On Day 4, we had to prepare a visualisation without using Alteryx and were challenged additionally to focus on finding interesting stories in the data. We had access to data from the World Bank which covers topics from international economic and population trends, to poverty and gender inequality. I started out exploring development indicators, particularly to do with access to health and sanitation facilities. But I realised there was also some data on disease incidence and decided to examine the relation between these elements.
Here were the key learnings from today:
1. Finding a story and scope
One of the main challenges for me was narrowing down fields from the thousands of metrics in the available datasets. There were so many possible perspectives and ways to bring the data together. I initially wanted to compare whether development metrics (such as immunisation and nutrition, as well as sanitation) affected the incidence of different diseases. But as the day went on, I realised that for the sake of coherence and clarity, I’d be better off focusing on one disease. Also realistically, if I wanted a dashboard by the end of the day, I needed to narrow the scope significantly. Once I had decided to focus on malaria, data prep also became much easier, because I knew exactly which fields to keep.
2. Data prep
I was initially intimidated by the thought of not having Alteryx on hand, but Tableau Prep got the job done just as easily. The data was relatively clean, so the main task was getting the data into the correct format. Once I got my head around the required joins, unions and how to configure the pivoting, the data was ready to be visualised.
Here is my workflow below:
3. Telling the story
To help me form a coherent story, I actually started out doing some research in scientific literature alongside my data prep. Across the board, there was a focus on two key preventative strategies: access to clean water and good hygiene, and insecticide treated mosquito nets. So while sampling the dataset, I tried to categories metrics into three key groups: disease incidence, access to sanitation and physical protection. These three areas then formed the basis for my dashboard structure.
This challenge also allowed me to experiment with using the long format. It pushed me to be more intentional with the flow and sectioning of my visualisation. I wanted to take users from a wider, global perspective, to then drilling down on particular countries and health determinant indicators. To further enhance understanding, I would like to go back and add in some more textual analysis, and some points of interest to guide the users through.
Here is the product as it currently stands. Click here to interact with the full viz.
Image source: Freepik
