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What it’s like being a woman in data


What it’s like being a woman in data


Laura Hague is worried things may get worse for women in the industry. Here are four challenges that need addressing.

Laura HagueI’ll admit it. I work in data but I’m rubbish at coding.

I firmly believe that you don’t have to have coding as part of your job description to work in analytics. I need to be fluent in coding, because if I wasn’t I’d have been out of a job years ago. But, suffice to say I realised at an early stage that coding wasn’t for me and have made some very deliberate moves away from it so far in my career.

When I speak to my clients about how analytics can add value to their business, I talk in terms of the ‘data to insight, insight to action’ journey. My career has mirrored this journey. My first role was very much data to insight and I’ve made a conscious decision to move into insight to action (because I’m not one of those admirable people who try really hard at something they’re bad at to improve!). I’m squarely in the ‘I’m rubbish at this and I hate being rubbish at stuff so I’m giving it up’ camp.

I now help businesses translate insights and action, to ensure the analytics we produce deliver real value.

So what has it been like to be a woman on this journey? No different from being a man I suspect. I don’t feel that I’ve had a tougher ride, or that I’ve been overlooked for roles or promotions. As I progress into more senior roles, there are noticeably fewer women but I suspect this is the case in all industries, not just analytics.

Nevertheless, there are some challenges. I’m quite often the only female in a meeting of 20 senior leaders and that can be intimidating. There is definitely an underrepresentation of women in data, and I’m passionate about addressing this because I believe a more balanced analytical workforce will benefit us all.

That is why I was excited to speak at the inaugural Women in Data event last year, where women from across the data industry had the opportunity to network and share their challenges and successes.

The challenges I see facing everyone – but particularly women – as our industry evolves into the future? As I see it, there are four that could impact our ability to address the gender imbalance in analytics:

1. Attracting them early

We know that women are underrepresented in university science, technology, engineering and mathematics (STEM) subjects and addressing this would obviously help to address the imbalance. But I think we need to cast the net a bit wider. What about marketing degrees? Do they have a substantial analytical component? Data-driven marketing is not a new concept, but we’re still not equipping our marketing graduates with the knowledge and skills they need to succeed in this area. In doing so we could certainly introduce more women to analytics, since we know they are overrepresented in marketing subjects.

Attracting more women into STEM is easier said than done. I wonder whether putting more emphasis on insight to action and on the softer skills we know some analysts struggle with could also help.

2. The greedy hiring manager

I have been both a victim and a perpetrator of this in the past. As a hiring manager it’s easy to throw a whole heap of requirements into a position description (PD) to ensure you attract only the ‘best’ candidates. Currently using Python in the organisation? No, but we might in the future, so let’s put it into the PD. Sound familiar?

Internal research conducted at Hewlett Packard and referenced in The McKinsey Quarterly and Sheryl Sandberg’s book Lean In claims women don’t apply for roles unless they think they’re 100% qualified, whereas for men it is more like 60%. So by throwing everything but the kitchen sink into the PD we’re inadvertently alienating women. It’s happened to me in the past few years: job adverts asking for someone who’ll manage a team of 20 with just as many stakeholders, while being able to code in a bunch of languages I’ve never even heard of, let alone used. I’ve had a sneaky suspicion that in reality, the successful candidate wouldn’t be expected to code because they simply wouldn’t have time. Regardless I haven’t applied.

So to all you hiring managers out there, think about what you need rather than what you’d like.


3. Keeping up with the pace of change

We all need to take responsibility for our career development and demonstrate an appetite to learn more. But equally, organisations have a responsibility to give us the time (and budget) to learn and then use new skills. Expecting analysts to learn new tools and technology in their own time is unrealistic for all, but particularly for working parents or those with commitments outside work. Sending someone on a training course isn’t enough. Software skills need to be practiced and if we’re not given the opportunity to use them at work, we’ll lose them.

This latter point is, I fear, in danger of becoming more of an issue with the development of ‘advanced’ analytics teams. Teams who are given the opportunities to use new methods, tools and technology, sometimes at the expense of other teams. And on that point, what does it mean for morale if you’re an analyst with 20 years’ experience who doesn’t make it into the advanced team because you haven’t been given the opportunity to develop? Are you a member of the ‘regular’ analytics team? Or worse, the ‘mundane’ analytics team?

In my experience, these advanced teams tend to be staffed with younger, more recent graduates who have learned some of the cutting edge stuff at university. It makes sense. But as we’ve already discussed, the pool of available STEM graduates is already heavily skewed towards men. If we add in the challenges of keeping up with the pace of change, I wonder whether we’re compounding the issue of female representation in analytics. I worry that despite our best efforts, things might get worse instead of better for women in the industry.


4. The data science effect

This is more of an observation than a challenge and is very much linked to the points above. I’ve seen a worrying trend of snobbery emerging in recent years where data scientists are seen as somehow better than analysts, and can command a higher salary to boot. You only need to look at job ads out in market currently to appreciate the recent shift. What’s even more worrying is the conversations I’ve had with female friends who say they can’t apply for these roles because they’re ‘just’ an analyst.


So there you have it, my observations on the challenges facing the analytics industry in the pursuit of gender diversity. These are based on my own personal experience and are by no means representative of the industry as a whole. Or are they?


Laura Hague is senior insights manager at Track.



Image copyright: rawpixel / 123RF Stock Photo


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