Much of structured data are available for online processing. And all elements of data in
the structured environment are able to be located and accessed for processing. For this
reason, all structured data are categorized as business-relevant data.
Consider an example. A customer walks into the bank and asks for a withdrawal of $500.
The bank teller accesses the customers’ account and sees that there is a sufficient balance
in the account. The bank teller then authorizes the withdrawal for $500. The data
regarding the customers’ account have been used and are certainly business-relevant.
Now, consider the data in the structured database of the bank that are not being accessed
by a bank teller. Are these data still business-relevant even though they are not being
used? The answer is that the data are still business-relevant even though they are not
being used. They are still business-relevant if they might be used.
That is why all structured data are considered to be business-relevant. Its actual usage has
little to do with its business value. The data still have business value and relevancy even
if they are not being actively used.
Now, consider the business relevancy of repetitive unstructured data. Fig. 1.4.7 shows
that only a tiny fraction of repetitive unstructured data are business-relevant. A larger
percentage of repetitive unstructured data are potentially business-relevant. And a
significant portion of repetitive unstructured data are not business-relevant.
Fig. 1.4.7 Business relevancy.In order to understand the business relevancy of repetitive unstructured data, look at one
of the many examples of repetitive unstructured data. Consider log tapes. When looking
at a log tape, nearly, all the records on the log tape are meaningless to the business user.
Only a few important records on the log tape may have direct business relevancy.
Or consider telephone call detail records. In a days' time, many, many records will be
Chapter 1.4: Demographics of Corporate Data