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Data models evolve toward excellence when we take advantage of tried and proven designs.

Design patterns enable us to benefit from mature solutions that have previously been developed.

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This inevitably results in an overdesigned model that costs more to implement, is costlier to maintain, is more likely to contain defects, and is more difficult to understand.

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The model evolves to support future requirements as those become reality.Sometimes it is a fear of rework; data modelers prefer the goal of getting it right once.Other times it is the fear of unintended side effects or the risk of creating a spaghetti mess. Agile modeling calls for a minimally sufficient design up front to establish a reference model that guides the delivery team’s incremental development activities.TDWI’s BI This Week newsletter ran this short article on June 22, 2011, and I’ve included it below.It provides a glimpse at the important technical practices that enable you to safely evolve your data models in ways that improve the design quality rather than degrading it.

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