Recently, all in one week, I listened to two podcasts (Digital Analytics Power Hour and Present Beyond Measure), attended a Google female founders meetup and a Women in Focus event all focusing on women in tech and women in digital analytics.
Unsurprisingly, I have been unable to stop thinking about women in tech and what that means for the digital analytics community.
I am not a woman in tech. My professional background is working within a highly technical Government Defence organisation, where, yes, I was a woman. But I’m not technical, right, so I never think of myself as a woman in tech. I might have advised people on highly technical operations – but I can’t do them. So it doesn’t count. Last year I joined a digital analytics company where I am learning to run analysis through R. True, my most visited website each day is stack overflow (hey – I’m learning) but I’m not a woman in tech because I’m not technical.
And then I came upon those two podcasts. And then an event. All in one week. It shifted my perspective on how I think about myself.
I listened to women talking about what it’s like to work in a male dominated industry – that applies to me — check/check (Defence/Digital Analytics).
Women talking about being intimidated at work because they are in meetings with highly technical colleagues talking about highly technical work and feeling like they can’t ask a question for fear of it being a ‘stupid question’ but also desperately wanting to understand how something works – check/check.
Or worse yet – women talking about asking the stupid question (which isn’t actually stupid) and then being judged for asking it – check.
Women talking about how they are motivated to learn terrifying new skills for their job but feeling like they can’t get there fast enough – check/check.
Women talking about how they want to be known as a thought leader in their field, and not just the token female for a working group, a speaking panel or a company quota – check.
Women talking about how they have to work twice as hard to be considered as competent as a male colleague (check/check). And don’t get me started on what happens when you make a mistake.
Well it seems I am a woman in tech. When did this happen? Remember… I’m not technical.
So after listening to these women in tech share their experiences and realising that I am after all, a woman in tech, I started to think what’s the next logical step – how do we get more women in tech.
I am a woman in tech and I have an answer for you.
The easy group:
Some women are drawn to the analytics field and they love it – they take names and kick butt. These are the women that you already work next to and hear from regularly – they are like Michele Kiss, Krista Seiden, Lea Pica and Aurélie Pol. Keep supporting them and they will continue to inspire many young women to get involved in a more technical field through the amazing examples they set across the industry.
The hard group:
These are the women that don’t think they are technical but also have an open mind – this is me. I came to be an analyst because I love understanding how people work – and data gives me an answer to how, why or what someone did and an idea of what they will do next.
I’m not afraid to ask a ‘dumb’ question if it means understanding a problem better (in some jobs this has even been an asset). I’m not afraid to learn a new skillset. I’m not afraid to be surrounded by intelligent, technical colleagues, even if it means being the only woman in the room. I still remember being the only female in my advanced physics class and my teacher betting I would top the class – and I did (note: I do not encourage gambling in high schools). I thrive off being around people smarter than me because it pushes me and I learn from the best (the same reason I like to exercise with people fitter than me).
The key to getting and keeping this group is to bring them into the industry in any way possible. If you interview a woman who is good at marketing hire them for a marketing job. If you interview a woman who likes cluster analysis give them the opportunity to work on cluster analysis. If they like data visualization, focus their efforts on data visualization across the business. If they enjoy tagging let them be the tagging expert. If their strength is client relationships make them client facing. If they get a buzz from testing, put them into a testing role.
Harness what they are good at. Don’t focus on what they are bad at. And here’s why.
You will make women feel confident. And confidence in their ability to do their job will keep them. It will inspire them. It will drive them to try something outside their comfort zone that will make them better in their area of expertise. And then they will get hooked and realise what an amazing industry digital analytics is and how the technical is not so scary. In turn, those women will inspire other women. And those women will listen to some podcast and realise somewhere along the way – they are also a woman in tech.
I don’t need to explain why bringing someone into a new industry, an industry which they are already uncertain of, and getting them to spend 90% of their time on things they are not familiar with or told that as a woman, they shouldn’t be good at, will lead them to run for the hills.
