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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.
This online training course presents Steve Hoberman's Data Model Scorecard®, which provides the tools needed to measure and manage data model quality.
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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 towards:
- Data Modelers
- Data Analysts
- Data Architects
- Data Stewards
- Database Administrators
DM-03 The Data Model Scorecard®
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DM-03-00 About the Course (8 min)
DM-03-01 Scorecard Need (43 min)
- Why Measure Data Model Quality
- Traditional Review Methods
- Archer vs Data Modeler
- Enter the Scorecard
DM-03-02 Scorecard Categories (67 min)
- Category 1 - Model Type
- Category 2 - Correctness
- Category 3 - Completeness
- Category 4 - Structure
- Category 5 - Abstraction
- Category 6 - Standards
- Category 7 - Readability
- Category 8 - Definitions
- Category 9 - Consistency
- Category 10 - Data
DM-03-03 Scorecard in Practice (54 min)
- Introducing the Scorecard into your Organization
- Scorecard Challenges
- Scorecard Tips
- Applying the Scorecard
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Click –here- to download a more detailed outline of this course.
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This exam tests knowledge and understanding of criteria and processes for evaluation of data model quality.
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You will be tested in these areas:
- Importance of an objective data model scorecard
- Categories that make up the data model scorecard including correctness, completeness, structural soundness, flexibility, standards, readability, and model consistency
- Differences in evaluation approaches for subject area models, logical data models, and physical data models
- Techniques to strengthen data models, including model reviews, model substitutes (screens, prototypes, sentences, spreadsheets and reports), and the use of automated tools
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Additional Information
Number of Questions: 20
Time Limit: 40 Minutes
Passing Score: 70%
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Once you pass the exam, you will receive a Certificate of Education
documenting that you have demonstrated mastery of the topic. Course
exams count towards eLC certification programs. Visit our Certification page for more information about our various programs.
We recommend that you take detailed notes and review the course material multiple times before taking this exam. Click here to learn more about CIMP exams.
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