Mind the Gap, London Underground, Gender Pay Gap

Article provided by Hannah Thomason, Senior Data Strategist, Code Worldwide

My friend and I were reminiscing about our first jobs and I was astounded to hear about how she turned up to her first day and found she was the only woman in the web technology firm.

She also learnt they employed her for her appearance and hadn’t even read her CV! Another friend, an architect, often felt intimidated by her male colleagues due to the “laddy” culture that prevailed in her office.

Whilst I’ve never experienced such shocking behaviour, it made me realise there have been occasions where I’ve been the only female in a meeting room. This never surprised me – I knew before I embarked on a career in data science that it was male dominated, like many other Science, Technology, Engineering and Mathematics (STEM) fields. However, I now appreciate this attitude of acceptance is wrong, and changes are needed to improve inclusivity.

Why do we need more women in data science?

Demand for data science jobs is predicted to grow rapidly, yet just 26% of women occupy data scientist positions in the UK (Better Buys, 2021). Lack of representation occurs early on, with just 35% of women in the UK studying higher education STEM subjects (stemwomen.co.uk, 2021). Those that do embark upon STEM careers, 53% of women leave organizations after 10 years compared to just 31% of men (Stych, 2019).

The data science gender gap needs addressing. Not only does a wider talent pool encourage collaboration, creativity, and innovation; it also enhances staff retention by improving employee morale and reducing churn rates. This results in increased sales revenue and improved competitiveness.

Additionally, mounting evidence suggests women’s viewpoints and experiences are being omitted from development of data technologies. Instead, they are predominantly based on a “universal” male perspective. There are concerns that algorithms will widen gender gaps, with Big Data only telling us half the story rendering machine learning systems invalid and less robust.

So, what’s preventing women from applying for a job in the data industry?

Causes are widely disputed– some believe men have a greater, innate aptitude for maths-based subjects, whilst women prefer to work with people (Wang and Degol, 2017). Others believe it’s due to environmental factors, with societal pressures of a male-dominated workplace culture and lower female confidence levels preventing women from applying (Williams and Ceci, 2012). A 2012 survey conducted by OECD to 15-year-old students highlighted how females feel discouraged due to the “confidence gap.” 41% of girls (vs 24% of boys) surveyed agreed with the statement: ‘I’m not good at mathematics”. It’s no wonder I was the only female in my A-level Maths class!

Inherent cultural biases also affect people’s perceptions around data science.  I must admit, I believed the media stereotype of a data scientist being a “nerdy” male sat in front of a computer all day poring over spreadsheets. Don’t get me wrong, spreadsheets play a part (and who doesn’t love a pie chart?!) but there’s much more to a data career than what the media portrays! Data is innovative, exciting and is transforming the way we live by solving real-life problems. Also, there ARE women in data roles. RAPP’s Marketing Science department is pretty much equal, with 48% women (which is unheard of in the industry!) More needs to be done around raising awareness and breaking down these barriers to allow changes to permeate the industry.

Of those women who DO apply what’s stopping them from staying?

Lifestyle choices play a fundamental part, with fertility and parenting impeding women’s career development. The gender pay gap leaves women feeling frustrated and undervalued resulting in attrition. Whilst the UK gender pay gap for full time workers has decreased from 9% to 7.4% since 2019, it still exists (ONC, 2020). These factors are arguably dominant in all professions – it’s simply intensified in data fields due to the smaller volume of women at the outset.

Lack of female mentorship is also a factor, with 30% of women in STEM roles feeling isolated due to lack of mentorship provision (McKinsey, 2015). I’ve been lucky in my career to have been exposed to amazing female line managers who have provided guidance and support. But I know I’m in the minority.

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So, what can be done about it?

The problem needs to be addressed at the source, with the UK government investing heavily in improving gender diversity of STEM subjects within schools and universities. As a result, there’s been a 50% increase in the number of women accepted onto STEM related degrees between 2011 and 2020 (gov.uk, 2021).

Initiatives like The Alan Turing Institute connect women within data science, generating a like-minded community that shares valuable resources to develop their data careers. Creating partnerships with such organizations will demonstrate businesses genuinely care about achieving gender diversity.

RAPP is an example of how businesses can close gender gaps by fostering an open, company culture that champions diversity and receives strong commitment from senior management. Internal initiatives such as DISCO (Diversity and Inclusion Steering Committee) promote an intersectional mindset through education, raising awareness and celebrating diversity. They also offer flexible working practices which is instrumental in helping both men and women stay in employment when they have young children.

Despite positive changes, there’s still work to be done. Recruitment processes should be non-biased and anonymized, and junior female data scientists should be linked with senior mentors who have the experience to provide encouragement during critical points in their career.

What does the future look like?

Drastic changes are happening to address the gender imbalance within data science. Whilst it’s noticeable that more women are coming into the industry, it will take time for it to filter through – especially to the more senior roles.

The gender gap is a serious issue and if ignored will compromise economic and sustainable growth. Indeed, it’s predicted that improving gender equality within STEM industries could improve global GDP by $12 trillion over the next four years (McKinsey, 2015). We have much to do to get there, but it’s possible.

Hannah ThomasonAbout the author

Hannah Thomason is a senior data strategist at Code Worldwide and has been with the RAPP Group for almost 6 years. In her role, she helps clients (including IKEA) with their audience targeting strategies and performance analysis. Hannah is also studying for a Masters’ degree in Management and Leadership at Cranfield University. Her thesis is about how RAPP Group can improve its recruitment and retention of women within data science.