Since the concept of cross-tabbed data is so important to the use of the
InsightVX, I decided to write a small article to try and explain how the basics
work, as it might be a difficult concept for someone new to the software platform to grasp.
Most of the data is represented as a rectangular grid (or matrix) of values.
This is similar to how data is represented in Excel, although the way Insight
does it can be likened more to a pivot table instead of just a static grid.
A typical example of a cross-tab can be seen below:
As shown in the image, there are three main information areas in the
grid. Along the top are the column labels and along the left side are the row
labels. The area below and to the right of the labels contain the main data,
where the actual values are located. To look up a value, simply follow the
labels you are interested in to the right or down into the main data grid.
Where they intersect is your data value.
So from the above example, the most populous province is Gauteng and its largest demographic by age is people between the ages of 25 and 34.
The above is pretty basic and should be familiar to any Excel initiate, so let’s make it a little more complex by introducing filters.
A filter allows the user to exclude any unwanted data from the result, so they
can zero in faster on the data that is actually important to them. This is what is known as “drilling-down” into the data.
The example in the previous section was already using filters, though it may
not be obvious initially. Every item along the top columns was a filter, as
well as every item in the rows on the left. Each item applies a filter to the
entire data set and returns a result. The main data area just applies two
consecutive filters. It filters the filter.
Applying two filters to the data is the bare minimum that can be done – there are more complex combinations of filters that can be used.
This was just a small introduction to what can be done, the software allows
for many more operations on the data in an effort to help the user quickly narrow down the data they are interested in and make sense out of it.
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Written by: Albert Minnie