Prerequisite: This course assumes a basic understanding of data modeling.
Gravity, wind, and aim influence an arrow's trajectory, much the same as deadlines, skills, and biases influence a data model's trajectory. These factors strongly impact whether a model will reach its target of appropriately representing a business solution. The archer’s score can be calculated quickly, and we can easily see success or failure. This is where the analogy ends, however, because there is no standard way of measuring the strengths and weaknesses of our data models, leaving much up to interpretation, perception, and the test of time.
After years of reviewing hundreds of data models, I have formalized a set of data model quality criteria into what I call the Data Model Scorecard. The Scorecard contains all of the criteria for highlighting strengths and identifying areas for improvement in our designs. This course goes into detail on the Scorecard and provides techniques and tips for improving the quality of your models.
Course Outline
You Will Learn
- The importance of having an objective measure of data model quality
- About 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
- Leveraging techniques to strengthen our models, including model reviews, model substitutes (screens, prototypes, sentences, spreadsheets, and reports), and the use of automated tools that enforce modeling best practices and standards
- How to introduce the Scorecard into a development methodology and your company culture
Geared To
- Analysts; architects; developers; database administrators; modelers