For the final day of Dashboard Week, we had a shorter challenge which involved analysing data about US museums. The data was relatively straightforward to prep. I used Alteryx to create some spatial points and clean the data. Then for my dashboard design, I went with a simple view that allows users to select a state and museum category of interest. I used a set action calculation to tell Tableau to show the state map and category selector only when one state is selected. The steps to create this calculation can be found documented in my previous blog.

Here is the workflow and initial dashboard I produced: 


Secondary dashboard

Since the initial dataset was quite simple, we were allowed to find supplementing data from elsewhere. Finally by the end of dashboard week, I learnt to timebox an exercise! I wanted to focus on producing a clean dashboard before attempting to pull in more data. After completing my initial dashboard, I went back to the datasource and found survey data from US museum visitors. We haven’t worked a whole lot with survey data before so I thought this would be a good challenge. 

The main roadblock for me was in the data prep. The survey questions and responses had been coded with a key so I had to go back and match these with their documentation.

The structure of the documentation pdf also meant these codes could not be just brought straight into Alteryx. So I had to first build my own reference table with a clean structure that could be used in Alteryx. 

Then to map these with the survey responses, I had to transpose data to match each question code with its proper name. Then, the data was crosstabbed back to get a row per respondent and column per question.


I thought it would be interesting to compare museum users’ experiences with the availability of museums across the US. However, the survey data was aggregated at the national level which prevented more detailed analysis. Nevertheless, I created a simple dashboard showing the demographics of visitors and a breakdown of their last visit. (It seems like a handful of people were willing to travel 9 hours to visit a museum!)



And so that concludes dashboard week! It definitely lived up to expectations of being a fast-paced rollercoaster of a week. The most challenging aspects for me were probably pacing myself well under the time pressure element, and having to understand new data and different tools each day. But it also demonstrated how much we have learnt in just a short span of time. This struck me most during Day 4 which was similar in content to the very first viz I published to get into the data school. Thinking back, I was amazed at how much faster it was to produce my dashboard this week, even with added complexity and interactivity.


Image source: (

Danica Hui
Author: Danica Hui