Has the role of women been underplayed in the history of data science? Do women have the same opportunities as men in the field in 2019?
The Women In Data campaign by the Data-Driven Innovation (DDI) initiative launches in Edinburgh next month.
What is the aim of the Women in Data project?
Historically and currently, the contributions of women to data science, innovation and STEM [science, technology, engineering and maths] more widely have been under-represented. The project aims to raise the profile and reveal the landscape of what women are doing in Edinburgh in data science and related areas.
Why is the project so important?
Despite significant barriers to women in these fields in terms of representation and equal opportunities, women are thriving, and the project sheds light on the cutting-edge, expert work they are doing every day. But, there is still lots of work to do in terms of girls taking up STEM subjects at school, then following through into higher education and employment.
Girls actually outperform boys at school in STEM – contrary to the stereotypical beliefs that boys’ brains are ‘naturally’ better at it. However, girls report significantly lower confidence.
Despite initiatives and campaigns for women and girls to go into STEM subjects, we are seeing next to no improvements. There has been a drop in female applications in
areas like computer science, and the national gender pay gap remains rigid.The FTSE 100 STEM companies have around 10 per cent women CEOs, lower than other non-STEM companies.
We want to see these efforts translate into women going into data
science and related careers, and perhaps more importantly, staying in them and progressing to positions of seniority, which is impossible without workplaces free from discrimination and genuinely supportive of diversity and women succeeding. Encouraging girls into STEM isn’t enough, and places the burden of change solely on women rather than employers and men with influence. It is a collective responsibility.
How was the project carried out?
From January 2019, I conducted almost 60 interviews with a wide variety of women involved in data science, artificial intelligence, technology and related areas, at
different stages in their career, at different levels of seniority, from students through to CEOs and MSPs. The diversity of roles that relate to data and data science was quickly apparent – participants come from academia, astronomy, policy, education, industry and politics. It was mind-blowing to hear about the technology and data sets women are using and the huge research projects and teams women are leading.
The interviews will be hosted as magazine-style features with professional photography on the Data-Driven Innovation website, with snapshot profiles for digital and social media use. The project will be formally launched at The Scotsman’s conference Doing Data Right on 10 September.
Women have always been inventors, scientists, innovators and worked in data through history, but their contributions haven’t always been recorded and do not carry the same prestige or authority as work done by men. History captures stories of men’s lives, creating assumptions that women have been absent rather than invisible, in innovative and scientific work.
We have created a profile on historic women in technology, data science and related areas, which draws on the university’s archive collection. This includes the Bletchley Park women code-breakers who shaped intelligence as we know it today, the ‘Edinburgh Seven’ who fought to study medicine in a climate of horrific discrimination, and the first women computer scientists at Edinburgh.
What did you ask the interviewees and what key themes emerged?
We focused on two things: their day jobs and their opinions on gender equality in the field.
What emerged was an overwhelming enthusiasm to talk about the cutting-edge, highly-skilled work women are doing in their day jobs. When I started the project, I was inundated with contacts and suggestions. I have only captured the tip of the iceberg of women’s achievements and scratched the surface in terms of the complexity of gender equality problems in their fields. Women are ambitious and thriving in these roles, despite statistics and workplace models often being stacked against them.
In terms of qualitative insights, there are differences of opinion of what women see as the main problem. Some regard it as a skills gap issue – women are socialised into and steered towards specific school subjects and careers and the challenge is upskilling women and girls, building confidence and delivering more initiatives to keep that STEM pipeline going, to get women into data science and related professions, and then get them to stay.
Others think the main problem is a toxic workplace culture which normalises historical male dominance and bases the workplace infrastructure on a male world view. A STEM ‘Me Too’ emerged from the wider ‘Me Too’ movement from 2017, revealing also that sexual harassment is prevalent. Some say the solution does not lie with upskilling, as girls and women are highly competent and suited to STEM, but rather changing the culture to value the ways women work and encourage women to enter and stay. Change lies in women not being subjected to discrimination or higher workloads, or being overlooked for promotion, sponsored less or paid less than male counterparts. Currently, STEM workplaces are not always right for women – rather than women not being right for them.
There was mostly consensus that a diverse workforce is essential for STEM because a plurality of world views and experiences are essential for innovation. Most agreed we must do more to support girls and women to go into data science and technology as there are well-paid, creative, international jobs available with fantastic opportunities.
There is a concern about representing the problem in a balanced way, too – moving away from ‘victim’
narratives, recognising systemic barriers to women are real issues while also demonstrating women are succeeding and thriving.
Some participants had very vocal attitudes towards gender equality agendas, others have a ‘get on with it’ attitude or are more interested in the issues from a diversity of talent and skills angle.
It’s understandable, as participants come from different walks of life, have different experiences and a number have operated for a long time in a male-dominated context where women need to fit in with the existing workplace’s terms to succeed. There is no one way we experience the workplace as women.
What surprised you?
I thought the answers would be more homogeneous. Everyone generally agreed that we need diversity in STEM. There was a lot of vocal criticism of workplaces, which many of the women said they try to voice openly. There are still simple things we get wrong – for example, it is not acceptable to have such low levels of women in senior roles in data
science and related disciplines.
Workplaces are not doing enough to recruit women in the first place by considering the factors women consider when they make job
application decisions, including the language employers use in job adverts and promoting opportunities that matter to women. Women are often not supported to rise into senior management and can be the first to lose their jobs.
It’s not just about leadership or recruitment, it’s a combination of factors. It’s everything.
I was also surprised at the lack of confidence among women doing
revolutionary things in their day jobs. They are working in highly-skilled, transformative roles and sometimes defy the odds to get to where they
are – but they often apologised for taking too much of my time and were anxious about potentially taking up a place on the project that could have been taken by another woman.
They were sometimes anxious their work was not quite comprehensive or cutting-edge enough to be included in the project.
Younger women were bolder that their rightful place is within STEM – you can observe the changes in attitude and climate.
To achieve gender equality in data science and STEM, I think the
chronic lack of confidence among women needs to be addressed.
What next generally?
By raising the profiles of women across a diversity of sectors and industries in Edinburgh, the campaign aims to provide an alternative perspective to tired, old tropes that women don’t belong in data science and technology.
The campaign element hopes to show girls and women that people ‘just like them’ go into these roles. It’s not about being a ‘genius’, or getting a specific degree. Women who didn’t go to university, or did a social science or humanities course, are climbing up the STEM and data
An automatic equation is often made between data science and academia, but there is a huge range of roles. You can be a scientist, an educator or write policy. There are areas that are more socially and politically-conscious, which women can be more attracted to, using data and technology to help answer pressing social problems. However, equal pay and equality of workloads is an issue in more feminised elements of this spectrum.
The mere presence of the project intends to create internal and external conversations about how we genuinely ‘do data right’. Women in Data isn’t about political correctness; its message is evidence-based. Inclusivity is directly conducive to
business success and innovating in ways that are effective, sustainable, environmentally-conscious and intelligent. If you want your innovation projects to succeed and you want your workforces to be happier and more productive, you need women. If not, products and services will be designed in sexist ways and will not be cutting edge! Modern projects and problems need modern workforces.
Will more work follow on from the project?
I’ll produce a report to contextualise the interviews in wider research on the Edinburgh and Scottish context. We are also looking to create a Data-Driven Innovation events framework to ensure gender equality goals are embedded in everything we do, rather than being tackled in separate projects. One of my colleagues is working with Equate Scotland to recruit an inclusivity and diversity expert to work on the data science education programmes run by DDI.