models
  • Roles, generalization (subtyping,) and aggregation – when to use them, and when not to
  • Implementing lists, trees, and networks with recursive relationships:
  • Modeling difficult rules by combining subtyping and recursion
  • Preparing and delivering a data model review presentation


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