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Sr. Data Science Content Engineer
Sr. Data Science Content Engineer

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The benefits of diversity have been researched and documented in many studies. These studies show that companies with diverse leadership tend to have better financial returns and be more innovative. Academic research echoes these results, where findings indicate that diverse groups are more effective at problem-solving and tend to deliberate decisions more thoroughly.

 

The emerging theme is when we collaborate with someone who sees the world differently than we do, we incorporate more perspectives. When we account for more possibilities, we get better ideas and outcomes.

 

The business case for increased diversity is clear, so why aren’t tech companies and leadership positions across all industries more diverse? The answer is straightforward: diversity efforts can be difficult to achieve. Finding a genuine, practical strategy that promotes and sustains diversity is challenging. There is not a simple, universal solution that will work for every company.

 

The good news is that we live in an exciting time where we can leverage data, analytics, and machine learning as tools for transparency to promote equality.

 

In honor of International Women’s Day, Women of Analytics, I’d like to highlight the presence of women in analytics and describe how analytics and data science can be applied to support equality in the workplace.

 

Women in Analytics and Data Science

 

In 2018, women made up 56.8% of all professional occupations (fields like computer and life sciences, architecture, law, healthcare, education, and art) but only 25.6% of computing and mathematical occupations (according to the Labor Force Statistics published by the Bureau of Labor Statistics).

 

Despite poor representation across all computing and mathematical occupations, women are well represented in the fields of statistics at 53.8% of the population, as well as operations research analysts, where women are reported to make up 49.1% of the population. 

 

At this moment in time, it seems reasonable to say that women in analytics are just as common as men are, even if they are less visible

 

Because data science is still a relatively new field, there are not comparable statistics on gender participation from the BLS. The best resource I’ve found is from Kaggle’s 2018 State of Data Science and Machine Learning Survey and Competition. The winner of the competition, Martin Barron, applied the survey results to exploring gender participation in data science. Barron's findings demonstrated that women were underrepresented in the survey and suggested (based on the women that did respond) that although the men and women that responded tend to do similar work, spend equal amounts of time coding, and use equivalent tools, women have different titles in the data science space, and have different salaries. I highly recommend reviewing Barron’s findings- they are fascinating.Venn-WoA-500.png

 

Data science sits in the crossroads of computer programming, applied mathematics, and statistics. Women are very underrepresented in the computer science pipeline. In 2017, only 18% of undergraduate computer science degrees were awarded to women. In fact, computer science is the only STEM field that has had a long-term decline in female representation after peaking in the mid-1980’s. Perhaps this is an explanatory factor for the lower representation of female data scientists currently. 

 

But it doesn't have to be that way. One of the cool and powerful things about data science is that people come to it from diverse educational backgrounds. The data science pipelines are truly interdisciplinary, ranging from sociolinguistics, biology, and economics to engineering, computer science, and mathematics (my background is in geography). The core pillars of data science; machine learning, coding, mathematics, and statistics are all learnable skills. There are many open-source or inexpensive ways to fill in any skill gaps you might have.

 

Participation in analytics and data science from people with a wide range of backgrounds (e.g., gender, education, nationality) is important because the analytic process and its outcomes directly depend on the people asking the questions and finding the answers. Different people think differently, meaning they ask different questions and approach problems differently.

 

 

Leveraging Analytics and Data Science for Diversity

 

An exciting prospect for the age of analytics is the ability to uncover patterns and tell stories with the massive amount of data being gathered. Data can often provide firm evidence to suspicions or hunches based on experience and observations, or even contradict them. Analytics and artificial intelligence (AI) can also be leveraged to identify patterns in bias or discrimination as well as to develop solutions to improve diversity and equality. 

 

There are numerous creative ways analytics and AI can be (and have been) used to monitor or promote diversity and equality (I’d LOVE to hear what you’ve seen or have been working on in the comments). One example is using AI to help write recruitment emails and job postings. Companies such as Johnson & Johnson, Expedia, and Zillow have seen an increase in female applicants since using the product Textio to help write recruitment materials. Another example is using analytics to create matches between candidates and jobs, while also tracking diversity through the hiring pipeline. Startup company Blendoor’s app does just that with the hope of reducing bias in the hiring process.

