Modeling your data

Data analysts can perform the task of data modeling in a variety of ways.

To model data:

1. Build critical user views.
   1. Carefully examining a single business activity or function.
   2. Develop a user view, which is the model or representation of critical information that the business activity requires.

      This initial stage of the data modeling process is highly interactive. Because data analysts cannot fully understand all areas of the business that they are modeling, they work closely with the actual users. Working together, analysts and users define the major entities (significant objects of interest) and determine the general relationships between these entities.

      In a later stage, the analyst combines each individual user view with all the other user views into a consolidated logical data model.

2. Add key business rules to user views

   Key business rules affect insert, update, and delete operations on the data.

   For example, a business rule might require that each customer entity have at least one unique identifier. Any attempt to insert or update a customer identifier that matches another customer identifier is not valid. In a data model, a unique identifier is called a primary key.
3. Add detail to user views and validate them.
   1. Add other descriptive details that are less vital.
   2. Associate these descriptive details, called attributes, to the entities.

      For example, a customer entity probably has an associated phone number. The phone number is a non-key attribute of the customer entity.

   3. Validate all the user views

      To validate the views, analysts use the normalization process and process models. Process models document the details of how the business will use the data.

4. Determine additional business rules that affect attributes.
   1. Clarify the data-driven business rules.

      Data-driven business rules are constraints on particular data values. These constraints need to be true, regardless of any particular processing requirements.

      The advantage to defining data-driven business rules during the data design stage, rather than during application design is that programmers of many applications don't need to write code to enforce these business rules.

      For example, assume that a business rule requires that a customer entity have a phone number, an address, or both. If this rule doesn't apply to the data itself, programmers must develop, test, and maintain applications that verify the existence of one of these attributes. Data-driven business requirements have a direct relationship with the data, thereby relieving programmers from extra work.

5. Integrate user views.
   1. Combine the newly created different user views into a consolidated logical data model.
   2. Integrate other data models that already exist in the organization with the new consolidated logical data model.

   At this stage, analysts also strive to make their data model flexible so that it can support the current business environment and possible future changes.

   For example, assume that a retail company operates in a single country and that business plans include expansion to other countries. Armed with knowledge of these plans, analysts can build the model so that it is flexible enough to support expansion into other countries.