Wednesday, 28 November 2012

At A Glance Or Interactive?

Data visualisations can come in many forms. In its crudest form it can be an easily created chart in Excel. In its true form a data visualisation answers a question “at a glance” whilst not actually displaying any figures or values.

An “at a glance” visualisation by its very nature must provide at least a single piece of information that can enable the user to make a decision without any further filtering or analysis. It may also contain some form of alert or exception, and from all of this information it should promote an action or output.

In other cases, a data visualisation can and should promote more questions. Asking more questions requires analysis of the underlying information so an “at a glance” dashboard is now too limiting. So perhaps other “interactive” analysis tools are required … but to work as trusted information, the data must be from the same source with the same business logic.

So let’s move away from the business world of data and decisions for the moment and describe how a visualisation works.

We see data visualisations all the time, all around us. The world is full of them.

If I see a dark cloud coming this way then I can assume it is going to rain pretty soon … I don’t need a weather forecast or any form of rainfall data … my data is in my view “trusted” and more reliable than any weather forecast.

However, if I do go to a weather website I see the following image … hopefully.  So this provides an “at a glance” answer to my question. What I cannot do is analyse the situation without additional underlying data.

So “interacting” with the underlying information a little more and filtering on the next 5 days we can see this visualisation.

Now we make a decision not based on today’s weather … but based on a short range forecast.

The weekend is looking great!

Well as we all know, although weather data is getting better and better as technology improves … it is not totally guaranteed. And by the weekend things may have changed.

But let us look at some data that is correct and is fixed. The first image is fixed, as a dashboard, and although we could provide a few parameters for the user to select the data is pretty much configured for one purpose or one “at a glance” piece of information.

The London Olympics 2012 was a great time for the UK, and athletes from all over the world put in more effort with more technology and more financial input than ever before.
The information we have is collected for each competing country and allows us to compare information across competing countries and athletic achievements.
(data supplied courtesy of The Guardian)

This is a dashboard that displays the “top 10” results of the London 2012 Olympic Games.

Officially, the results are measured by accumulating the points for each country based on 3 points for a gold medal, 2 points for a silver and 1 point for a bronze. (Although we do like to count the UK 3rd based on the gold medals but that is not the official calculation).

This is a great “at a glance” visualisation at the results and one of the options I have on this dashboard was to view the medals over the 16 days … which would have shown the UK pretty low for the first few days until our events started to arrive on the timetable.

But is this fair? Should we not be judging the number of medals based on population? After all a very small country will struggle to supply the number of athletes that UK, USA, Russia and Australia can produce and therefore could be seen as never having a chance.

So with a lot more data from each competing country we can move away from the “fixed” dashboard approach and dig deeper into the data and go “interactive” to analyse how successful countries were based on their population. 

A very different “visualisation” appears, and in this we can see that in real terms, based on their population, Grenada, Jamaica and the Bahamas do really well.

Ok, let’s move the “interaction” a little deeper, still on “headcount” and see how the team results appear based on the medals they won compared to their team size?

Once again a different picture but we now see China and the USA coming back into the picture, simply because of the size of their teams and the number of medals they won.
But Jamaica appeared officially 21st, Iran were officially 19th and Botswana officially 69th.
Well done Botswana … all 4 of you who won a single silver between them!

Finally, one more interesting item to check is “finance”. How much money does each country have to spend on their athletes, training facilities, stadiums, coaches, etc…
Well I don’t have the exact figure of direct spend for the games so let us use the country’s GDP as a rough guide to each country’s wealth, and therefore an indicator of availability of spend for sporting events.

Well once again, a very different story.

Perhaps with a lot more “interaction” from the information above we could calculate where in the world would be best for an athlete to train to get the best chance of being in the 2016 Olympic Games and then of winning a medal?

Those and other questions I will leave until the DSCallards December Seminar on the 6th February 2013 at SAP in Maidenhead.

We will be discussing the merits of fixed “at a glance” dashboards that have the information immediately available compared to “interactive” analysis tools, which have the data set in the background waiting for the user to interact and extract in any way they seem fit.  Both technologies work for the business … depending on who the business user is and what questions they are trying to answer.

But the underlying data and business logic must remain the same.

Hopefully see you all there on the 6th.

Written by Ray Kemp, Technical Director, DSCallards.

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