 

 

Applying Analytics to Closing the Wage Gap

 

We can credit analytics for surfacing both the controlled pay gap (a difference in wage for the same work and experience) as well as the median pay gap (a difference in the overall median wage between populations). Studies have demonstrated that women are typically paid less for performing the same job as a man (it is an even larger difference for women of color) but also that women tend to be less represented at higher levels and leadership positions, resulting in a median difference in earnings between all men and women.

 

One company that has leveraged analytics and data to close the wage gap is Salesforce

 

In 2015, Salesforce's chief personnel officer, Cindy Robbins, approached Marc Benioff (CEO) with the issue of the pay gap. Marc's initial reaction was denial. He thought, how could there possibly be a pay gap issue at Salesforce? There are explicit policies (at the company) that say men and women should be paid equally. Despite his initial reaction, the company performed an analysis of the salaries of their (at the time) 17,000 global employees.

 

In their results, the pay gap was apparent and pervasive.

 

Salesforce spent about 3 million dollars in 2016 towards adjusting salaries to close the pay gap. Then, they performed another analytical audit in 2017. They were surprised to find 11% of their employees were still underpaid and spent another 3 million dollars adjusting that. They determined that this population had come from the two dozen companies they had acquired in the past year.

 

To combat pay gaps within the company, Salesforce has committed to continuous salary audits and analyses. They have effectively implemented a people analytics program, specifically targeted at using data to combat differences in pay. Other companies such as Starbucks and Glassdoor have made similar efforts.

 

The rise of data and computing has allowed this to be possible- nearly all leaders have the data and the computing power to make this same auditing practice possible in their own companies.

 

Numerous resources outline or demonstrate performing this kind of pay gap analysis

 

Analytics helps us diagnose issues, and with more people aware of the state of compensation in an industry or company, the better chance that it will be addressed. Any initiative to improve diversity and equality in a workplace needs to be backed by leadership who champion the cause and are willing to be fiercely innovative. The questions asked in the analytics process, as well as how results are addressed, is entirely determined by people. Analytics is a tool, but we will always need to choose where and how it is applied.

 


Join the movement

 

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#BalanceForBetter This years International Women's Day campaign is #BalanceForBetter and we’d like to know your plans to help forge a more gender balanced world? Show your support and colors by downloading one of our pledge cards (or customize your own), take a selfie with your pledge, and share it here on Community or on your social networks using @alteryx #BalanceForBetter #WomenofAnalytics.

 

Share your story - We'd love to hear about your journey as a woman in the analytics space! Fill out our Women of Analytics Survey for the opportunity to share your experience and be featured in an upcoming Women of Analytics Spotlight.

 

Stay engaged - Last but not least, get involved in the Women of Analytics Group on Community. Use this space to ask questions, share your experiences, insights and resources, as well as promote your own events or initiatives. Learn more here.

 

 

About Women of Analytics

 

Women of Analytics is an inclusive program rooted in our global initiative to empower and enable all to achieve success in their analytics and data science careers.

 

Our goal is to celebrate thought leadership in analytics culture and form meaningful connections to foster support for women and all individuals in analytics and data science.

By leading honest and insightful conversations with influential women, we’re able to empower analytic talent for success in their careers.

Sydney Firmin

A geographer by training and a data geek at heart, Sydney joined the Alteryx team as a Customer Support Engineer in 2017. She strongly believes that data and knowledge are most valuable when they can be clearly communicated and understood. In her current role as a Sr. Data Science Content Engineer, she gets to spend her days doing what she loves best; transforming technical knowledge and research into engaging, creative, and fun content for the Alteryx Community.

A geographer by training and a data geek at heart, Sydney joined the Alteryx team as a Customer Support Engineer in 2017. She strongly believes that data and knowledge are most valuable when they can be clearly communicated and understood. In her current role as a Sr. Data Science Content Engineer, she gets to spend her days doing what she loves best; transforming technical knowledge and research into engaging, creative, and fun content for the Alteryx Community.