In this article, we will go into detail what PivotTable is, how it applies to Roambi, how to use it, and why it is awesome.
Pivot Table turns “raw data” into a “cross tab” format.
It is probably easier to show what that means.
Here is an example of “raw data”.
It is basically a table. We have 8 columns: “Category”, “Lines”, “City”, “State”, “Quarter”, “Margin_”, “Quantity_sold”, “Sales_revenue”.
Each row is a transaction.
If you create a Pivot Table out of that data set, you get something like this:
We turned rows to be State, then each Lines that’s sold in each State.
In columns, we are showing “Sales Revenues”, “Quantity_sold”, and “Margin_”, then break down those values by Quarter.
And because PivotTable sums everything up, it’s really easy to tell what’s the total “Sales Revenues” for “2001-Q1” for the “State” of “California” for the “Accessories” Line is (Answer: $219,756).
There are other cool features like changing filters, sorts, and change the summarization methods. It’s a really good way to make the data more accessible.
Speaking of making data more accessible...
Incidentally, the way PivotTable lays the data out matches very nice with the way Roambi expects the data.
With the introduction of the PivotTable support, once you created your Pivot Table in your XLSX file. If you import that into Roambi, with a few easy steps you group the fields into a micro chart.
Here is the same XLSX file as a Layers View:
Or if you prefer, the same XLSX as a Catalist view:
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