Careers involving data expertise are soaring. Data skills have seen significant growth in recent years, according to The Quant Crunch report.
By 2020, the report predicts, the number of positions for data and analytics talent in the United States will increase by 364,000 openings, to 2,720,000.
- Clinical Data Analysis (+54 percent)
- Data Science (+40 percent)
- Quantitative Data Analysis (+38 percent)
- Data Visualization (+31 percent);
- Data Engineering (+28 percent); and
- Machine Learning (+17 percent).
What percentage of those roles will be filled by women? A look at current statistics can give some clue as to what the future of women in Data Management careers may hold. 85 percent of Data Scientists and 74 percent of Predictive Analytics professionals, for example, are male, according to research from executive recruiting firm Burtch Works. While percentage-wise women ranked close to their male counterparts in terms of holding job titles like Data Scientist – and indeed significantly surpassed them in careers like Data Analyst – according to the 2017 State of Data Science and Machine Learning survey hosted by Data Science Analytics competition platform Kaggle, females represented just over 16 percent of the respondents in total.
“Across the board, whether you are looking at Data Scientists or another data-related career like DBA or Data Engineer, women are still greatly under-represented in those categories,” says Sadie St. Lawrence, Founder and Executive Director of the non-profit Women in Data.
That situation is ripe for change. To that end, Women in Data has three primary pillars:
- To create awareness
- Education
- Advancement for supporting diversity in data careers
“I saw there was a real need in our community for something of this nature to help support women in this field,” says St. Lawrence, whose day job is as a Data Scientist at vision care specialist VSP Global. “Our goal is to lessen the gender gap.”
More Diversity Equals Smart Business
It’s to every organization’s benefit to realize this aim. While data doesn’t lie, the results reaped by analytics processes are dependent upon what queries are made in the first place. And men and women won’t necessarily think to ask the exact same questions of the data at their disposal, because they may define problems and possibilities in quite different ways. Companies that don’t encourage diversity in their data ranks, then, could miss out on discovering new insights that could lead to new business opportunities.
“We like to have this idea that the data is the ultimate truth, but it really comes down to how you ask questions and the evidence you are looking for to support the question you have or to deny it,” St. Lawrence says. “Remember that data is just a representation of something that happened. A lot of times it tells a portion of the story but not the full story.”
It’s important to bring diverse people into a problem “to be able to ask the right types of questions to get a better understanding of the full story,” she says. They may even bring data elements into Predictive Models that no one had ever thought to include before. That could open doors to providing better products or services to whole portions of a company’s user base that it currently isn’t fully engaging with or thinking about in the right way.
“Any time you bring in someone who brings a different perspective and different experience, you will only add to being able to solve problems,” St. Lawrence says.
Getting There from Here
Women in Data doesn’t believe there’s some perfect male/female ratio that organizations should strive to reach among their data talent. Some companies may find it helpful to set some sort of percentage goal, if only to get out of a habit they may have of not generally considering women for data-intense roles.
But for most it’s likely more a matter of simply being conscious that not everyone whom their data leaders hire should resemble each other. “There should be a conscious awareness to make sure you go after building a diverse team,” says St. Lawrence. In a male-dominated sector, it shouldn’t necessarily be surprising that men often make their way onto data teams.
“It’s so easy to hire people in our own circle, and those people usually are most like us,” she says. “That’s where awareness needs to be.”
Such concerns were echoed at December’s Women in Big Data Meetup in Paris. A writeup about the event noted that Remy Chambard-Williams, consultant, SAP PPM at SAP France, “elaborated on the tendencies to hire people who are either similar to you or to the team member who is leaving. Both practices are common and obstruct diversity hiring.”
Awareness about the opportunities that await in the data field also needs to be awakened in women themselves. One of the first things Women in Data addresses is proactively pointing out that these career options exist. St. Lawrence says that it seems to her that many women in data positions today seem to have stumbled their way in, in comparison to men who are more likely to take a straight path to data careers from computer science positions.
As surprising as it may be, even today schools don’t necessarily promote technology and data fields to girls as aggressively as you might think. “It’s not catered to girls in their teens in middle school as something interesting,” says St. Lawrence, who received her MS in Analytics in 2016; her B.A. is in Psychology.
Generally speaking, IT and tech culture tends to be a little less of a female-friendly culture, she adds, and it would be helpful to make the data field more inviting to women as part of education programs. In her Masters’ Program, for instance, there tended to be a lot of interest among her peers in applying analytics to sports-related issues. While plenty of women enjoy sports, it wouldn’t hurt to show that analytics can apply to everything. “You could use analytics in fashion,” she says as an example. That could draw in people who don’t care about the big leagues and who don’t want to have to pretend that they do in order to succeed in a course of data studies.
Women in Data has education forums – from hack nights to boot camps – to help women realize how they can be part of an increasingly data-dependent world in their own careers. It also works with individuals to help its 600-plus members find those jobs.
Women in Data: Impact on the Future
As Tina Rosario, director of the Women in Big Data EMEA region, reportedly remarked at the recent Women in Big Data meeting, the growth of the Big Data and Analytics sector is running up against an increasing shortage of talent in all roles, from technical to managerial.
“Women constitute 50 percent of the population and should not be excluded from these career opportunities,” she said. “Secondly, women tend to do really well in career tracks that have positive social impact, and the Big Data field provides multiples opportunities of this kind, and many roles where human judgement and not losing the big picture are crucial skills.”
St. Lawrence adds to that point, noting that women can be critical to bringing more compassion and social consciousness into the data space. Women, she says, can be highly influential in using Artificial Intelligence for good, for example:
“We hear so much in the media about AI taking over the world, and we have to remember that we are at the point where it isn’t good or bad – it is what we make of it,” she says. “We need women in this field because of the perspective they can bring to these systems, infusing social consciousness so machines learn to make compassionate decisions.”
Looking a few years down the road, St. Lawrence hopes to see a really strong, supportive and collaborative community for women in data – including building out WID chapters across the U.S. “I joined Data Science because I felt it was made up of these rebels from academia who saw the potential of data and knew working with its scale and volume could make an impact on the world,” she says. “I want to keep that feeling but also make sure we are open to all who are out there.”
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