Overcoming common data design mistakes

Even though data modeling and today's data modeling tools have matured, practitioners continue to make suboptimal logical and physical design choices. Good design is never automatic nor guaranteed without attention to detail.

This webinar will show how to spot and correct common data modeling mistakes, including:

• Logical data modeling mistakes that don't appropriately capture or enforce the business requirements
• Logical to physical data modeling transformations that compromise data alignment with business requirements
• Logical to physical data modeling transformations that can complicate application programming
• Physical data modeling decisions that can create major data integrity problems
• Physical data modeling decisions that can create major database performance issues