Tableau tricks: Adding colour to geomaps by continent or region

Tableau is a great tool for data visualisation. One major selling point of the product is its excellent mapping tools which make building visualisation fun and interpreting data a hell of a lot easier than in a flat table.

Recently, I was attempting to replicate a neat visualisation I saw on the Guardian’s data blog. Simply put, I wanted to measure some data by country but colour code the data by region as well. A trip through Tableau’s detailed online help and forums only turned up solutions that were either way too complicated or not quite suited to what I was chasing.

Essentially, I just wanted to map a variable by country and then colour those variables by the continent or region the country belong to. So, for example, all data points that represented a country in Asia got a particular colour, whereas any data points in the Americas got a different colour again.

I’ll freely admit that my skills in Tableau are developing. I spent about a day researching the issue until I stumbled across obvious help in the Tableau mapping tutorial. The dominoes began falling in my head around the 13 minute mark of this Tableau help video and I could complete my task.

The solution is remarkably easy.

First bring across the variable you want to measure in Tableau on a geomap. Using Tableau’s example Superstore dataset, I’ve brought the state variable across into the main view.

Second, you just need to simply need to drag across the variable you want to colour by. Using the same Superstore example, I’ll drag region on to the colour mark.

Now, you should see the different regions of the United States distinguished by different colours.

From there, you can then do all sorts of fun things like adjust the size of the bubbles by other variables. I just have to move the appropriate variable from the data pane on to the size option in the marks pain.

Here’s me using the sales variable to control the size of the circles above (also I’ve put a border on the circles for aesthetic reasons).

Now, to do this in terms of global data, you simply need a way to link countries with regions. If you have a dataset with a variable like countries, all you need to do is map those countries to a region and include that data in your Tableau project. This can easily be done using a standard UN country/region dataset.

Philip Burger has handily made one suitable for Tableau available via his website (nice one Phil!).

Importing the dataset into my Tableau project means I can link it up with my source data and begin the process of making a cool looking geomap visualisation similar to the one used in The Guardian example above.

Linking my tableau output data to the UN data set based on country. I’ve used a LEFT JOIN meaning all data in my source table is outputted and only matching country names are returned in the UN dataset.

And voila, by using the technique above, I can make nice looking geographical maps like the image below.

Pretty neat, huh?

 

 

Time to embrace data-driven decision making in Australian international higher education

Hi all, this is an older article from 2015 where I presented on the topic of data-driven decision making in Australian international higher education. It’s slightly out of date now, but I’ve published it for posterity’s sake. Originally published in October 2015.

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Somewhat ironically, I went with my gut-feeling when I argued that most international divisions in higher education in Australia were not making best use of the massive amount of data that our universities and educators have on international student mobility at the 2015 Australian International Education Conference (AIEC) held in Adelaide last week.

Fortunately, no one called me out on that slight bit of hypocrisy. In my defence, I had done some qualitative research on how Australian higher education institutions in international education used business analytics. I had spent a good part of the last fortnight interviewing many business intelligence colleagues around the country, whose day-to-day involve stewing up porridge of statistics, trends and reports on international student movements and management, seeing what they thought about how institutions used their skills and abilities.

While it does seem that there are different levels of maturity of business intelligence support and skills, a general theme rang through all my conversations: that, by and large, international higher education in Australia uses its business intelligence resources for achieving operational results. That is, analytics professionals are used to run reports, publish statistics, create charts and visualisations, usually in a descriptive fashion. Essentially, these resources are devoted to answering ‘what has happened’ and ‘what is happening right now’ type questions.

Now, don’t get me wrong, these are very important questions for a university to answer when it comes to a globally competitive market like international education. But as we see in other very mature analytics industries, such as banking and finance, analytics is capable of being an extremely valuable tool for solving strategic problems.

There is an excellent book by Davenport and Harris called “Competing on Analytics: The New Science of Winning” that essentially argues that those organisations that are successful in certain markets where other competitors are failing are nearly certainly winning by basing their decisions using an analytical and data-driven approach to strategic problem-solving. “Good decisions usually have systematically assembled data and analysis behind them,” Davenport and Harris (2007: 9) argue.

The key is a change in mindset. Institutions and businesses should be thinking about what sources of data or information could be useful in solving business problems. They should be thinking about how they can leverage sources of private and public data to create efficiencies in their practice. Australian international higher education should be thinking about data as a method for answering question like “what will happen?” Such a mindset will inevitably lead to better outcomes for individual institutions, but also raise the bar when it comes to competing with overseas education powerhouses such as the United States, the UK, Canada, and up-and-coming educational hubs such as China.

Say you’ve got a problem with identifying new market opportunities. Your strategy calls for new methods of quickly locating and evaluating new geographical areas for potential international students. Analytics can help. You could ask your BI professional to investigate your data set for key variables that may influence enrolment outcomes. You could segment or break up your data to check where you’ve had a lot of success in the past and use that information to apply it to candidate markets. Really – the devil is in the data.

What I’m trying to say here is that data-driven decision making mindset has a lot to offer international education in terms of driving strategy. You’re not making best use of your resources should you simply rely on analytics to fulfil only operational objectives.

If you’re in a jam when it comes to marking a strategic decision, perhaps give your analytics department a ring (assuming you have one – if not, really you should be seriously looking at that issue!). It might be the best decision you’ve ever made!

[1] Davenport, T. Harris, J (2007), “Competing on Analytics”, Harvard Business School Press, Boston, MA.