Highs and lows for women in data

Toni Sekinah, research analyst and features editor, DataIQ

The challenges of being a woman working in data seems to revolve around the low numbers of women in this sector and the issues associated with low representation. As highlighted by the annual Ada Lovelace day, attracting women into STEM courses and onwards to tech- and data-based careers remains a challenge.

As Nadia Sood, CEO of a financial services start-up, Credit Enable, said: “Women are under-represented within financial services and the data industry, at least as it relates to this sector, suffers from the same issues.”

Although Sood did not go into the details of the effects of this, in the finance field it translates to far fewer females in management. A 2017 Women in Finance annual review revealed that the percentage of women in senior management in financial services industries was just 28% in 2017. Furthermore, in 2016 former economic secretary to the Treasury, Simon Kirby, said that women in finance are “under-represented, under-paid and under-valued.”

Lisa Carpenter, developer evangelist at Infosum, has found that she is subject to stereotypes about her abilities from potential clients. But she has had to overcome any discomfort she may feel while continuing to do her job of presenting and demonstrating her company’s software. She said that she feels there is an expectation from some male clients that women can’t do the mathematical side of analysis.

"A lot of men assume I'm going to be on the creative and people side."

“A lot of the men I speak to always assume that I am going to be more on the creative side and people side. I have definitely had situations where they have said, ‘Oh, that's cool. Can we speak to the one that is actually going to do the analysis?’ and I say, ‘That's still me’," she explained.

In spite of these problems, there is a lot to like about working in the data industry. For the women I spoke to, enjoyment can be derived from treating data work as an exercise in looking for patterns, solving puzzles, making discoveries and then creating something new.

"I enjoy the journey from hypothesis to find insight."

Clara Higuera Cabanes, a data scientist in the audience engagement team at BBC News, said that she likes what she does because she and her team are able to understand audiences better and consequently make better and more inclusive products. She said: “I personally enjoy the journey from having either a business or a scientific question or hypothesis to the finding of certain insight or patterns that can be useful to someone.”

Sood said that she loves patterns and deriving insights from them and enjoys fitting pieces of a puzzle together: “For me, working with data is like getting up in the morning every day and being offered the chance to play connect the dots, but at a massive scale and with the potential to create massive positive impact for millions of people.”

"Data is like doing a puzzle. Stitch it together and you've got a full story."

For Maya Kuzalti, founder of Sipscouts, working in data was a natural progression of her love of maths and conundrums. She explained: “What I like about data is essentially it is a little bit like putting a puzzle together. So you've all these little bits and pieces and on its own that piece of data doesn't really mean a whole lot. But if you stitch it together, then all of sudden you've got a full story that can explain a lot of things and I love puzzles. For me, data is just like that.“

To encourage more women and people from other diverse groups to enter the data sector, Kuzalti thinks that initiatives that train women in coding, such as CodeFirstGirls, are a good way to go. “They are excellent at helping get more women into data because by having those tech skills, they've got a way of accessing the data.”

She did say, however, that perhaps there could be a way of targeting such initiatives more specifically because certain groups of women seem to be better represented in this industry than others. “Data for women in Asian cultures is already quite entrenched, but how can you expand it so it includes everybody?” she questioned.

"Put aside preconceived ideas of what a person can do."

To attract as wide a range as possible into the field, Kuzalti thinks that people need to be shown practical applications demonstrating “cool things you can do with data.” This would be beneficial as there would a focus on the outcome and not just the programming.

One thing that would help women already working in data, as well as those who might be attracted to the field, would be a concentration on the individual coupled with a dismissal of preconceived notions, according to Carpenter. She said: “Put aside preconceived ideas of what a person can do based on gender or ethnicity or anything else. Just focus on the individual and give them opportunities that work for them.”