Why should we use a pivot table?

Imagine that you are the leader of an office retail business. Here is your dataset where you keep track of every single shipment, including shipping classes, sector, location, product type, sales, quantity, discount, and profit. Here are ten of them. 

There are questions that you ought to ask: what shipping class is most commonly selected, and for which types of products, are consumers or corporate buying the most what region of the country is buying the most, what category and subcategories are selling the most and which are most profitable, etc. Note that I am no financial analyst but even from a layman's perspective these are worthy questions when every business is playing the game of big data. Now with ten shipments, doing this manually wouldn't take too long, but we need to plan for when we'll have thousands and maybe millions of shipments. Pivot tables can be quickly implemented to do all of this data compiling so that we can leave our mental processing power for actual analysis. For example, I made these within a minute. The first one counts quantity while the second merely displays the values as a percentage of the column total. So it means that 58.84% of furniture shipped through standard class delivery.

To answer the first question, there was no difference between categories with respect to shipping classes. What else can we glean? We can see that. in ascending order, it goes same-day, first, second, and standard was most popular and technology, furniture, and then office supplies sold the most. I am not sure if we can do anything with the distribution of shipping classes, but let's consider the fact that office supplies sold the most. We should then see if they are the most profitable:

It appears that technology made us the most profit while also being the least category sold. This tells us that technology is a high-margin category, meaning that it has a high sale price and a low production cost, and the other categories are comparatively lower-margin. As the leader, you should use this data try and market more technology to increase quantity sold and thus profit. Furthermore, you should conduct an investigation into your other categories to see if you can potentially increase margins; it may be that you can't but it's still worthwhile to look. 

These are just a few of the questions that we can answer by creating a few pivot tables from our dataset. You may think this was rather trivial but I assure you that pivot tables can be manipulated and advanced into more complex forms that fit whatever inquiries you have about your dataset. I assure you that learning how to make pivot tables is absolutely worth the time as they can analyze enormous datasets very quickly and leave with the important statistics. Pivot tables also update as you update the dataset so there is no need to recalculate. 

The example we covered today was a sample dataset from Kaggle (https://www.kaggle.com/datasets/ishaanthareja007/samplesuperstore) and I used it because I wanted to show you how we can transform a raw dataset into a simple but informative table of numbers. I'm sure I could have just shown you a pivot table but I felt that it was necessary that you understand the whole process to get how useful pivot tables are.