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A frequently overlooked aspect of data quality management is that of
data model quality. We often build data models quickly, in the midst of
a development project, and with the singular goal of database design.
Yet the implications of those models are far-reaching and long-lasting.
They affect the structure of implemented data, the ability to adapt to
change, understanding of and communication about data, definition of
data quality rules, and much more. In many ways, high-quality data
begins with high-quality data models. Steve Hoberman's Data Model
Scorecard® provides the tools needed to measure and manage data model
quality.
You Will Learn:
- The importance of having an objective measure of data model quality
- The categories that make up the scorecard including correctness,
completeness, structural soundness, flexibility, standards, and model
consistency
- How to apply the scorecard to different types of models
- Techniques to strengthen data models, including model reviews,
model substitutes (screens, prototypes, sentences, spreadsheets and
reports), and the use of automated tools to enforce modeling best
practices and standards
- How to introduce the scorecard into a development methodology and your company culture
This course is geared toward:
Analysts, architects, developers, data stewards, database administrators, and data modelers
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Data Model Scorecard Review 
