Data Science: Women Needed

Anna Johansson

October 18, 2020

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There's a noticeable shortage of women in the data science field, and it's time we started addressing it. Only 15 percent of data scientists are women, and female data scientists tend to go unnoticed and uncelebrated. But why is this the case, and what steps can we take to give women more of a presence in the field?

Data Science in the Workplace

First, it's important to understand the increasingly vital role that data science is playing in businesses and organizations. Business leaders and decision makers are beginning to understand the value of collecting and using data for decision making, relying on feature-rich data dashboards to learn more about their customers, their operations, and their business environment. With better data science, they can foster better innovation, operate more efficiently, and ultimately trounce their competition. Accordingly, data science is only going to grow in influence in the coming years.

The Value of Women in Data Science

So why is it so important that we have more women in data science?

First, there's the "diversity of thought" angle. When solving tough problems and coming up with new ideas for an organization, it's important to have a diversity of different minds working together. Minds that come from different backgrounds, have different identities, and think through issues in different ways will come up with very different ideas - and very different conclusions. Women will likely see data sets and analyze them in ways that differ from men, and when working together, a mixed workforce can get closer to the truth than a gender-exclusive one. This is one reason why organizations that are gender diverse tend to be more productive and more successful than their non-diverse counterparts.

Second, there's the inspiration angle. If more women take on prominent data science roles and push the field for more advancement, they're going to inspire other women to do the same. They're going to set a tone for an entire generation, and possibly encourage women everywhere to choose a more valuable and interesting career path.

Why Aren't Women in Data Science?

Gender discrimination in the workplace is illegal in the United States and in most developed countries, so what accounts for the existing gender discrepancy?

There are a few possible explanations:

- Lack of existing representation. First, there's a kind of self-perpetuating feedback loop in play. When there aren't many women to serve as representatives in a given field, young women are naturally steered away from that field as a potential profession. In this case, not many women are data scientists, so not many women are inspired to become data scientists, continuing the cycle indefinitely.

- The "boy's club". There's also a perception that data science (and other tech fields) are a "boy's club," where male leaders and thoughts are dominant. Women don't want to enter a field where they're going to be perceived as inferior, or where they're going to be intentionally excluded.

- Subconscious biases. Women may also be dissuaded from pursuing data science, or taken less seriously in the field because of subconscious biases. For example, one study found that men pay more attention to the news when there's an attractive female presenter - but they're much less likely to remember what she said. If women leaders in data science aren't being listened to or taken seriously, they're not going to be able to advance - even if their work is impressive.

- Natural preferences. Though it's somewhat controversial to suggest, there's a chance women are naturally less interested in data science than men. There are some biological differences between men and women, which could lead to slightly different career preferences. Of the factors on this list, this would be the most difficult to overcome.

What Can We Do?

What steps can we take to encourage more women to pursue careers in data science and support the women who are already here?

For starters, we can elevate and emphasize the voices of women who are already in the field. If you're looking for a keynote speaker on data science, or if you're doing research in data science, specifically look for women who might otherwise be neglected. At the very least, challenge the biases that might be causing you to evaluate men and women differently.

When hiring, make sure to publish your job descriptions and tailor your recruiting efforts to reach more women. Depending on the nature of your organization, you may also be able to offer data science training and education to some of the women who are already on your team.

Changing the mindset and demographics of an entire field is an exercise that understandably takes many years. However, with enough people pushing and enough motivation, we can collectively change the perceptions and standards that have held data science back for decades.

Opinions expressed by the author are not necessarily those of WITI.

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