Data Model Scorecard: Applying the Industry Standard on Data by Steve Hoberman

By Steve Hoberman

Info types are the most medium used to speak facts necessities from company to IT, and inside of IT from analysts, modelers, and designers, to database designers and builders. for this reason you must get the information version correct. yet how do you establish correct? that is the place the knowledge version Scorecard is available in.

The information version Scorecard is a knowledge version caliber scoring device containing ten different types geared toward enhancing the standard of your organization's information versions. a lot of my consulting assignments are devoted to employing the information version Scorecard to my client's info types - i'm going to enable you to observe the Scorecard during this ebook.

This e-book, written for those that construct, use, or evaluation information versions, includes the information version Scorecard template and a proof in addition to many examples of every of the 10 Scorecard different types. There are 3 sections: In part I, facts Modeling and the necessity for Validation, obtain a brief facts modeling primer in bankruptcy 1, comprehend why you will need to get the information version correct in bankruptcy 2, and know about the information version Scorecard in bankruptcy three. In part II, info version Scorecard different types, we'll clarify all the ten different types of the information version Scorecard. There are ten chapters during this part, each one bankruptcy devoted to a particular Scorecard type:

  • Chapter four: Correctness
  • Chapter five: Completeness
  • Chapter 6: Scheme
  • Chapter 7: constitution
  • Chapter eight: Abstraction
  • Chapter nine: criteria
  • Chapter 10: clarity
  • Chapter eleven: Definitions
  • Chapter 12: Consistency
  • Chapter thirteen: Data

In part III, Validating info types, we are going to organize for the version evaluation (Chapter 14), hide find out how to support throughout the version evaluation (Chapter 15), after which assessment a knowledge version dependent upon a precise undertaking (Chapter 16).

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Extra resources for Data Model Scorecard: Applying the Industry Standard on Data Model Quality

Sample text

Relationships A relationship between entities indicates that the instances of those entities may be related in some meaningful way. Each relationship may define rules around when and how many instances are related. Relationships are depicted as a line between the two entities. However, some kinds of modeling allow more than two, and these have different depictions. ” In a relationship between two entities, cardinality captures how many instances from one entity participate in the relationship with instances of the other entity.

Subtypes Subtyping allows grouping the common attributes and relationships of similar or related entities. Subtyping is an excellent way of communicating that certain concepts are very similar and for showing examples. In the publishing industry, an Author may write many PrintVersions and many eBooks: Each Author may write one or many PrintVersions. Each PrintVersion must be written by one Author. Each Author may write one or many eBooks. Each eBook must be written by one Author. Rather than repeat the relationship to Author, as well as the common attributes, we can introduce subtyping: Each Author may write one or many Titles.

Every relationship has a parent and a child. The parent entity appears on the one side of the relationship, and the child appears on the many side of the relationship. In this example, the parent entity is Author and the child entity is Title. When I read a relationship, I start with the entity on the one side of the relationship (the parent entity) first. ” I also always use the word each in reading a relationship, starting with the parent side. The reason for the word each is that you want to specify, on average, how many instances of one entity relate to an entity instance from the other entity.

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