Today marks International Women’s Day (IWD), a global celebration of women’s achievements in all domains, including social, economic, cultural, political, and technological. Started in the early 1900s, IWD continues as an annual reminder of women’s capabilities and a call to action for gender parity. Tech, specifically, is an area where women are far less numerous than men, but opportunities in the growing field of data science could now bring significant change.
Women have played many essential roles in technology during the past. However, public recognition of their skills and contributions has not always been forthcoming, as the 2017 movie “Hidden Figures” shows. Notable female figures from the past include Ada Lovelace, the 19th-century mathematician who wrote down one of the first computer algorithms, and Grace Hopper, who invented the first compiler for a computer programming language.
The Decrease of Women in Technology
Melinda Gates, co-founder of the Bill & Melinda Gates Foundation, is concerned about not only the lack but also the decrease of women in technology. In her words, “We’re graduating fewer women technologists. That is not good for society. We have to change it.” She points to a sharp drop in the percentage of computer science degrees awarded to women, declining from 37 percent in the 1980s to 18 percent today. The National Center for Education Statistics and The Washington Post confirm the latter statistic, adding that almost half the women are graduating with engineering degrees never work in their field or leave it soon afterward.
Can Data Science Save the Day?
Data science could reverse this career trend for women. Unlike the figures for computer science degrees, women now account for 40 percent of graduates with degrees in statistics, a fundamental building block for working as a data scientist. Not only is it one of the fastest growing careers in the world, but it is also opening unprecedented opportunities for women. Also, advances in software platforms are helping to shield data scientists (women and men) from the more abstract intricacies of coding, which may help lead to the diversification of hiring. However, there are other challenges in the job market that must be addressed.
Diversification in Hiring as a First Step
Businesses need to diversify as they employ people to work in data science specifically, and in tech in general. Employers should focus on real aptitudes for data science, rather than select staff that corresponds to preconceived ideas of what a data scientist should look like. Being objective and using gender-neutral “blind practices” when hiring, defining assignments, and offering incentives and promotions, is also likely to work for the employer’s benefit in finding and keeping the highest quality workers, as well as encouraging gender parity. While technology is a crucial enabler for all sorts of business activities, its potential will be stunted if businesses do not break out of traditional hiring and workforce patterns to become more inclusive, across genders and other characteristics of the total population willing and able to achieve results.
Beating the Built-In Bias of the System
One of the biggest hurdles to this inclusion is a built-in bias in the data processing and tech sectors. With most workers and leaders in these fields currently being men, rules, resources, and opportunities tend naturally – but not necessarily justifiably – to reflect how men, rather than women, think and behave. This leaves women at an inherent disadvantage, while men continue to use the system they have created, often in a state of blissful ignorance. But as Einstein noted, the same thinking that creates a problem is unlikely to lead to an answer. The solution here starts by introducing diversity into the industry as early as possible with corresponding changes in the content and formats for education and hiring practices.
Top Roles for Women in Data and Tech Too
The solution must then also extend to enable women to assume higher-level positions in data science and tech, based on their abilities, including access to top positions like CEO. There is preparatory work to be done here as well in reshaping opinion with facts, instead of assumptions or prejudices. A Rockefeller Foundation survey found recently that general opinion is considerably more likely to blame female CEOs for problems in an enterprise, compared to male CEOs. The survey studied recent news stories and found that when enterprises were in difficulty, 80 percent of the stories blamed female CEOs, but only 31 percent of the stories blamed male CEOs. However, hard data tells a different story, for instance, Fortune.com recently showing that “Fortune 1000 companies with female chiefs outperformed the S&P 500 index over their respective tenures.”
Use IWD as a Marker for Progress in Women Working in Data and Tech
Data science may be one of the best opportunities around for women to contribute and achieve status based on their skills, not their sex. Enterprises and organizations are scrambling to find data scientists, and are forced to be pragmatic in their search. They now look for candidates with the special mix of skills needed to understand, manage, and analyze data, instead of scoring applications based simply on past career positions and titles. By the time International Women’s Day comes around next year, let’s hope that this leveling of the playing field has also led to a more balanced cross-section of women and men in data science (and tech in general) and that the trend continues rapidly towards an equilibrium that is positive for all.