DataViz Confessions

About

This work is our official submission for The State of the Industry Survey Challenge 2022 by Data Visualisation Society.

Behind every captivating data visualizations lies the story of a skilled practitioner who meticulously crafted that story out from raw data. This project is our attempt to delve into the narratives of these practitioners, unveiling their journeys, inspirations, and the challenges they encounter along the way.

We have arranged the stories of these practitioners into three key components:

The Confession: Stories of individual data viz practitioners and the challenges they faced while working with other people on data visualizations.
The Individual: Details around the role and geographical location of the data viz practitioner confessing their experiences.
Fact: We analyze and highlight how many respondents shared similar confessions, adding a layer of context and comparison.

As you explore these three key components, you will be immersed in the rich tapestry of experiences that data visualization practitioners encounter daily. Each story provides a unique glimpse into the intricate process of transforming raw data into captivating visual narratives.

By highlighting these stories, we seek to create an immersive experience for the reader and seek to foster a sense of connection and empathy, emphasising the journeys taken by practitioners (both confessors and reader) and around the world while creating beautiful work.

This work is produced by 36truths. We are a data strorytelling studio from India. You can reach out to us at hello[at]36truths[dot]com.

Credits:

Illustration assembled from Open Peeps
Fonts: Inter: Google fonts

Methodology

Background

The decision of making "data viz" a key pillar of their careers arrived very differently for both Arpit and Hamsa (creators of this work). Arpit, a statistician by trade, was working in the social development setting trying to convince stakeholders the importance and impact of his findings. Hamsa, a graphic turned web designer, found herself using more and more data over time to validate the arguments for her design decisions. Though both of them started on different ends, they found data visualisations to be an effective communication tool to create impact through their work. Their own journeys of storytelling through data made them realise how there is learning from everyone's unique journeys and how these shared experiences in different worlds bring us closer as a community.

Approach

As we started exploring the data, we found the qualititve response sections of the survey to be one of the richest pieces of information in understanding the state of the data visualisation industry. And what excited us the most was learning from the challenges survey respondents face in their data visualisation journeys and their approaches to overcome them.

Our focus was using these qualitative responses to highlight individual stories in the dataset. These responses bring forward the human aspect of the people behind the numbers while also enabling the reader to identify with the personality behind the data at an interpersonal level. The quantitative responses in the data acted as a guide towards finding a diverse set of stories to tell in our submission.

You can find the detailed dataset along with the methodology used and relevant summaries here.

Steps

The data preparation for the final visualisation was done through the following steps:

Fields representing the role, pay, and challenges faced by the respondents were shortlisted from the master dataset to explore further. [Sheet - 2. scope; Columns - A:H]
The responses in the "DataVizNotUnderstood__" field were categorised and grouped under 9 different types of challenges highlighted by the respondents while working on data vizzes with other people. [Sheet - 2. scope; Columns - I:R]
For each of the 9 categories generated based on responses to the "DataVizNotUnderstood__", four qualitative responses each were handpicked to represent each category with a focus on diversity of responses, role and location. The qualitative responses were edited for clarity and brevity. [Sheet - 3. final; Columns - I,J]
To support the qualitative responses, details around the role and location were used to attribute the quote to build a persona of the individual corresponding to the survey response. [Sheet - 3. final; Columns - H]
Finally, a supporting data point was added highlighting how many other individual respondents have faced similar journeys, coming from different career perspectives. [Sheet - 3. final; Columns - K]

Limitations

We recognize and acknowledge that we were not able to solve for the following challenges with our submission, mostly due to limitations around time available before submission date.

In our categorisation of the responses to the "DataVizNotUnderstood__" question, we may have not covered all possible categories of responses, some of which might have a high number of survey responses.
We have refrained from using percentages and have stuck to absolute numbers to represent any data point from the survey responses. This is to recognize the inherent bias in survey response population and ensure we are not misattributing the quotes selected to any major trends in the dataset.
The approach to selecting qualitative responses in English is, by some extent, exclusionary in design. This approach may miss out on key information in quantitative/categorical responses by survey respondents for whom English might not be a language of choice or comfort.

You can find the detailed dataset along with the methodology used and relevant summaries here.