I caught an interesting post over on the Juice Analytics blog regarding Dashboards. They were arguing against the common wisdom of defining a Dashboard as a ’single page of information’.

I’d have to say I agree. It seems an entirely arbitrary rule for the display of information. I’ve seen very usable and insightful dashboards that are multiple pages long. They also note that the idea of a space restriction is only one of several ways to enforce constraining information to what is valuable and useful - another way is simply restricting the number of measures. If only people would do that!

While the whole discussion on what is or isn’t a Dashboard is interesting, it’s less interesting in my book than understanding information typologies that are used in decision making. Dashboards are an end point in this process, not (as they are typically made out to be) a jump-off point.

Information types for businesses fall into two major categories:

1. Actionable - information where the root cause of a change in state is known. On a car Dashboard this would be the fuel gauge - you know exactly what to do when it hits empty.

2. Derivative - information that you know is important to understand and track, but the measure itself is derived from multiple areas and therefore state changes could reflect a multitude of underlying issues. In the car example, this is the ‘fix engine’ light - very useful to know, impossible to understand.

If you think of Dashboards as displaying either one or both of these types of information, the design you employ is driven by the information typology, not any particular arbitrary layout rule.

A Dashboard that is heavy in Actionable information needs to measure the point of action. It’s no good having the ability to drill down to region when you need to know ‘at a glance’ if all your regions are ‘full on fuel’. This could arguable dictate a high degree of detail in a single layout.

A Dashboard that has Derivative information on it needs more interaction. If something at an aggregated level is trending down, you need to be able to drill into the reasons why. Hopefully the business case for putting together a heavily Derivative Dashboard would have included a ‘causation pathway’ for you to investigate.

There are probably other information typologies you could add to the list. There are certainly subsets of the two I mentioned here (Derivative measures could have underlying quantitative and/or qualitative issues). but by and large, it seems a better starting point for figuring out (and even defining) the essence of ‘Dashboarding’ than whether or not information should be on a single page.

An while we are on the subject, why hasn’t anyone come up with a better solution to the ‘check engine’ light indicator? In this day and age, you would expect some of these fancy cars to know what the actual problem is!

UPDATE: Just saw a recent post over on Tim Ferris blog that covers some of this from guest blogger Eric Ries, co-founder and CTO of IMVU.

I wrote a post a while back about using visualizations as metaphors. Seth Godin recently posted about how useful he found pie-charts when compared to your average bar chart. He got a lot of flak for this as most visualization experts will tell you the opposite - that bar charts are a far superior visualization tool.

I believe Seth’s point was similar to the one I was making in my first post - that sometimes a purposely overt graphic (such as a single pie with one large piece sticking out) is the best way to make a point. You could structure it as a metaphor, or it could be a simple exaggeration. Some political ‘data spin’ maybe?

The reason Seth thinks like this is because he is a Marketer. Marketers spend their lives (inside and outside their company) trying to convince people of things. To a marketer, a presentation that presents just the facts is pointless. Facts without an argument that in some way enhances the Marketer’s agenda is a waste of time.

This is a good thing. You’re paying your Marketing people to have a point of view.

To many data visualization experts though (and scientists), facts are these pure things that need to be wrapped in cotton wool and protected from opinion and false hypothesizing. Hence their dismay at the misleading pie-chart segment size error in displaying quantitative information.

The gulf here, between Marketing and most data visualization experts and BI (Business Intelligence) people, is about the size of Texas.

But you need both points of view. Marketers who get paralyzed by facts tend to do a poor job. I know too some people that will sound strange, but we’re not talking about denying the existence of gravity, we’re talking about challenging or changing perceived norms. If you get too caught up in why x number of people don’t do y, you are never going to try and figure out how to make y work.

Likewise, show me a company run by data visualization experts. No more commentary necessary.

What you really need is a mix of both mentalities. You need enough understanding of numbers and graphs to know when to break the rules. And enough respect to know when not to.

I think Seth has a pretty good balance.