Women of Multiverse - Patricia Clarke

(5 min read)

Written by Patricia Clarke, a data journalist at Tortoise Media.

I can’t pretend that my foray into data journalism began with a long-held passion for numbers. I did my degree in English Literature and Creative Writing, and started my career at a publishing house, writing and editing articles for client magazines, mostly on arts and culture. More than anything, I was drawn to the job by the storytelling.

After another job - this time as a commissioning editor at a travel publication - I decided to do a master’s degree in Digital Journalism. I was excited by the idea of digital publishing, and wanted to be more ambitious with the stories I told - both in format and content. I learned to code - some Python and R for analysing data, some JavaScript for visualising it and building out web pages. It all felt useful, but I wasn’t sure how I would end up using it in my career.

And then the pandemic hit. Suddenly numbers were everywhere - from case numbers to medical reports - and I realised how crucial data literacy really is to journalism. My newfound data skills became central to my understanding of a quickly unfolding global crisis. 

I joined Tortoise in October 2020, and have had the opportunity to work on long-form podcasts, our daily newsletter and our indices. I’ve had the chance to do the ambitious, multi-format reporting I always wanted to do, using data to help our audience understand the forces that shape the world.

It was at Tortoise that I met my Multiverse mentee, a young woman working as a Data Analyst who has big ambitions and wide-ranging interests. She has hard skills like coding for data analysis and visualisation, and soft skills - she is very organised, charming, creative and hardworking.

Even for her - someone to me seemed so obviously talented, skilled and employable - data journalism felt like an abstract goal - a job that was hard to get into. 

I’m not particularly surprised that she felt that way. Journalism can be a tough industry to break into at the best of times, and that’s even harder if you’re a woman, harder still if you’re a woman of colour, and even harder if you’re from a low-income background. 

Data journalism in particular can feel bewilderingly male-dominated. Remember that infamous Times article about “smoking-hot data heroes” during the pandemic, that only emphasised the work of white men?

Of course, women are doing vital work in data journalism. I have had the rare - and immense - privilege of working mostly with teams of women at Tortoise. Our data team is led by Tortoise partner Alexandra Mousavizadeh, and here I’ve worked with my fellow data journalist Kim Darrah on stories ranging from the gender pay gap to rape culture at schools. My editor Basia Cummings has also guided me on long-form data investigations into topics like domestic abuse data and spiking. 

These women have lifted me up, improved my work, and given me important professional and personal advice. They have helped me tell stories about issues that affect women, and supported me in pitching stories that really mattered to me. 

For that reason, I think the number one priority for the industry should be to hire more women - especially into senior positions. Women of colour, women from diverse income backgrounds, trans women - this sort of representation can only lead to better coverage for the media, as senior women will understand what people like them want to read, and why some stories are worth fighting for. 

When reflecting on this year’s theme, #breakthebias, I also keep coming back to the idea that data itself is often biased, or lacking. We turn to data for answers, and hard facts, but if there’s one thing that I’ve learned in my modest career it’s that data needs to be interrogated just like any other human source. 

Most of the work I’ve done at Tortoise has centred on this very fact. And actually, I would say that a lot of data-led investigative journalism often ends up being about how data is lacking, or missing. 

I worked on our four-part podcast Hidden Homicides, and an adjacent long read, that highlighted how poorly we understand domestic abuse data and homicide data - and how the consequences of that can be deadly for women. I’ve also told stories about how drink spiking (and the newer phenomenon of injection spiking) are hardly tracked by data, meaning they aren’t properly dealt with as crimes.

Plenty of brilliant women, most notably Carline Criado Perez with her book Invisible Women, have also talked about the unseen bias that can hide away in data and impact the lives of women. 

It might sound odd coming from a data journalist, but I want to see more data being scrutinised, questioned, unpacked in this way. And my hope is that there will be more women doing just that.

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