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Dashboard Week Day 3 – MoMA Acquistions

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

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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.

 

Introduction

 

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.

Dashboard

 

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.

 

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