In the free Data Model Scorecard Validation Tool, there are five “killer” questions out of the over 150 questions that are part of the tool, that if answered improperly will automatically score the model with the lowest setting of “Poor”. If you’ve used the tool, you might have experienced one or more of these questions :L).
I’ll talk about the first of these five questions in this post. This is the question on model scope: Is the scope of the model clear?
A data model should have a well-defined scope. If the model captures the requirements for a particular application, it should have that application’s scope. If the model explains the concepts in a data warehouse or other broad initiative, it should have the scope of that initiative.
For example, imagine you are a data modeler for a hotel in New Jersey and the hotel manager asks you to build a data model for a survey application. Your data model would have a very well-defined scope of all of the concepts needed to build a survey application for that particular hotel. This is an example of project scope.
Now imagine if you are a data modeler for Hilton or Marriott and are asked to build a data model for a survey application for all of Hilton or Marriott? Your scope would still be the survey area, but the concepts on the model would be for the entire hotel chain instead of for just a particular hotel. This is an example of program scope.
This question on scope is extremely important because it ensures we are using the right set of terms and the correct name and definition for each term. Project scope would allow us to use the terms that have meaning within that group or department, without the need to reconcile across departments. Program scope would require reconciliation across departments using consistent terms across a much broader area such as the entire enterprise.