Dashboard Week


Dashboard week is an infamous week marked on every Data Schooler’s calendar where, for every day, you’re given the task of creating a new dashboard and writing a blog for that day. I missed my cohort’s Dashboard week, and have since been catching up whilst (attempting) to adhere to the strict one-day time block. Here’s my work for the third Dashboard week challenge, in which everyone had to create a Museum of Modern Aart (MoMA) Dashboard. Click here to see my previous challenge.




Day 3’s challenge involved using a dataset that describes MoMA’s acquisitions over the years, their current pieces on display. The dataset used can be found here.

Data Preparation


There was no preparation done in Alteryx for this challenge. The only field that I calculated in Tableau was the age of the art piece at the time of acquisition, using the DATEDIFF function between the year of the piece and year of acquisition.



I didn’t have a direction initially, and so spent some time building charts and seeing if there were any immediate underlying stories. My first angle was to compare the ‘still’ to the ‘moving’ image, referencing discussion in the art world of how still imagery (photography) transitioned to the moving image (film) during the mid 20th century. This exploration was cut short as I realised there wasn’t enough detail to properly describe this insight.

I decided to instead compare acquisitions across MoMA’s 5 most popular classifications, and see how these differed across metrics like the age of the art at acquisition, different media used, nationality of artists and if pieces were acquired posthumously. There was some googling involved in explaining spikes in acquisitions, and so annotating the time series was important. I decided to try a graph I hadn’t used before and created a jitter plot for the age of art at acquisition. The whole dashboard is able to be filtered at the classification level by clicking on a point in the line graph.

To explore this dashboard, visit my public Tableau profile here.


Joshua Verbeek
Author: Joshua Verbeek

Josh completed a Bachelor of Psychological Science in 2021, with a focus on research. He discovered a great passion for analytics through the degree. Following graduation, he joined a Market Research graduate program working in data engineering. His decision to join the Data School resulted from a desire to perform analyses, gain insights and communicate results in a visually engaging way. Having previously studied and worked within Graphic Design, Josh is particularly interested in the way data is visualised and presented.