5 steps to successful data visualisation
Jorge Camoes says you can learn a lot about/from the data during the preparation stage.Jorge Camoes has a passion for data visualisation. Following a successful career in business analytics at Merck, Camoes founded WiseVis, a data visualisation consulting business based in Oeiras, Portugal. Camoes is recognised widely as author of the popular blog Excel Charts, in which he shares strategies for getting the most out of data with common tools like Excel.
He just published his first book, Data at Work,where he explores these themes further, presenting readers with a thoughtful discussion of the potential of data visualisation and strategies for implementing executive dashboards. On Oct. 26, he will present at the JMP Explorers Seminar in London, which is being streamed live online. I asked him a few questions on exploring and visualising data.
If you can easily connect to the organisation’s information infrastructure and get exactly the data you want and the way you want it, to answer a question truthfully, thoroughly and in a timely manner, then congratulations, you probably have good data to work with. And you’re a beautiful unicorn.
Jorge Camoes just published his first book, Data At Work is the first book.The real world is uglier: In each organisation, there is a secret dark basement (unknown to top management), home to a few creatures who lurk there and whose job description includes “data preparation,” and other tasks of similarly unsavoury nature.OK, I can’t prove these basements are real, but data preparation is indeed a resource-intensive, time-consuming, unsexy, underappreciated but fundamental task in any data analysis project.
While we are getting more and better ETL tools, we often have to deal with problem that we shouldn’t have, in the first place, like converting PDF files. Here is an example of a good practice: The UN Population Division publishes population projections and estimates in regular Excel spreadsheets that take some work to convert. It also makes the data available in nicely formated CSV files. This dual format addresses the needs of most users and should be a standard practice. If you need to know why well-structured data is a cornerstone of data analysis, Hadley Wickham’s Tidy Data (PDF) is an essential reading.
I’m assuming that there will be no problems with concepts when using the data, but that’s not always the case. For example, the USDA publishes data on food availability. According to the USDA, this is a good proxy to actual food consumption, making it a great source for analysing changing patterns in the American diet. Your project should explicitly explain this and make sure the audience knows the difference. Often you’ll have to manage less benign assumptions.
Final tip: I wouldn’t see data preparation and data usage as two distinct steps. You can learn a lot about/from the data during the preparation stage.
There are two things that you should consider: first, the graph itself, and second, the graph in a specific situation. Think of a line chart: Spotting a trend is very easy. A table with the same data will tell you a few interesting things, but becoming aware of that trend will be much harder. Data visualisation is, to some degree, like a cooking robot: It will not make you a better cook, but it’ll take the table and optimize some of the more time-consuming steps, allowing you to focus on result.
In other words, data visualisation preprocesses the data, offloading some cognitive tasks and allowing the brain to focus on higher-level tasks. If you want to design an effective graph, you should select the graph type and the formatting options you believe will maximise this. Never forget that this is situation-specific. People tend to think of “effectiveness” in purely rational terms, but you may want to factor in emotions or a more attention-grabbing design. You should expect some trade-offs.
No chart should be removed from your data visualisation toolkit.
