Look around, everyone out there in analytics land. You may have noticed a preponderance of young males among you, many relatively new to the field, or, if not, then at least to their current places of employment.
But did you know that analytics professionals most involved in data preparation or having their hands in a little bit of everything are those most likely to have a Master's degree? Or that analysts tend to work in small groups?
These are among a variety of findings the International Institute for Analytics (IIA) and Talent Analytics learned in a recent survey of analytics talent. You might recall this study: Back in July, Greta Roberts, CEO of Talent Analytics, a talent data firm, shared an invitation with AllAnalytics.com members to join the study as she e-chatted with us about the impending analytics skills shortage and how best to address the issue.
Hopefully some of you did participate and number among the 302 "deep dive" analytics professionals who responded to the survey, results of which the IIA and Talent Analytics shared in a web conference earlier this week. I'll be talking with the researchers next week and will share their deeper impressions then, but in the meantime, here are some additional tidbits.
Gender and age
About that young, male contingency? The male/female balance among survey respondents is about what you'd expect based on what we see elsewhere. However, the sampling was, in fact, younger than anticipated, Pasha Roberts, CTO of Talent Analytics, said during the web call. Specifically, he noted, 72 percent of the survey population was male. Fifty-seven percent hadn't hit their 40th birthdays yet, with only 17 percent topping 50.
Education
Analytics professionals are an educated bunch -- no surprises there. But only 16 percent have doctorates, and those cluster mostly in management positions. Slightly less than half -- 47 percent -- have Master's degrees; 36 percent have Bachelor's, or less. Math/statistics and business dominate the areas of study while "surprisingly few" have degrees in science, economics, or finance, he said.
The talent pool
While the number of analytics professionals at respondent companies ranged pretty much equally from the smallest to the largest grouped sizes, results show that most analysts -- 80 percent -- work in groups of no more than 10. Add "ability to work in small groups" on your hiring checklist for analytics talent, he suggested.
Analytics software in use
Interestingly, given the frequency of conversation around R and other open-source analytics software, survey findings show that's harder to find in the workplace than commercial alternatives. The survey showed twice as much commercial software as open-source software in use by analytics professionals, Roberts said. Oh, and one other tools note: "Everybody uses spreadsheets."
Tenure
A young workforce means analytics professionals haven't been at their jobs all that long, naturally. The survey showed that 29 percent of respondents have been professionally employed in analytics for fewer than five years and 60 percent for fewer than 10 years. Slightly more than half -- 52 percent -- of respondents have been at their current places of employment for fewer than three years and only seven percent for more than 10 years.
Roberts also shared how the numbers break down by current role: 49 percent at fewer than two years, 88 percent at fewer than five years, and two percent at more than 10 years.
Functional roles
Researchers broke respondents into four functional clusters:
Data preparation: Spends most time in
acquisition, preparation, and analytics
Programmer: Spends most time programming and in analytics
Manager: Spends most time in management, administration, presentation, interpretation, and design
Generalist: Does a little bit of everything
Where do you fit in? And how does this play out in terms of your psychometric profile? We'll find out more next week!
Although the representation of women in computer science has been dropping since the eighties, women are far better represented in analytics professions than we are in the Talent Analytics survey. The methods that were used to invite participation in the survey were relatively informal, and as such, represented some segments of the community better than others.
I've written at some length about women in analytics in my article "The STEM Profession that Women Dominate". My sources for this piece included data from the US Census Bureau and Bureau of Labor Statistics, among others. Have a look and you will see that, by many measures, women have very strong footing in the analytics professions.
This is not intended to pick on Talent Analytics, or their survey, but simply to remind us that what we see in our own professional silos does not necessarily reflect the world as a whole.
I'm not surprised that many analyst have master's degree. I always said my bachelor's taught me the basic and then the master's taught me how they were all connected.
Also, not surprised by analysts working in small groups, at least for certain period of time. Too many voices create confusion at times. Also being an analyst can be like being a coder where one needs to shut out the world and go into the 'zone'.
I hope to see my women analysts, because I believe diversity is important in helping perceive all the data is connected.
@Beth: Your experience at conferences and professional meetings reflects my own. This was a major catalyst for the underlying theme I used for my book: Data Mining for the Masses. "For the masses" to me meant I was trying to shake up the digital divide if I could, tear down the misconception that some disciplines are "for men".
There have been lots of efforts, formal and informal, to try to break down the gender gap, yet they persist. Here is one effort I'm aware of at Carnegie Mellon University: http://www.cs.cmu.edu/afs/cs/project/gendergap/www/index.html
I totally agree about the caliber and professionalism of women in our industry, and I'd like to see more. Will we ever see the gender gap close?
Matt, I'll comment first on gender. I know overall males exceed femaies in these disciplines, and in the professional analytics ranks, as we've learned in the survey. Still, I've been impressed with the caliber of professionalism and knowledge I've seen on the conference circuit, for example, of women involved in executive analytics roles. Pamela Peele, chief analytics officer at UPMC, comes to mind, as does Amy O'Connor, senior director of big data at Nokia. Of course, these were two of maybe a grand total of something like three or four women speaking at a two-day conference with about 75 sessions. Sad.
On gender -- This appears to mirror the gender gaps we see in academe in the Math, Information Systems, and Computer Science disciplines. All three majors have less than 30% women, and all three majors naturally segue into analytics careers.
On age -- Analytics is a good place to work at multiple career levels (entry, mid-, later), but I see most really good analysts becoming manager and administrators by the 40-50 age range. Why? Money usually. If they stay in analytics too long, the get (or fear getting) pigeon-holed.
On education -- The doctorate stat surprises me. Most management level folks I've worked with in the past stopped at the master's level, usually MBA. Those who had doctorates were usually not managers, but rather highly paid and specialized analysts who preferred relationships with supercomputers rather than humans. Am I stereotyping here?
On tenure -- It seems to me that a lot of young analysts didn't start out as analysts; they started in customer service, marketing, or some such discipline. Sometime in their first year or two on the job, somebody noticed the person was good with figures, or was good and finding and identifying trends and assigned them to an analyst project which became a job. Thoughts?
Lisa Dierker, a Wesleyan professor who taught a statistics class on the Coursera massively open online course platform, talks about all her behind-the-scenes help.
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Visual Analytics: Who Carries the Onus? The Issue: Data visualization is an up-and-coming technology for businesses that want to deliver analytical results in a visual way, enabling analysts the ability to spot patterns more easily and business users to absorb the insight at a glance and better understand what questions to ask of the data. But does it make more sense to train everybody to handle the visualization mandate or bring on visualization expertise? Our experts are divided on the question. The Speakers: Hyoun Park, Principal Analyst, Nucleus Research; Jonathan Schwabish, US Economist & Data Visualizer
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