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territory D. Thus, associated with the quantitative data element that we record are
numerous other important data elements that may, or may not, be quantitative.
Sometimes the context is obvious, sometimes the context is complex and difficult
to identify, and often, there is more than a single context that is essential to consider.
Without an understanding of the data context, important insights related to the data
can be lost. To make matters worse, the context related to the data may change or
reveal itself only after substantial time has passed. For example, consider data which
indicates a substantial loss of value in your stock portfolio, recorded from 1990 to
2008. If the only context that is considered is time, it is possible to ignore a host
of important contextual issues—e.g. the bursting of the dot-com bubble of the late
1990s. Without knowledge of this event context, you may simply conclude that you
are a poor stock picker.
It is impossible to anticipate all the elements of data context that should be col-
lected, but whatever data we collect should be sufficient to provide a context that
suits our needs and goals. If I am interested in promoting the idea that the rev-
enues of my business are growing over time and growing only in selected product
categories, I will assemble time oriented revenue data for the various products of
interest. Thus, the related dimensions of my revenue data are time and product.
There may also be an economic context, such as demographic conditions that may
influence particular types of sales. Determining the contextual dimensions that are
important will influence what data we collect and how we present it. Additionally,
you can save a great deal of effort and after the fact data adjustment by carefully
considering in advance the various dimensions that you will need.
Consider the owner of a small business that is interested in recording expenses
in a variety of accounts for cash flow management, income statement preparation,
and tax purposes. This is an important activity for any small business. Cash flow
is the life blood of these businesses, and if it is not managed well, the results can
be catastrophic. Each time the business owner incurs an expense, he either collects
a receipt (upon final payment) or an invoice (a request for payment). Additionally,
suppliers to small businesses often request a deposit that represents a form of partial
payment and a commitment to the services provided by the supplier.
An example of these data is shown in the worksheet in Table 2.2. Each of the pri-
mary data entries, referred to as records , contain important and diverse dimensions
referred to as fields —date, amount, nature of the expense, names, addresses, and an
occasional hand entered comment, etc. A record represents a single observation of
the collected data fields, as in item 3 (printing on 1/5/2004) of Table 2.2. This record
contains 7 fields—Printing, $2,543.21, 1/5/2004, etc.—and each record is a row in
the worksheet.
Somewhere in our business owner’s office is an old shoebox that is the final
resting place for his primary data. It is filled with scraps of paper: invoices and
receipts. At the end of each week our businessperson empties the box and records
what he believes to be the important elements of each receipt or invoice. Table 2.2
is an example of the type of data that the owner might collect from the receipts and
invoices over time. The receipts and invoices can contain more data than needs to
be recorded or used for analysis and decision making. The dilemma the owner faces
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