InetSoftWebinar: Building a Visualization Off of Excel Spreadsheets

This is the transcript of a Webinar hosted by InetSoft on the topic of "What Is Visual Analysis?" The speaker is Jessica Little, Marketing Manager at InetSoft.

I can choose a database. I could even be using an Excel spreadsheet to build this visualization. And then I open up a new worksheet, and you see here those fields that I was showing you before come up on the left hand side. And the basic user experience is all visual. The whole desire of visual analysis is to make it so that people can think with their data.

And here I can browse through my data just simply through drag and drop. It’s a very simple experience. If I want to look at markets, I drag them over. I see I have four markets. And if I want to look at the sales, I drag that here. And this is the aggregated sales for the central region, and I can right click here, and you can see here is the raw data that I was showing you a moment before. Here is all the data for central that’s been aggregated together.

Anyone listening who has ever used pivot table, they will recognize this basic drag-and-drop style. A pivot table is also an example of this. I can drop product type here, and now I have the aggregated sales for coffee in central. And I can continue to proceed forward here. I could break this down by flavor. So I have the aggregated amaretto for central.

If you are a pivot table user, it's a very powerful way of working with data. However, it has some limitations. Humans don’t process tabular data as effective as they do visual encodings of data. And so what we do is we have a specification language here that allows us to specify graphics. In particular, this represents my summer sales. If I drag it up here and drop it in here, I will get an axis.

Now what we have is a graphical view. So here is amaretto in central and then Columbian in central, and I can now visually compare these two. Before in the tabular view, I had to do mental math to compare those two. Humans are not very fast at mental math and fairly inaccurate. But in the graphic view here, I can compare the line lengths, and I have the visual cortex that does that extremely quickly. And so that’s a really great way to work with data. It's very intuitive to people. Everyone has a visual system, and you can tap the power of it.

Related to that, I can leverage graphics properties. For example, if I drag out profit and assign that to color, now I am actually working in higher dimensions. I can instantly see right here that cafe mocha in the east has got pretty good sales. You can see that by the length of the line, but notice it has negative profit.

So there is something unusual about that particular situation, and it's these kinds of pattern detections that visualization is really effective at. And because it's in context, I know it's unusual. I can see the sales relative to its neighbors both across market and across product type, and I can see that there is an outlier here. And down here, I can also see that green tea on the west that’s got a similar issue around profits.

Instantly, visual patterns might cause you to ask a new question, and it's that iterative process of question answering that is facilitated using your visual system. That’s what visual analysis is about. You are looking at views and answering questions or asking questions. And the key thing about that is there is no one right view. There is no single view that you could prepare in advance. It's the sequence of views.

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So here I have got a question. Well, what is the relationship between summer sales and summer profit? Well, there is another view that’s very good for that, but I like this view so I am going to keep it here. And we have a tab style here. If I make a new one, I can now proceed to explore forward.

And so if I do that, I can see instantly that here we have summer sales, and we have summer profits, and they appear to be sort of linearly related. I have assigned market to shape and color is showing product type. So here is a graphical view that is good for answering a different question than I had before. This is very good for comparing sales. It's being able to generate a lot of different views in an exploratory human thought kind of way. That’s what visual analysis is about.

Now if you are really into data analysis, of course, visualization can also be used for richer kinds of analyses. For example, we can turn on trend lines. We can see here that there are trend relationships for the different types of products types. So we support that kind of statistical analysis in the application as well. We can also change what we are looking at.

So for example, this is a breakdown here by product, but I can instead break down by state. And if we do it there, we are going to see that tea is an unusual product type compared to the other ones because its trend line is different. These sorts of things happen that lead to new questions. So now that I have brought state into the mix, it leads to a new set of questions, like we have been talking now with that, would it be a geographic question.