Data Science is for girls!

The inaugural Girls in Tech event was a sell out. For a Monday evening this was impressive. Monday is a day when bed starts calling at 4pm! But this event offered a refuge for women with ambition. Not necessarily the type of mindless ambition that drives some people but certainly the type of normal, natural and understandable ambition that would be fully expected in a room full of men. The type of ambition that demands a fair hearing and a level playing field.

At Girls in Tech, no one questioned the purpose of the organisation or the event. Everyone knew why they were there and what the goal was. Days earlier however, I attempted to explain the organisation and the event to a male colleague. He maintained a quizzical look that suggested I was making no sense whatsoever. I berated myself for a poor explanation until I realised that my explanation was NOT the problem.

The reason for an organisation like Girls in Tech is best understood by women. Men should feel slightly embarrassed by this. Men should attend Girls in Tech events if only to increase awareness of the issues being discussed.

Top of my take-aways at the launch event was a palpable sense of risk adversity and lack of confidence among women. It wasn't just among those women wishing to become entrepreneurs or open their own business. It was also apparent among women wishing to apply for new and challenging positions with their current employer. Fear of being "found out" for not having EVERY requirement for a position or lack of experience for a job they know, deep-down, they would be great at. As a man, this makes no sense. I've always confidently "over-sold" my ability to land a new job. That's not the same as lying by the way, its simply, selling yourself!

My field of data analytics, wasn't always my field. I just found that I was pretty good at making sense of data whilst at the same time understanding business objectives. The women that I know in data analytics are, intuitively much better at it than me. They are much more likely to let the data do the talking without adding unnecessary narrative to a perfectly fine data story. A man's habit of over-confidence in the story he THINKS the data is telling him often leads to trouble. In fact, some of my best analytics moments have been peer reviewed by female colleagues who are always ready to rein me in...and rightly so.

I've been to so many analytics conferences and breakfast briefings that have men congratulating each other on their rise in the corporate ranks. I often wonder how many of them have over-sold themselves or their work to be there. Data science and analytics needs a measured, scientific approach without the often biased narrative.

So, next time you review a candidate's resume try to avoid reading their name and instead try to guess their gender. If you're looking for a data scientist, think about what level of integrity you need in that role!