A Framework for Classifying Data - High-Performance Web Databases: Design, Development, and Deployment - page 69

Exhibit 5-3. Combined payback and systematization

ratings of each quadrant.

Internal External

Administrative 11 18

Directional 7 11

This quadrant contains such data as
new technology, competition, and economic conditions. Under any rules,
there is a very high payback to the systematization of this data but there
are also many problems. Some of these are:

The External Directional Quadrant.

• the irregularity of the availability of the data
• the extreme diversity of media over which information is transmitted
• the lack of uniformity of the data itself

In short, the data in this quadrant, as important as it is, resists system-
atization at every turn.

Adding the Ratings

Another, somewhat arbitrary, way to assess the affinity of the quadrants
for automation is simply to add together the payback and the systematiza-
tion ratings, as illustrated in Exhibit 3. The exhibit shows that the adminis-
trative external quadrant is easily the most favored target for automation,
whereas the directional internal quadrant is the least amenable. An argu-
ment can be made that adding payback and systematization ratings further
obscures an already obscure rating. But the rebuttal is that, whatever the
payback, if systematization is very difficult, then the likelihood of automa-
tion is just as poor as if the systematization prospects were very high and
the prospects for payback were poor.

The key for a customer is usually fairly clear, while the key for an objec-
tive is not. The attributes for a product are fairly clear, while the
attributes for a goal are ambiguous. The specifics of, for instance, an
insurance policy are clear and well defined, whereas the specifics for an
organization's policy regarding overtime or employee absences are less
clear. These reasons lead to the conclusion that internal data is difficult
to automate and external data is not. If the purpose of a data model is to
enhance or enable automation, it is questionable whether modeling the
internal directional environment is even appropriate. Data from such
areas as mission, strategy, and objective does not lend itself to automa-
tion in the same way that customer, product, and account data does.
There may be real benefits to modeling the organization's internal work-
ings, but these may not be easily realized by automation. Modeling the
internal directional data of the organization can be useful in identifying