As someone that believes in mentoring, I advocate a strengths-based approach for all staff, not just women. Too often we focus on what a person can’t do, instead of what they can do. It’s a mode of corporate thinking which is rubbish. A strengths-based approach to your staff will not only ensure you get the most out of them but also keep them longer because they will be interested and passionate about what they do. It’s true for all and is particularly important for women in tech.
I confess. This is not based on evidence or data (seems I forgot my job title didn’t I). But it is based on personal experience and when it comes to why you love your job, often times that can mean more.
“Without data you’re just another person with an opinion” (W. Edwards Deming).
My name is Moe Kiss. I am a woman in tech with an opinion about how we can get more women into digital analytics. I am here. But more on that to come…
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Finally did it – and it is a good read.
I like the distinction, although the easy/hard boundary can be very hazy. I suspect that the ladies in your easy category experienced, and ultimately overcame, the very same issues that the women in your hard category must face. They have just been at it a bit longer and fought and won a few more battles.
Some of the women around me have taught me about the paramount importance of confidence, and how a woman reacts differently to men. A woman’s confidence is a very powerful force when in full flight, but (when viewed from a man’s standpoint) it looks somewhat fragile. Fortunately, these great women have taught me ways to better understand how their confidence works, how they feel about it, and helped me to understand how to help nurture it. While this is still very much a work in progress, I have come to learn is that a woman’s confidence is no less powerful or fragile than a man’s – but it is different, and that difference matters.
When I look objectively at the world of tech, and the women and men in it, I don’t necessarily see the men being statistically better than the women as it is commonly perceived. The males are no smarter, no more talented, nor are they more innovative than their scarce female counterparts. They do, however, hold the edge in “applied confidence”, and that is what I think is driving the imbalance.
Also, I never, ever underestimate the power of a positive attitude in bringing the woman into the world of tech. Their attitude can elevate a woman in any field – even male dominated ones – regardless of her background. An attitude can move the barriers (even if the confidence is not completely there).
And I have at least one data point here (in keeping with your data orientated approach). It was a great attitude that really tipped the decision when I hired a young para-legal working in WA a few years ago – into a world she knew nothing about, nor was she trained to enter. She did just great.
Thanks so much for contributing Sutty! Good to know that I serve as data somewhere 🙂
There are two drawbacks with that approach –
1) No one can ever master any one thing at any time – the mastery of some particular topic or aspect is an illusion
2) Throwing someone in the deep end is to actually get the invisible confidence to come out as a survival instinct and not in slow-motion mode.
The approach might be crude at best – but I believe that’s how the best swimmers learn. I rest my case however. Will concede to your points! 🙂
Moe – As someone who’s worked with you and with a lot of women ‘in tech’, I completely agree with your check list and the often patronising attitude towards women in tech.
However, I’m also a believer in swimming from the deep end of the pool which is strongly against a ‘strengths-based’ approach to work.
It’s my belief, and like all other opinions and beliefs, solely mine – that in an ever evolving and rapidly changing world like analytics and data sciences, the ability to swim out from a deep end of the pool helps some one stand out more prominently than someone comfortable swimming in their own corners.
Let me explain. If someone is really good at Data Visualisation, instead of saying do more visualisation in Tableau(which that person likes), if they are asked instead to do ‘cooler’ visualisation in let’s say, RShiny, that would be a challenge and not an obstacle to do more.
I personally have a learnt a lot of things, most of the things, by being thrown into the deep end and asked to swim out – the learning is very natural and quicker that way, both of which are needed in this day and age.
Of course, none of this takes away the fact of the token female or condescended-to female instances that happen in a lot of places – but my firm belief is that irregardless of gender, that obstacle should become an interesting challenge in virtuous self-expression and development.
Very interesting viewpoints, hope to read more of your writings!
I agree completely! But I think that comes with someone feeling confident and then shoving them in the deep end once they have confidence in having mastered one thing already. What I am talking about is taking someone who has no strong skills in say data visualisation (sticking to your example) and then saying do a cooler visualisation in RShiny. I think you’re example is best practice – take someone who is good at something, help them perfect it, and when they are no longer being challenged then you give them room to learn something new.