IWD 2024: New paths for women into data analytics
Does data science have a gender problem? The question is worth asking because these new technologies are rapidly embedding themselves into society. The current statistics are not heartening, and we need new approaches to getting women into data.
Women have always lived in a world designed predominantly by men, but these technologies tend to spot patterns of inequality without a sense of injustice. As machines influence decision-making, we need women at the table. Although STEM, in general, has become more welcoming to female staff, data science and computer science have been bucking that trend.
Measuring gender ratios in the workplace is tricky as we don't have good data. The World Economic Forum Global Gender report estimated that around 5-22% of the profession is female. With data science no longer restricted to R&D departments, data analytics is rapidly becoming a vital professional skill. It's not just the numbers that are troubling. Looking at recruitment data, it seems that only 10% were in senior positions. This corroborates other findings that suggest that a dropout in women working in tech is around the age of 35. At the other end of the career spectrum, when women enter the profession, the numbers are no better. In education, where we have accurate figures, in my country, the UK, for example, in 2013, 47% of ICT candidates were female. Fast forward to 2022 and the number dropped to just 21%. While it has improved slightly, we are not going to be seeing a rapid increase in women coming up through the education system any time soon.
Some solutions are unconventional but may help restore some balance to the field. Many countries are now embracing the concept of a mid-career reset. With delayed retirement now the norm, few careers can continue on a linear course over decades of social change. Just last month, the Singaporean government announced funding for mid-career retraining for workers over the age of 40 to go back to university and learn new skills.
Other countries have sought to plug the skills gap in data science with conversion courses, taking people with domain knowledge of accounting, insurance or other professions with a strong female presence today and upskilling them. Through these conversion courses, we are beginning to see more women walking around computer science departments. There is, of course, a catch. Women in their 40s may well already be overburdened with responsibilities in the home, with care duties both to their older and younger family members, as well as careers. But there is an opportunity for women to start making their way into the data workforce.
The benefits of female mid career transitioners are many. They may well already have the leadership skills to head up teams and have honed their communication skills. They will be aware of regulatory environments and business operations, and while they may never become Silicon Valley tech wizards, this balance of skills and knowledge is very much in demand at the moment.
It is a myth that data scientists have to be registered in a computer science training program at the age of 18, and if we accept this myth, we will continue to see unbalanced workplaces for many years to come. We may disagree over the reasons, but young girls have opted out of computer science undergraduate degrees. There are so many problems in the world that those young women care about that they could start to fix with the new superpower of the workplace in the 2020s and beyond - data science.
There are now ways into data science through software and gentle coding tasks. At the Institute of Analytics, where I oversee data science training, I find no gender difference in the way trainees respond to the interpretation side of data analytics. When we crunch some data and get a number out, that is the first part of the process. We always need to contextualise that number in context and consider many nuances before making a decision. This mix of the rigour of analytics, with the nuance of business skills are the qualities that companies need.