Communication, Consistency, and Complexity

Overview:

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 modelling classes.  This intense, participative workshop provides approaches for many advanced data modelling 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, two-day workshop – specific, repeatable techniques that you can use to drive your data modelling skills to the next level.

Three main themes will be explored:

1. The technical side of data modelling – getting better at modelling difficult, complex situations
2. Developing and using data models in new ways, and in conjunction with other techniques
3. The human side of data modelling – improving processes and communication skills

Topics will be covered with a discussion of the issue, a review of techniques, guidelines and examples, a brief workshop exercise, and a group solution and debriefing. 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 modelling professionals have continually rated as the most concrete and useful.

Instructor – Alec Sharp:

With almost 35 years of consulting experience, Alec has provided hands-on data modelling expertise throughout North America, Asia, Europe, and Australasia –  this workshop is based on real-world experience, not textbook theory. Alec has also delivered hundreds of Data Modelling and Advanced Data Modelling workshops, and top-rated presentations at international conferences, including “The Seven Deadly Sins of Data Modelling,” “Data Modelling – New Uses for New Times,” “The Lost Art of Conceptual Modelling,” “Getting Traction for Data Modelling – Winning Over the Masses,” and “The Human Side of Data Modelling.” Alec is the author of “Workflow Modelling, Second Edition” (Artech House, 2009) which is a consistent best-seller in the field, and is widely used as an MBA text and consulting guide.

Target Audience:

Specialist data modelers, data architects, and DBAs who wish to hone their skills. Also business analysts, application developers, and anyone else with substantial data modelling experience who needs additional skills.

• 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
• Modelling time – special considerations for recording past, present, and future values
• Seven questions you should always ask when a date range appears
• Risk and compliance – why we need “as-of reporting” and how to model data corrections

• Modelling 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 modelling
• Associative entities – circular relationships, shared parentage, and other issues
• Alternatives for modelling constraints across relationships
• Advanced normal forms – how to quickly recognize potential 4NF and 5NF issues

• 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 modelling
• How to start a large project with a contextual data model
• Guidelines for staying at the conceptual level, and how to tell when you’ve gone too far

• Bridging the “E-R vs. Dimensional” divide – the world’s shortest course on dimensional modelling
• The perils of dimensional modelling 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

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