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08 Mar '21

Diversity in Data: A Graduate’s Perspective:

By Suniyah Minhas

‘Terrifying’ in any thesaurus should be a synonym for ‘life after university’. Not even a graduate and barely a functioning adult, you finally have to answer the dreaded question ‘what do you want to be when you grow up?’. The reality sinks in, unlike when you were ten and had dreams to be a Disney channel back up dancer …. now you have no idea. 

And who do we turn to when we have no idea? Those who are often equally clueless– our friends. You discuss career paths together and often end up applying to similar graduate schemes, industries and locations. You finally find a position, keen to meet new people, and realise everyone has applied with their peers, similar groups of people have ended up in similar roles – suddenly the diversity that everyone seems so keen to promote in 2021 doesn’t look so easily achieved.

For me as a female British-Pakistani graduate, this came as no surprise. Although I thoroughly enjoyed my Physics degree, only around 1/5 of the undergraduates were female and even less from a BAME background, often making me both the only woman and person of colour (PoC) in the room. So, when I entered the exciting world of data, I was not incredibly surprised to be in the same situation within our small (5) graduate cohort. Despite the data world’s infancy, it is clear it has inherited the same problems as its parent sector; Technology. According to a report from Inclusive Tech Alliance, 65% of boards in top UK tech firms had no female directors and 74.5% of boards had no PoC members.

The numbers are alarming, but not necessarily shocking. This disparity is felt at all levels particularly for women/non-binaries and BAME groups. With BAME, it is important to not always look at them as a homogenous group; as a South Asian I can recognise that we have a larger Technology representation than the Black community (the Evening Standard estimated only 3% of Technology professionals within London were Black, in comparison to 13% of the London population). It is clear there is a problem that needs to be addressed. 

Since entering the workforce, I am eager to be at the forefront of resolving this problem. This is ignited by the fact that Data/AI is an emerging and expanding field, which is set to become an integral part of the worldwide economy. Currently we have only scratched the surface of the impact this sector will have on our lives. Everything from who gets interviewed for their dream job to who gets accepted for a visa will likely use the influence of the data industry and thus the influence of a non-representative group of people; unless we take action now.

This is why it is crucial to resolve these issues, before they grow into wider systemic problems engrained within our society. Indeed, this is not a new concept, in 2015 Google issued an apology when its AI software could not recognise the faces of Black people, and Amazon had to scrap its AI recruitment tool which demonstrated bias against women. It begs the question, if there had been more diversity in the teams constructing the AI and datasets, would more questions have been asked at an earlier stage? 

Aside from the clear societal impacts a diverse workforce would provide, there is also a strong argument for the business benefit of diversity. A 2020 report from McKinsey indicated that companies with better gender diversity on executive teams were 25% more likely to have above average financial performance, with ethnic diversity this figure rose to 36%. This demonstrates that a diverse workforce is no longer a desire but a necessity to thrive in a world where consumers and clients are becoming increasingly globalised; businesses must appeal to a wider net of people in order to survive.

The McKinsey report also highlighted a large disparity in progress between different companies, with some firms managing to fully embrace changes and make genuine gains and others making extremely slow progress. For me, this demonstrates how diversity is not necessarily difficult to achieve, real improvements can be made if goals are set and become a legitimate priority to executive boards. 

Although this should be the case, the Data/AI industry often argues that their lack of diversity arises from the available pipeline, for example a lack of females with a Computer Science (CS) degree. However, I have concerns with this as an explanation for the problem – particularly within the Data segment. It suggests that to break into this sector, having a developer background is a necessity; this is simply not true. Looking at our own Directors within DTSQUARED there is a large variety of educational backgrounds, many of which are not CS related.

At a more junior level, there are many products throughout the data landscape such as Snowflake, Collibra, Informatica and Power BI which no undergraduate degree covers in depth. Learning new skills is part of the process of joining the working world, from whichever discipline you graduated. Further to this, many of these tools do not require specific developer languages, meaning their success is not prohibited by your technical experience.

Computer Science degree or not, everyone has a chance to thrive working with data. This preference for computing backgrounds also overlooks the importance of softer skills which are necessary to deliver successful implementations within a team. It therefore makes minimal sense to restrict your recruitment pool to CS graduates only; last year DTSQUARED widened its search – interviewing graduates from a range of disciplines. This made a genuine impact, with our new graduate pool 50% female.

In this blog post we’ve covered the issues surrounding Diversity within Data, however I want to emphasise that diversifying your workforce is not a complete solution, it is integral that once minorities enter the workplace they feel part of the wider picture. This means using more inclusive language and encouraging a culture where concerns can be raised. Without inclusivity all the efforts made to increase diversity can be fruitless, as retention rates for minority groups plummet. I am proud to work for a company that holds a culture of inclusivity, support and acceptance at its core. The complexities around diversity means there is not a ‘quick win’ solution for any firm, but by keeping the issue at the top of the company’s agenda you can expect to see lasting change and I know that DTSQUARED are committed to this journey.

I would like to end this blog by pointing out that I have focussed mainly on PoC and Women in Data as this reflects my personal experience. However, there are many other minority groups that feel under-represented including but not limited to; LGBTQ+, non-binaries, carers, those with learning/physical disabilities and mental health difficulties.

There are many blogs and platforms available discussing these issues in more depth, for example Jennifer Opal has a blog discussing learning to code with ADHD as well as her experience as a Black Woman in Technology. 

Thank you for reading, hopefully together we can work on a more diverse and inclusive future for the world of Data. Please click here if you want to sign up for future blogs on this topic, you can also  join our Diversity in Data community, launching soon.

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