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What are some of the potential drawbacks to the gender imbalance in data science?

Answer
By Akshita Puram | Last updated: February 14, 2022

There are lots of drawbacks. Data science is still mostly a male field – hard to advance, hard to get equal pay and opportunities. In fact, it has been shown and backed by much research in the industry that women make less than men: about 75¢ compared to the $1.00 a man makes, and for women of color it is even less, sometimes as low as 55¢ compared to the $1.00 a man makes. In addition, women find it harder to move into leadership and executive roles. Women also struggle to get on company boards. However, it has been proven that when women are in leadership roles and also on company boards, they improve revenue for the company considerably.

I did a dissertation focused on this specific topic. I studied the significant factors of why some women excel in leadership roles within information technology. I surveyed over 200 women, and I found the most significant factor that really propelled women into leadership roles is the factor of sponsorship. Sponsorship is key and it is different than mentorship. Sponsorship is advocating women into these types of leadership roles. (Also read: 5 Ways to Support Women in Your Tech Company.)

The good thing for data science is that it is a newer field so there is room for changes to be made. Promoting women and also having women in technology has a positive impact on the technical fields. There are so many famous women who have made a difference in technology. Women just need to be given a chance. Look back on history and see the impact women have had on computer science, astronomy, biology, etc.

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Written by Akshita Puram

Akshita Deora Puram is a subject matter expert and evangelist in digital transformation and software testing. She manages the software testing portfolio at SmartBear, which includes award winning tools such as TestComplete, TestLeft, CrossBrowserTesting, and Hiptest. She loves to talk to software quality teams on adopting practices for an agile world, including AI-driven UI test automation, shifting left, and behavior-driven development. More recently, she walked hundreds of QA engineers and managers through how to adopt shifting left and measure their ROI for automated testing. Akshita has over 10 years in the software technology industry working as a system architect, tester, and IT consultant. She has an MBA from MIT Sloan and has also been published by SD Times, CDOTrends, DZone, TheNewStack, Cucumber, Capgemini, MIT Initiative on the Digital Economy, MIT Sloan, and SmartBear.

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