Datasheet

Figure 1-12 The last record of CORRUL Corp. was not correctly updated to the new
address.
If the City data is not properly updated everywhere, when you attempt a by
city filter/analysis, you will not get accurate results. Some of the invoice
records could reflect the incorrect state locations of the customer. The attrib-
utes of data can and often do change, and if these changes are not accurately
recorded, your data analysis will provide an incorrect picture of the actual
situation.
Splitting data into separate tables
Data must be consistent if analysis is to have any true value in the decision-
making process. Duplicate data is the bane of consistent data. If an entity is
changed in one place, it must be changed everywhere. Wouldn’t it be more
logical and efficient to record the name and information of a customer only
once? Instead of recording the same customer information repeatedly, you
could simply have some form of customer reference number, which could then
send you to another list where the information is unique and written once.
This brings us back to the relational database concept, which enables you to
have separate, carefully designed unique lists of data that are related to one
another by their unique identifiers (primary key).
Many Excel users, in fact, without realizing it, make great efforts to make the
data on their spreadsheets “relational.” For example, the use (or overuse) of
the VLOOKUP or HLOOKUP helps you match data from separate lists that
have some data field or key in common. Although much is possible with these
functions, they do have their limitations. Furthermore, the functions are not
very intuitive. They attempt to solve a problem that Access was designed from
the ground up to address. When Excel users use these functions to bring data
from separate lists onto a single row, they are in essence creating a relationship
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