Panorama has two powerful methods for summarizing and analyzing data — crosstabs and data outlines. I’ll show you crosstabs first. Using a crosstab I’ll break down sales for my fictional hobby shop down by month and by different types of credit cards. Here is the original data, downloaded from Rails.
It’s very easy to set up a crosstab, but for this guided tour I’ve already set one up called Card vs. Month. So all I have to do is select Card vs. Month from the View menu.
Note: To cause the crosstab view to open in a new window, I’ll hold down the Control key when I click on the View menu. This leaves the original data sheet window open while also opening the selected crosstab view:
Each column in the crosstab displays one month. Each row displays one type of credit card. Using the crosstab view I can easily see how much was purchased using Visa in April 2008, or by American Express in June. I can also see overall totals by month, totals by card, and the overall total for all orders.
If you refer back to the original data sheet you’ll see that includes data up to the end of August 2008. However, my crosstab view only shows data up thru June, which was all the data available the last time the crosstab was updated. To update the crosstab now I use the Calculate Cross Tab tool.
After a short delay the window updates to show the additional two months of totals.
Drilling Down to Raw Data
Here’s a cool fact — Panorama’s crosstabs are interactive. Not only can I see the summary values, but I can also drill down to see the raw data behind any cell in the crosstab. To do that I click on the cell that I am interested in, in this case purchases made using a Visa card in April 2008. Then I click on the Select Original Data tool.
Since I opened the crosstab in a separate window, the original data sheet is still open. When I click over to the data sheet I can now see the actual invoices made using a Visa card in April. In this case there are 31 such invoices.
I can click on any cell in the crosstab and select the underlying data, including the totals and the categories in the left column. This feature is a great auditing tool for investigating anomalies in any database.
Continue the Tour —> Data Outlines