MANY problems with the logical resolution of corporate data. Some of the many
problems are as follows:
Resolving key structures—a key in one part of the corporation is different from a similar key in another
part of the corporation.
Resolving definitions—data defined one way in the corporation are defined another way in a different
part of the corporation.
Resolving calculations—a calculation made one way in the corporation is made using a different
formula in another part of the corporation.
Resolving data structures—data structured one way in the corporation are structured differently in
another part of the corporation.And the list goes on.
In many cases, the difficulties of resolution are so difficult and so ingrained in the data
that resolution cannot be satisfactorily done. In this case, the corporation ends up having
different analyses being done by different organizations in the corporation. The problem
with different organizations doing their own separate analysis and calculation is that the
result is parochial among the different organizations. No one at the corporate level is able
to see what is going on at the highest level of the corporation.
The problem of resolution of data is magnified with corporate data when data cross the
boundary of structured data and big data. And even within big data, when data cross the
boundary between repetitive unstructured data and nonrepetitive unstructured data, there
is a challenge.
There are then serious challenges when the corporation attempts to create a cohesive,
holistic view of data across the entire corporation. If there is to be a true corporate
foundation of data, it is necessary to integrate data, as seen in Fig. 1.5.3.
Chapter 1.5: Corporate Data Analysis