A Framework for Classifying Data - High-Performance Web Databases: Design, Development, and Deployment - page 71

fits into the external administrative and the external directional quadrants.
However, what is meant by the term customer is not necessarily constant;
and the more detailed the data attributes applied to it, the less commonal-
ity of data exists across quadrants.
For example, in the external administrative quadrant, the interest in cus-
tomer focuses on current customers and on servicing their immediate
needs to keep them. But in the external directional quadrant, the focus is
more on potential customers and on demographics that are useful in help-
ing the organization to attract new customers. In the external directional
quadrant, there may be an interest in the existing customer base, but only
insofar as the existing customer is also a potential candidate for new ser-
vices or products.
The essential difference in the interest and focus of the data model or
data administration in each of the quadrants shows up in at least two ways:
the number of attributes in each quadrant, and the stability of the model in
each quadrant. In the external administrative quadrant, data in a data
model typically has many attributes. For example, the major subjects and
their subtypes in account would have such attributes as:
• date opened
• domicile
• current balance
• type
• fund assignment
• statement date
• statement cycle
• social security number
The major subjects and their subtypes of loan have such attributes as
• interest rate
• collateral
• late payment penalty
• balloon payment
• renegotiation date
The major subjects and their subtypes of savings have such attributes as
• interest rate
• christmas club
• minimum balance
• minimum deposit
• withdrawal date
The data attributes found in the external administrative quadrant are
relatively stable and numerous. But this is not so with regard to major cat-
egories in the external directional quadrant-customer, competition, and