Women in Data

| August 1, 2022

Women in Data

Whether it is in school, the workplace or daily life in general, it is no secret that women are a group whose challenges grow and evolve daily. The obstacles they face are no different in the world of data, where women face major inclusion barriers.

A report issued by the 2020 National Center for Women and Information Technology (NCWIT), concluded that in the world of data literacy, women are 57% of the workforce, but hold only 26% of data and analytics jobs. Worse, representation steadily declines for more senior roles, and among technology firms, women represent just 17% of the C-Suite, or the executive-level managers within a company.

While it is clear there is a huge amount of progress to be made for women in data, there are promising statistics. A Burtch-Works study conducted in 2020 found that the number of women in data scientist roles is on a slow, steady rise. Compared to 2015, where only 15% of data scientists were female, 2020 boasts a 3% upgrade with 18% of scientists being women. This study also found that the largest percentage of women in data science roles are in the entry-level individual contributor category– which leaves tons of room for growth as these beginners rise through the ranks. These statistics show us a possible increased presence of women in the field and the beginning of more women in leadership.

Another positive statistic regarding women in data is the diversity among women subgenre. The 2020 NCWIT report found that women in the computing workforce are more ethnically and racially diverse than men in the field. There is a higher percentage of women who are African American, Black and Asian holding computing roles than men.

Overall, gender and ethnic diversity in the workplace are extremely important in reducing bias and making a difference in the field. A study done by Columbia University shows us that diversity in the workplace can even improve quality of the work produced – and while diversity reduces bias, it also leads to higher innovation revenue, according to an earlier study from BCG. According to this same study, 73 percent of women entering the data science and machine learning field prioritize tangible impact in their career choice, compared to 50 percent of men. This statistic, along with many more, is a perfect example of why we need to prioritize and recruit women in data – simply put, they have the initiative to change the world.

Other references for Women in Data Groups

Also check out the people and organizations we follow on our @calstate_dafanh Twitter. Lots of good sharable stories and resources!

About the Author
Emma Mitchell is an Assistant Account Executive for the Tehama Group Communications Department of Journalism & Public Relations California State University, Chico E-mail: tehamagroup@csuchico.edu Web: http://tehamagrouppr.com/