An Introduction to Pivot Tables

An Introduction to Pivot Tables


Welcome to the FDTC’s course on pivot tables! This is one of our favourite and most useful courses as pivot tables are probably the most useful tool to any member of a Finance Team (until PowerQuery and Powerpivot came along!).

So what is a pivot table?

Well, a pivot table should really be called a summary table as they sumarise datasets, large or small based on any criteria that is in the dataset that the user chooses.

Let’s say you’ve got a great big dataset of sales data and you want to see the total amount of sales by salesperson for analysis or perhaps for revenue journal postings. You could do that by creating a formula table using SUMIFS. However, that would take a while to write, and every time you wanted to add in a new criteria, you’d need to rewrite the formulas.

A pivot table will do the summary instantly, and any changes that you want to make, by bringing in additional summarising fields, can be done simply and quickly.

Lesson Synopsis

In this lesson we compare how easy it is to create pivot tables to summarise large datasets against static formula driven tables. The answer of course is that it’s much easier and quicker to create a pivot table to summarise datasets vs writing static formula tables. It’s also faster to amend them, add data to them, take data away from them, format them, refresh them and pretty much anything else you might want to do to change your initial table.
This lesson is simply to demonstrate what a pivot table can do and compare it to it’s closest ‘summarising’ alterntive in Excel.

We also look at the dataset that we’re going to be using in this course which is a set of sales data showing sales made by a fictitious company of different types of products, in different sectors and in different locations around the globe, over a period of time. This then presents us with lots of opportunities for summarising datasets in different ways; perfect work for a pivot table.

Resource Listing

The Video

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