How to support gender diversity in data and AI industry

This November we conducted a small survey for women working in the data industry to better understand how the industry is perceived by women professionals, how issues of diversity come across, and in what ways we could support more women to enter the industry, which benefits of talented people coming from different backgrounds. The study was conducted in collaboration with Women in AI Finland and the results of the study released as a part of the Women in AI mentoring program event.

Many paths to the industry

The survey highlighted that it is possible to enter the data industry via various routes. Many of the respondents had worked in a different position/unit or a completely different industry before becoming data professionals. Ending up in the field was often described as a coincidence. It was emphasized that it is possible to find work within the field with different skill profiles and educational backgrounds. In sum, the industry needs new talents and there is no one right way to become a data professional.

“I studied to become a math teacher but ended up hopping into the IT industry and first working on architectures and infrastructures. Then discovered that on the data side of things I could leverage my math skills as well. So ended up doing things that included everything that I had been doing so far in math and IT.”

However, it was not always the case that women ended up in the industry by chance. In fact, many women were interested in data and had aimed for a career in data via technology/IT studies or, for example, a training program.

(Entered the field) ”Through my interest, I applied to the Big Data Specialist recruiting training program. From this, I applied for a job from data consulting company and got to consult on a wide variety of data projects.”

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Excellent employment opportunities

As also this inquiry underlined, there is a high demand for data professionals, and with that the employment opportunities in the field are superb: Whereas new positions emerge, the data-related roles also become more diverse, and traditional business roles go to a more data-oriented direction. Due to the demand of the workforce and different skill requirements, it was seen that the door is now open for women to enter the field.

“At the moment, the situation for employment is excellent. There are many open jobs. I believe that the industry will continue to grow strongly because there are still industries and companies where decisions are not made using data. In addition, data teams will grow in data-driven industries.”

Diverse perspectives are needed

The data industry was seen as a male-dominated field, which, however, shows signs of opening and where women should strive to get into: The industry is well paid and possible career paths are numerous. Diversity was considered an important factor not only in terms of equality but as an enabler of better company performance and creating accurate, unbiased technology products and applications. Having diverse perspectives was also seen as central to problem-solving and innovation. Diversity was perceived to include not only different genders but other minorities and people with different character traits and neurotypes.

“The result of various data processes (whether it's assessment science, A/B testing, an ML model running in production, or something completely different) in some way shapes our environment and our opportunities to work in it. There have been many troubling stories from Silicon Valley about what happens if the developer pool of data-intensive products is too homogenous. For example, it comes to mind that the health meter does not know about the existence of menstruation and face recognition that only works on Western faces. In the best case, even a homogeneous team can adapt and know how to develop for diverse users, but this is more likely to succeed if the team itself is closer in composition to the population.”

“(Diversity is important) so that we do not waste talent because of irrelevant stereotypes, and everyone has a safe and supportive work environment. I've been thinking about this mostly from the neurodivergent perspective. We need to support different working styles. It's not easy for anyone.”

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The importance of role models  and mentoring 

In increasing the attractiveness of the data industry for women, the importance of role models and mentors was highlighted. Other key factors were sharing information, training opportunities, and dismantling myths and stereotypes about the field. For example, coding was seen to be associated with unnecessary mysticism. The technical skills required to act as a data engineer, data analyst or even a data scientist can be acquired and learned, such as any other skills. Nowadays data & analytics and related engineering skills can be learned in various training platforms or universities. 

With that, it would be important to dismantle the perception that the industry is particularly challenging, or one should have specific characteristics to enter the field. In addition, data-related roles and tasks should be better defined to make the industry more approachable. The data engineering and data science is technical in nature but the industry offers also opportunities to more business oriented career, for example by working as a data product manager, business owner, data steward, data strategist or data designer.

To proceed in their careers, the women working in data put value in the support of colleagues, supervisors, and other women working in the field. They also emphasized the significance of mentoring (for example mentor programs), inspiring role models, and experiences of success achieved at work as useful for career advancement.

“Inspiring women have encouraged with their example and made collaboration. Good predecessors, also men who have trusted and uplifted.”

Network, trust yourself and bravely go to out of your comfort zone

Based on our small query, the path for women is now open to the data industry, where diverse perspectives and different skills profiles are needed more than ever. Luckily, there are already pioneers in the data industry who have paved the way for future female professionals. The essential message emerging from the study was to be brave, develop one's skills, and go outside of your comfort area.

“Network, go to your discomfort zone, to ambitious projects and interesting projects. Be uncompromising in how you are treated. Set boundaries and vote with your feet if the alternative is to get stuck and waste your career in an environment that does not promote your growth. Also, find the right partners and do it together. Follow the biggest question or problem you have in mind and use your skills to solve it.”

“You can do it! I could too. Not everyone has to be the same. You will surely find your suitable slot, where your strengths help to produce added value for.”

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Here are some tips that respondents gave for women seeking a career in data…

  • Get to know women working in the data field through LinkedIn or other social networks
  • Meet people live on data related events and conferences
  • Join a women's network, such as the local Women in AI or Women in Tech
  • Get a mentor to help setting a direction to your career development
  • Build your skills through (online) trainings
  • Begin your career as an intern in a company or through changing to a more data oriented role in your current employer
  • Educate yourself through courses in training platforms or by studying computer science, software engineering, data & analytics based on your interest and targets
  • Boost your technical skills through completing certifications, joining hackathons or coding competitions
  • Be brave and believe in yourself and your ability to learn

If you are considering a career as a freelancer data expert, you can also contact us for sparring and help: iiris@rootsof.ai 

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* Survey background

The survey consisted of 11 questions, most of which were open questions. In total 18 female professionals answered the survey, and they represented a wide range of positions, such as Data Scientists, Data Engineers, Data Analysts, Project Managers, and developers. The women worked with various tasks, including data architecture, Machine Learning, DevOps, building digital identities, developing digital services, Data Archiving and Security. The majority of the respondents, 50 %, worked in a consulting company. 22 % worked in the companies’ internal data teams. The IT unit, entrepreneurs, and public institutions were also represented.