Explore Analytics: The Blog | Analyze and Visualize Your Data

Brand new feature for Explore Analytics — Pareto Charts!

Pareto charts have been around a while, and it’s easy to forget where they come from when to use them.

The name “Pareto” comes from an Italian economist, Vilfredo Pareto who noticed something that we call the 80-20 rule. 80% of the land in Italy was owned by 20% of the people, 80% of the peas were in 20% of pea pods, etc.

It’s a good rule of thumb: in many distributions, you can see this 80-20 rule at work. And if you want to know whether the 80-20 rule applies to you, and you want to understand it, you can use the Pareto Chart, which is intended to visualize this.

Let’s look at two examples and I’ll show you what I mean.

First, let’s take an example from IT Service Management. Let’s say that I’m responsible for Service Management, and I’m trying to understand where should I spent my time to make the most impact. Let’s look at the pareto chart:

The chart above shows service disruptions (outages), broken down by what systems are failing, and how much business impact (expressed in dollars) those outages are incurring.

You can see that 57.6% of our impact is coming from the email service; whereas Electronic Messaging is only 20.6% of the cost. But what’s most notable is the dotted red line — that’s showing us that the 80% mark is just higher than Email and Electronic Messaging.

That means that 80% of the impact is being caused by just two business services.

Of course, the 80-20 rule is just a rule of thumb; what we’re actually seeing above is that 50% of business services are causing 78.2% of the cost. And if you measured by a different measure — for example, by count of incidents, or by time spent restoring service — you might get a different breakdown. Try changing the cutoff using the controls in the legend.

Regardless of the exact data, you can see that this is a powerful way to make the decision where should I spent my time.

If I’m a service manager, I look at that chart and decide that our next big priority is figuring out how to improve our email service.

For another example, let’s look at some sales data.

The following shows a company’s revenue broken down by sales:

As you can see, this chart has a longer tail. But it’s still demonstrating a similar principle — out of 10+ products (we’re cutting it off at 10), it still only takes our three most popular products to get to most of our revenue. The total revenue, 1.2mil, is the top right dot that ends the pareto curve.

Deciding how to use this data is a different story, but the clear story being told here is where to get your bang for your buck.

Show Me Nothing!

Some relationships can be tough.

Most of the time, when you’re running a report on relational data, you’re trying to see what relationships exist, and with Explore Analytics, that’s fairly easy to do.

What can be more difficult, however, is showing relationships that aren’t there. Because there is no relationship, there’s no data that exists.

Let’s take a specific example: you want to create a view that shows users with no assigned assets.

Fear not! With Explore Analytics, we can create a report that can show you where there is nothing.

What we’re going to do is:

Create a pivot of assets grouped by users – this provides the mapping of assets to users. This will be missing the users with no assets, however.
Create a pivot of all the users – this provides us a list of all the users, including the ones with no assets – it’s going to fill in the gaps of the users with no assets.
Create a mashup of the two views – By putting the two views together, we are able to see any users without assets.

This method works the same whether it’s users with no assets, users who haven’t updated the system, assets without anti-virus, etc.

We’re going to walk through this example with the demo ServiceNow data source:

Create a pivot of assets grouped by users

Create a new view on the Asset table
Type: Pivot
Row Labels: Assigned To
Values: Count (should be auto-generated for you)

Relabel the “Assigned To” field to “Name”. This will ensure that the “User” in this view matches the “User” in the other view.

Create a pivot of all users

Create a new view on the User table
Type: Pivot
Row Labels: Name
Remove “Count” from the Values field, we won’t need it

Create a mashup of both views

Create a new mashup view, with both of the views we just created as an input view

Sort by Count, smallest to largest.

Voila! You should have a list, at the top of which are the users with no assets!