As the senior data modeler in your organization, you review data models built by project teams to ensure they follow modeling best practices. There is a new reporting system currently being modeled, called ‘Spam’. Spam is a reporting application that will initially produce weekly spreadsheets for business users capturing spam email quantity by keyword on a given day of the week. The following table is a subset of what might get produced, where the columns represent common spam words and the rows represent the days of the week:
| Viagra | Rolex | Model | Debt | |
| Monday | 56 | 48 | 5 | 15 |
| Tuesday | 38 | 22 | 19 | 35 |
| Wednesday | 19 | 24 | 9 | 43 |
| Thursday | 8 | 54 | 82 | 23 |
| Friday | 94 | 105 | 85 | 107 |
| Saturday | 6 | 0 | 5 | 3 |
| Sunday | 4 | 2 | 6 | 1 |
The project team could not agree on one single model for this project, so they present you with three different physical data models (subsets of each shown below). Note that in Option 3, a Spam Reporting Factor Type Name could be ‘Day of Week’ and Spam Reporting Factor Value could be ‘Monday’. Instead of selecting the best model from these three, you decide to play to safe and list the situations where each model would be ideal.
For this challenge, what situations would you use each of these models?
Option 1:
Option 2:
Option 3:
