We’re likely all aware that men disproportionately outnumber women in the data field. Women constitute just 26% of the global data and AI workforce; there is only one female data analyst or scientist for every four of her male counterparts. This is a worrying statistic, considering the significant role that data plays in our lives. In order to avoid bias in data models, society needs to collectively pave the way for more women to enter the data sector. As International Women’s Day is now upon us, let’s seize the opportunity to address some of the factors contributing to this issue, and examine some potential ways of overcoming it.

The stigma of STEM subjects

A lack of women pursuing careers in data and analytics is a symptom of a broader societal problem. As it stands, women constitute just 14.4% of all people in the UK working in a STEM-related industry. This is a deep-rooted issue that spans all the way back to childhood, as many young girls are implicitly conditioned to believe that they should steer clear of STEM subjects.

Girls opting to avoid studying STEM subjects beyond school level has nothing to do with a lack of ability on their part. In fact, in gender-neutral countries where an emphasis is placed on equality, girls actually tend to outperform boys at school in subjects such as maths and science. For example, in Iceland, according to one study, the maths scores obtained by girls actually surpassed the grades of the boys by 14.5 points.

An invisible barrier has been constructed around women entering STEM. Whilst there is no biological reason for men rather than women to pursue STEM subjects, women are frequently less confident in their abilities than their male counterparts. Despite performing better in class work in science and maths classes, girls typically do not perform as well as boys in an exam setting, as stated in a Guardian article describing the study. As part of a survey by the OECD, when posed with the statement ‘I am just not good with mathematics’, 41% of girls agreed compared with just 24% of boys. We tend to be drawn towards the careers which think we would be most competent in.

This is a problem embedded in educational institutions. This, alongside sexist messaging in media and in society in general, all combine to impose gender stereotypes on children from a young age. When one survey  asked children what they thought the most important traits in girls and boys were, the overall consensus was that the second most important trait for girls to possess was ‘being caring’, which can in part account for the large number of girls who go on to pursue HEED subjects (health, elementary education and domestic). It’s clear how gender conventions can contribute to this absence of women in the STEM sector.

In order to see more women entering the data field and STEM-related careers in general, we need to be encouraging girls from a young age to consider STEM subjects, instilling them with confidence and empowering them to change the narrative.

Breaking down barriers

In order to break down the barriers of entry into the data world for women, companies need to be aware of the importance of hiring female data scientists and analysts. It would ultimately lead to more accurate consumer insights.

Data has an impact on all areas of our lives. And when this data is exclusively in the hands of men, it will naturally be limited and leave out certain important factors and considerations. A diverse group of analysts is needed in order to cultivate truly representative data models.

In a world where 49% of people are women, it is in the interest of companies to have female data scientists working for them, in order to cater to a wider audience. Women can offer a unique perspective on the world of data. In the past, there have been numerous scandals involving AI algorithms and data models giving biased outcomes due in part to the inherent male outlook of many data scientists and analysts.

Companies should also re-evaluate their expectations from people seeking to enter the data field. As it stands, it’s very difficult for recent graduates to meet the expectations perfectly for an entry level data job, as they’re often expected to possess a number of technical skills, some level of experience, and a prestigious degree to match. Research shows that whilst men apply for jobs where they meet just 60% of the requirements, women do not apply unless they match the requirements 100%. This means that more men are taking a leap of faith and receiving job offers in the data sector. In order to break down the barriers for women, companies hiring for data roles should be more open-minded to employing people with different skillsets and from different academic backgrounds, taking the time to train individuals who show potential.

Many women avoid data jobs from the outset due to the negative perceptions associated with it. According to BCG research, 81% of women studying a data-science related subject regard the field as ‘significantly more competitive’ than other career options. The data field requires an image overhaul in order to appeal to a more diverse group of prospective employees.

Increasing awareness

One potential cause of the absence of women in data science is that data roles may not even appear on their radar; they might lack information on what a career in data would entail. Young women should be surrounded by role models in the data sphere who they are able to look up to. This exposes them to the potential paths available to them in the future.

Conferences and events hosted by organisations dedicated to seeing more women in data roles (such as Women in Big Data, Women in Data and Women in Analytics, to name just a few) play a crucial function in cultivating a community of women in a male-dominated industry, who can motivate each other and increase awareness of data roles. These events allow women to share their experiences working in the data sector and inspire others. They’re platforms dedicated to the championing of women in data.

By hosting inspiring, motivational and informative events, these organisations inspire women to seek opportunities in the data industry.

An important feature of many of these organisations is that they are not open exclusively to women. Equally as important to the cause is male allyship; men working in the data field should also be supporting their female counterparts.

Despite the current gender inequality in the data sector, things are moving in the right direction. As long as society works to shine a spotlight on the issue and pinpoint potential solutions to this imbalance, positive change could be on the horizon, but it will not come without an institutional overhaul and significant attitude shifts from those already established in the data sector.