A friend called me the other day and excitedly announced that his daughter had just been accepted into law school. My heart sank. As a business futurist specializing in the intersection between human capital and the rapidly maturing field of data and analytics, I dedicate a lot of time researching how the 4th Industrial Revolution will impact existing professions.
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On Monday Amazon opened the first cashierless store, Amazon Go. LinkedIn posed the question, “Are cashiers' days numbered?” I think we’ve known this was coming since grocery stores introduced self-checkout lines and Wendy’s installed self-ordering kiosks.
Do you ever worry about robots taking over the world—or at least your job? Once upon a time, that notion might have made you feel a little silly; as if you were overreacting after too many viewings of The Terminator or Blade Runner. However, in the year 2017, it doesn’t seem like such a stretch to worry about the increasing presence of robots and artificial intelligence (AI).
If you’ve watched a single interview with the robot named Sophia, it seems that we should all take steps to understand our future “relationship” with robots and AI. It is on the horizon. Recently granted “robot citizenship” in Saudi Arabia, the humanoid robot created by Hanson Robotics frequently uses sarcasm and cracks jokes during interviews. She has even taken a few jabs at Elon Musk who has lately started sounding warning bells to alert humanity that AI is rapidly advancing and may soon become a huge existential threat. Check out Sophia talking to Jimmy Fallon on The Tonight Show.
Do you ever feel like terms like "big data," "data analytics," "data mining," "data optimization," etc. are bandied about to epic proportions these days? I mean, if you're in the field, you definitely notice these terms, but even your basic computer users and luddites are starting to notice these terms. Most people have a fairly decent idea of what these concepts mean.
Data is changing the world around you. You see it. You feel it. You worry about it. The headlines scream, "The Robots are Coming and They're Taking our Jobs," and you wonder, "Am I next?" But you don't have to worry if you take the time to understand what is happening, why it is happening, and why it doesn't have to happen to you.
In the simplest term, Data Science is the transformation of Data into actionable insights or visions. Raw data or data that has not been processed for use1, must be processed and transformed to produce information. That information is then analyzed to develop knowledge. The end goal is for the knowledge to drive action.
"Robots won't put humans out of business —they'll give them a promotion." —Bullhorn (introducing Bullhorn Bots for its CRM platform)
Process automation is not a new thing. We have been automating processes since the mid-18th century (circa 1784.)
Women represent a large, untapped talent pool that can be leveraged to address the critical shortage of expertise in data and analytics.
Data is everywhere, as are job postings for professionals with the skills necessary to derive value from massive amounts of data. However, the supply of professionals with these skills is not keeping pace with the requisite demand. Industry experts predict that by 2018, the shortage of analytic talent, in the US alone, could be as great as 1.7 million open positions without qualified candidates in the job market to fill them.
Although it is common knowledge that women are underrepresented in S.T.E.M. professions (science, technology, engineering, mathematics), women are well represented in traditional analytic professions; representing 50% of the labor force in Mathematics and Statistics.[i]
This puts women in a unique position to play a key role in addressing the existing gap in data and analytic expertise, as data science depend heavily on mathematics and statistics. Women represent a large, untapped talent pool that can be leveraged to address this expertise shortage.
The female brain is uniquely wired for analytics.
There is research emerging that suggests that the female brain is uniquely wired for analytics. In a robust study conducted at the University of Pennsylvania, researchers found that women's brains display superior connectivity between hemispheres, suggesting that the female brain is optimized for combining analytical and intuitive thinking[ii]. Those of us who perform analysis know that reason and intuition are synergistic. It is intuition from the right side of the brain that is used to form hypotheses, which are then proven or disproven by the left. The right hemisphere is as important as the left in extracting value from data.
The challenge for women is that data science requires programming and data management skills. Of all the S.T.E.M. disciplines, technology is where women are most notably absent and the situation is becoming increasingly worse:
- Women earn only 17% of the undergraduate degrees in Computer Science. This is down 54% from 1985, when women earned 37% of the Computer Science undergraduate degrees.[iii]
- Additionally, although women constitute 57% of the professional workforce, they hold only 25% of the professional computing occupations.[iv]
Here are two possible ways to enable women with a background in mathematics and statistics to add value in data science:
- Low-code or no-code platforms - there are solutions being developed that facilitate analytics without the user to have programming skills. These platforms allow individuals who aren't comfortable doing hands-on coding to perform advanced analytics.
- Collaborative Data Science - Given the complexity of 21st century business and technology, collaboration is increasingly a must. Ambient Intelligence's research shows that regardless of gender, data science requires a plethora of skills that are very seldom found in one individual. Identifying individuals strengths and forming data science teams that have the skills required to successfully complete the project will effectively overcome skill deficits.
With regard to the team approach to data science, I recently learned that women bring something very special to collaboration. At MIT's Center for Collective Intelligence, Professor Thomas Malone and his team are working on measuring the composite intelligence of a group of people, like a group IQ. Depending on the dynamics, a group can be either more or less intelligent than the individuals that comprise the group.
The more women on a team, the higher the team's collective intelligence.
Professor Malone has found correlation between collective intelligence and the following three factors:
- Social Perceptiveness - an individual's ability to learn about others' feelings and emotions by picking up information from physical appearance, verbal, and nonverbal communication. Facial expressions, tone of voice, hand gestures, and body position or movement are just a few examples of ways people communicate without words.
- Equality of Participation - the ability for group members to participate equally in group discussions.
- Proportion of Females - the number of women participating in the group, relative to their male counterparts.
So, the higher the group member's emotional intelligence, the higher the groups collective intelligence. Likewise, the more empowered group members feel to participate equally in group discussions, the higher the groups collective intelligence. And finally, the more women on the team, the higher the groups collective intelligence. Professor Malone attributes this last observation to be directly tied to women possessing strong social perception skills.
In conclusion, women represent an untapped S.T.E.M. pool to help address the critical shortage of data and analytic expertise. Women are equally represented in Mathematics and Statistics, emerging research suggests that women's brains are optimized to perform analysis, and women's social perception skills make them strong collaborators, which is essential today in the field of data & analytics.
Therefore, women are well positioned to play a key role in driving the narrative as we enter the 4th Industrial Revolution, that of an abundance of ambient data from the Internet of Things.
The future is here! Are you ready?
[i] US Population Reference Bureau. Mathematicians and Statisticians in the United States, 2007. Available at http://www.prb.org/pdf08/scientistprofiles/occprof08_mathstats.pdf.
[ii] Brain Connectivity Study Reveals Striking Differences Between Men and Women. (December 2013) Available at http://www.uphs.upenn.edu/news/news_releases/2013/12/verma/.
[iii] National Center for Women and Information Technology. Women and Information Technology: By the Numbers, 2015. Available at https://www.ncwit.org/sites/default/files/resources/btn_03092016_web.pdf