The document discusses 10 strategic and detailed pitfalls to avoid in data modeling. Strategic pitfalls include having a vague purpose, modeling literally instead of abstracting, creating large models, including speculative content, and lack of clarity. Detailed pitfalls include recklessly violating normal forms, including needless redundancy, using parallel attributes, symmetric relationships, and anonymous fields. The document emphasizes that data modeling is pivotal to a project's success and avoiding these pitfalls can improve data quality, extensibility, and performance.

1. Ten Things to Avoid in a Data Model
Dr. Michael Blaha
Modelsoft Consulting Corp
www.modelsoftcorp.com
E-mail: blaha@computer.org

2. Introduction
• A model is an abstraction of some aspect of a problem.
• A data model is a model that describes how data is represented
and accessed, usually for a database.
– Data modeling can be a difficult task and is often pivotal to the success or failure of
a project.
• There are many pitfalls to data modeling as we will explain...
– Strategic pitfalls.
– Detailed pitfalls.
• We do not discuss detailed modeling constructs such as keys, data
types, nullability, and referential integrity.
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3. Strategic Pitfalls…
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4. Strategic Pitfall: Vague Purpose
• Don’t build a model without understanding the business rationale.
• The purpose for a model dictates the level of detail.
– Just entities and relationships.
– Fully attributed.
– With data types and constraints.
• The purpose also dictates the level of polish, the degree of completeness, and
the amount of time justified.
• Different kinds of data models.
– Detailed application model for development.
– Rough application for a purchase spec.
– Enterprise model for integration.
• This pitfall might seem obvious, but I’ve seen modeling efforts with little
business purpose and no clear definition of deliverables.
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5. Strategic Pitfall: Literal Modeling
• Your job is not to do what the customer says. Your job is to solve
the problem that the customer is imperfectly describing.
• You must pay attention to the hidden true requirements.
• You must interpret and abstract what the customer tells you.
– You must recognize arbitrary business decisions that could easily change.
• You can raise abstraction by thinking in terms of patterns.
• The use case mentality really misses this point.
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6. Strategic Pitfall: Literal Modeling Example
Original
literal
model
Improved
abstract
model
• The original model is correct, but has problems. What happens if a person gets
promoted to a supervisor and then to a manager? Are there multiple records?
Movement of a record? Or???
• The improved model is more abstract and softcodes the management
hierarchy.
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7. Strategic Pitfall: Large Size
• Avoid large models. Limit a model to no more than 200 tables.
• Large models involve more work.
• Is the large size really justified or can you simplify the model with
abstraction?
• I rarely encounter a large model with a compelling justification.
• I don’t see this step in software development methodologies, but
it is certainly needed.
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8. Strategic Pitfall: Speculative Content
• Do not include content that is not needed now and “might be
helpful” in the future..
• All this does is to make a model larger, increase development
time, and raise cost.
• A model must fully address the requirements, but not greatly
exceed them.
• A quality model should be readily extended, so there is no need to
add content in advance of need.
• Speculative content runs counter to the philosophy of agile
development.
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9. Strategic Pitfall: Lack of Clarity
• A relational database is declarative. Declare data in your
models.
• A domain is the set of possible values for an attribute.
– ERwin lets you define domains and then assign them to the pertinent attributes.
• An enumeration is a domain that has a finite set of values.
– Declare enumerations in your databases.
• Don’t store data structures with a binary encoding.
• Don’t use cryptic names.
• Don’t use anonymous fields that application code must interpret.
• Obfuscation can happen through sloppy development practices.
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10. Strategic Pitfall: Lack of Clarity Example
Car table
Enumeration carID year color weight
stored in 1 2001 red 2000
place 2 1989 red 1500
3 2000 blue 2500
Car table Color table
Enumeration carID year colorID weight colorID color
stored 1 2001 1 2000 1 red
separately 2 1989 1 1500 2 green
3 2000 3 2500 3 blue
Car table
carID year color weight
Enumeration
1 2001 1 2000
encoded
2 1989 1 1500
3 2000 3 2500
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11. Detailed Pitfalls…
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12. Detailed Pitfall: Reckless Violation of Normal Forms
• Do not accidentally violate normal forms.
• A normal form is a guideline that increases data consistency.
• As tables satisfy higher levels of normal forms, they are less likely
to store redundant or contradictory data.
• Denormalization is only justified when there is a major
performance bottleneck, such as for data warehouses.
• Be suspicious of large tables (30 attributes or more).
• Be suspicious of any entity type that is difficult to define.
• It is acceptable to violate normal forms deliberately, when there
is a good reason to do so.
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13. Detailed Pitfall: Normal Forms Example
Violates
normal
form
Satisfies
normal
form
• The contact position and contact phone depend on the contact
name.
• The contact name depends on customerPK.
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14. Detailed Pitfall: Needless Redundancy
• Be careful with redundancy.
– Redundancy across applications.
– Redundancy within an application.
• Normal forms are one aspect of redundancy.
• Ideally there should be a single recording of each data item. (Rarely is this
completely feasible.)
• Organizations are rife with applications that overlap in awkward and loosely
controlled ways.
– This is a major justification for data warehouses.
• Don’t include redundant data to compensate for a poorly conceived
application.
• Redundant data is acceptable if you use built-in database features to keep
redundant data consistent (such as materialized views).
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15. Detailed Pitfall: Parallel Attributes
• Avoid parallel attributes for non-data-warehouse applications.
• Parallel attributes often codify arbitrary business decisions, reducing
information system flexibility.
Parallel attributes Parameterized model
• Widespread use of parallel attributes often indicates a poor model.
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16. Detailed Pitfall: Symmetric Relationships
• Avoid symmetric relationships for relational databases.
• Promote a symmetric relationship to an entity type.
Symmetric relationship Promotion to an entity type
• Otherwise double entry or double search.
• Symmetric relationships can be acceptable for programming.
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17. Detailed Pitfall: Anonymous Fields
• As much as possible, clearly describe the data being stored and
avoid anonymous fields.
fragment of Location table
locationAddress1 locationAddress2 locationAddress3
456 Chicago Street Decatur, IL xxxxx
198 Broadway Dr. Suite 201 Chicago, IL xxxxx
123 Main Street Cairo, IL xxxxx
Chicago, IL xxxxx
• How to distinguish the city of Chicago from Chicago street?
• May need to parse a field to separate city, state, and postal code.
• A few incidental user-defined fields are OK.
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18. Summary
• Data modeling is often a pivotal task in building a database
application.
• A data model determines an application’s data quality,
extensibility, and performance — and influences whether the
application has a chance at business success.
• You can improve your data models if you pay attention to the
pitfalls we have covered.
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19. Speaker Bio
• Since 1994 Dr. Michael Blaha has been a consultant and trainer in
conceiving, architecting, modeling, designing, and tuning
databases for dozens of organizations throughout the world.
• He has authored six U.S. patents, five widely used books, and
many papers.
• His most recent book, Patterns of Data Modeling, was published
in June 2010.
• Blaha received his doctorate from Washington University in St.
Louis and is an alumnus of GE Global Research in Schenectady, NY.
• You can contact him at blaha@computer.org and
www.modelsoftcorp.com.
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20. Questions?
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