Sadie St. Lawrence, Founder of Women in Data, never saw herself entering the world of Data Science. But at university, while undertaking a piano major, it occurred to her that perhaps music wasn’t the route for her. Halfway through taking General Education, Sadie found that she fell in love with the scientific method and the rest is history…We caught up with Sadie on her journey into Data, her passion for diversity and inclusion in the industry and the launch of Women in Data. As we celebrate International Women’s Day, we’re looking to commend those women who have been an inspiration to others, and you certainly come to mind. The work you have done within the Data & Analytics industry has been enthusing to so many. But as we mentioned, Data wasn’t your first calling – tell us more about your journey. My career into data certainly wasn’t linear and is a world away from my dreams as a little girl. Like most people in the Data world, I certainly wasn’t born a data scientist. My journey into this role came from an amalgamation of all my interests and my skills, rather than just because one day I woke up and decided that I was going to study Data Science.I began my life wanting to be musical, which then evolved into a piano major as I discovered a real passion for the instrument. But, along the way, I started to think logically about what I both wanted and needed from my life. Was piano teaching really going to pay the bills in the way I needed it to? During GE at university, it became clear that I was drawn to the scientific method. And looking back on it now, what I was learning wasn’t too dissimilar to my musical skills. Whether reading sheet music or analysing data, I loved seeking out patterns, getting them to make sense and creating a beautiful outcome. Putting my love for music and science together, I became invested in psychology and neuroscience, specifically looking at how the brain interprets music and emotion. And this is ultimately how I made the transition from music into data. I began working in laboratories to undertake my research and was introduced to the world of Data Science through that.What an interesting journey. You speak a lot about how the skills you learned through music were key to moving into the Data Science world. Other than being able to read music, what else did you take with you?Data Science and music are both incredibly creative subjects – even though the former may perhaps not be known for that. But when you’re trying to solve a problem, the best way to do so is to think creatively. You need to come up with new and innovative solutions and you can’t do that without thinking outside of the box. That mindset of creativity and expression that so many link with music is crucial in the data world, as is the ability to think critically. My strong creative foundation was, and still is, a defining factor in my Data career. That’s such an important crossover. It’s certainly true that without creativity, Data wouldn’t thrive. Of course, you started your Data career and very quickly created Women in Data, launching in 2015. Could you tell us a little more about why you began this community – what was its purpose and what was your vision?So, Women in Data started with a personal need for community and a vision for equality in the Data space. I was new to my Data journey back then and I felt that for me to survive as a female, I was going to need to lean on other women who I could collaborate with, learn from and work alongside.At the same time, while I couldn’t predict the sheer scale we are currently witnessing, I knew that Data was going to be big. And I thought to myself, if there are going to be all of these amazing economic opportunities in just a few short years, how can we ensure that women have a seat at the table? Women weren’t represented in that space, and it was a huge issue both for women and for the industry. Look at where we are today, so much of what we do is driven by data. In fact, most of what we do is driven by data. But, if we don’t have diverse representation, we’re not building products, services and algorithms that are suitable for everyone within our communities – and that is a large and complex issue with problematic knock-on effects. That is so true. If we don’t address this, then we’re at risk of creating huge problems within society. Of course, it’s no secret that the data industry still lacks good levels of female representation. From your point of view, what would you like to see business leaders do more of to help address this imbalance?For me, the first step is to look at retention. I know organisations are desperate to get female talent into their workplaces, but what about looking after the women that are already there? Even if you only have one female member of staff, you need to ask yourself – what am I doing to fully support this person? Is our current culture right for this person and if not, what are we going to do about it? You are going to be hard-pressed to attract new female talent if your current way of working doesn’t fit with the needs of your existing members of staff. Additionally, it’s crucial to set targets and KPIs for your diversity and inclusion goals. Not only do you need to know what’s working within your business, but data will help to show you where you might have pitfalls. For example, is diverse talent leaving your company at a faster rate than your non-diverse talent? If so, then it’s time to deep-dive into the reasons behind that and work hard to fix it. The more knowledge you can absorb about the inner workings of your own company, the better. Following on from this, what more can society as a whole do to encourage more girls into Data Science and STEM?We start to see a real shift in young girls around age nine and 10 – also known as the STEM cliff. It’s at this point where children become a lot more self-aware, they are listening to and absorbing the stereotypes around them and that begins to build their beliefs and values. This is the most crucial point in which all systems, from education to media to families, need to work hard to neutralise subjects such as STEM and not reinforce negative stereotypes. These children need people that they can look up to and be inspired by. Who did you look up to when you were younger – who inspired you?For me, the role model I looked up to was my future self. The reason I started Women in Data was because access to role models in this field was rare, and so I needed to take it upon myself to be a person that future me would be proud of. At the end of the day, I can look up to strong women like Oprah, Sarah Blakely, and Cleopatra – but I am never going to be them. While I can take little bits of them, their personalities, and their motivations, ultimately it was more important that I was my own person and my own role model. Every day, I strive to be the best version of myself that I can be. To learn more about Sadie and Women in Data, you can visit their website here.
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