Women continue to be underrepresented in tech, with only a small percentage of women pursuing careers in this field. Despite the fact that women make up half of the workforce, they are still underrepresented in many tech-related fields, including data analytics.
In every industry, businesses leverage data to drive insights and make informed decisions using data analytics. As the field continues to grow, so does the demand for skilled professionals to extract insights from complex data sets. Despite the fast growth of data analytics, the historically male-dominated field still fails to bring enough women to the table. According to the latest data from the U.S. Bureau of Labor Statistics, women held 27% of jobs in computer and mathematical occupations in the United States in 2020.
Data influences every aspect of our lives, so it's crucial that both men and women are involved in interpreting and applying this information. Women who pursue careers in data science not only contribute to shaping the data-driven world but also reap personal benefits, such as gaining access to a rapidly expanding field with attractive salary options and career prospects. By encouraging more women to join the field, we can create more opportunities for them to advance in their careers and attain financial stability.
Women in Data Analytics
What do Women in Data Analytics Bring to the SQL TABLE … or Dataset, or Matrix?
Diverse perspectives: women bring diverse views and experiences that result in more innovative solutions and better outcomes. With more women in the field, the industry can better address the needs and preferences of diverse populations. Women can also provide insights into issues that may be overlooked by male-dominated teams, such as biases in data or the impact of technology on marginalized communities.
Improved accuracy: women’s perspective can help identify and reduce biases, leading to more accurate and reliable data. Since women are often underrepresented in data, this can lead to a "gender data gap" that elicits biased decisions and policies.
Equality and inclusion: women can help promote diversity in data analytics by encouraging and supporting other women to pursue careers in the field through mentorship programs, networking events, and other initiatives that promote women's participation in the field.
According to Stephanie Brooks, a partner at Harnham, one of the leading providers of recruitment services and advice in the Data and Analytics marketplace:
“The business case for a diverse workforce is clear–research has continuously proven that diverse teams yield better results. A diverse workforce creates a more holistic business; one filled with more innovative products and services, in addition to creating a more stimulating, enjoyable and challenging environment for individuals to thrive in. In order to remain competitive in attracting and retaining the best skills in the market, businesses must explore ways to accommodate and support a diverse range of talent.”
So, why aren’t more women being hired? They are significantly underrepresented in many tech-related fields, including computer science and engineering. According to UNESCO, less than 30 percent of female students focus on STEM-related subjects globally in higher education. Since many women lack visible role models, women find it difficult to not only find mentors who can provide guidance and support, but also forge a clear and definitive path to success. Conscious and unconscious bias in the workplace, manifesting in assumptions about women's abilities or preferences or gendered expectations about the types of work that they should do, can impede their tech goals even further. To combat bias, companies must create a level playing field with equal opportunities for women to succeed.
By using the experiences of leading female voices in data analytics, we can not only inspire young girls to pursue tech, but also pinpoint areas for improvement in the industry.
Female Leaders in Data Analytics
As a data scientist and entrepreneur, Hilary Mason has worked at companies such as Bitly and Fast Forward Labs. She is also the founder of the data science community Fast Forward Labs, which provides consulting and research services to businesses.
The bestselling author of "Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy.", Cathy O’Neil is also a mathematician and data scientist. O'Neil founded the consulting firm ORCAA, which provides expertise in ethical and responsible data science.
Kristen Sosulski is a professor at NYU's Stern School of Business and the author of the book "Data Visualization Made Simple." She has also worked as a data analytics consultant for companies such as Accenture and Ernst & Young.
Tech advocate Erica Baker was a Director of Engineering at Github and is now Head of Technology at Parkwood Entertainment. She’s worked as a data analyst and software engineer at a number of other companies, including Google and Slack.
Ellen Nielsen is Chevron’s Chief Data Officer, ensuring the accessibility and trustworthiness of data to enable better decision making. Throughout her career, she’s taken a data-driven approach to sourcing strategies, organizational capabilities, and supplier relationships.
So, how can you excel in data analytics?
Data analytics involves collecting, analyzing, and interpreting large amounts of data to identify patterns and trends that can be used to make informed business decisions. This requires a strong background in math and statistics, as well as proficiency in programming languages such as SQL, Python, and R. Check out these strategies to help you get started:
Pursue a degree in a related field: consider obtaining a degree in computer science, math, or statistics. Many universities now offer specialized data analytics programs that equip students with the skills and knowledge required to succeed in this field.
Take online courses and bootcamps: explore online courses and bootcamps to acquire the required skills, including SQL, Python, and R.
Ironhack provides various data analytics and other tech bootcamps that enable individuals to learn industry-relevant skills and gain practical experience.
Seek out mentorships: join one of numerous initiatives created to empower you to succeed in the digital economy.
Stanford University’s Women in Data Science (WiDS) initiative hosts datathons, features data science leaders on their podcast series, and encourages secondary school students to consider data science careers.
Des Chriffres et des Jeunes trains young people in data science and offers data fellowships, to narrow the gender data divide.
Build a network: connect with other data analysts by attending conferences, meetups, and joining professional organizations.
Women in Data and Women Who Code provide resources, networking opportunities, and support for women working in data-related fields.
Women in Machine Learning maintains a directory of women data scientists, hosts mentoring events, and manages a job posting mailing list.
Ready to follow in the footsteps of great female tech leaders? Join the challenge with our Data Analytics Bootcamp.