Diversity in analytics: how to overcome the obstacles and optimise the opportunities

In this guest blog, Jennifer Major, head of  internet of things (IoT) at SAS UK & Ireland, discusses the obstacles and the opportunities involved creating diversity in analytics. 

In the face of the constant soundbites we hear about the British tech sector’s “worrying” lack of diversity, what are the steps being taken to resolve this mounting problem?

Constricting context

Globally, the context and cultures groups find themselves in have restricted their potential to excel in tech. Although progress has been achieved towards equality and increasing diversity, it has not been a straightforward process. There is still an uphill battle to be won.

It has to be clarified that this is not about “political correctness”! Increasing diversity in tech makes business sense. The impact that these previously excluded groups can have on science and analytics needs to be realised and harnessed.

The doorway to diversity

Many of the obstacles facing excluded groups are cultural. Habitual bias, or the assumption that a certain race, gender or religion is less capable, can exist in many situations, whether explicitly or implicitly. This might be the case for educational institutions, employers and recruitment processes, or just the domestic environment in which people grow up.

To break down the barriers of exclusion, our societies, institutions and decision-makers need to be made more representative. Diversity isn’t only important to a single race, religion, gender or group – depending on the context, the barriers of bias can limit anyone’s potential.

Educating earlier is the way to encourage greater diversity in science, technology, engineer and maths (Stem) subjects. By focusing on young people as a group and ensuring they have a level playing field on which to start their journey, we stand the best chance of increasing diversity in the field as a whole by helping to build a meritocratic industry.

Equality of educational opportunity is not a new idea, but the need for it is real and pressing. Indeed, in some advanced economies we seem to be moving backwards. In the US, for example, the number of computer science bachelors’ degrees awarded to women peaked in the 1980s and has been dropping ever since.

At the same time, however, there are causes for celebration. Rapid progress has been achieved in BRIC countries where, despite challenges, large numbers of women are eschewing arts subjects for degrees and careers in technology. In China, Brazil and Mexico, women make up 36%, 38% and 45% of the total IT workforce respectively. This is compared to only 17% in the UK.

To make further progress at home, we need to address perception problems towards Stem subjects. Old stereotypes of the sciences being the preserve of middle-class white men or awkward social rejects are being undermined every day. Yet those ideas still shape public thought. If children are repeatedly given perceptions that Stem is for a certain or special kind of person, it can reduce the chances they will pursue careers in maths or science.

We are often told that Stem skills can be the foundation of a lucrative career, but is that the most effective message to get children’s attention? In the end, we need to do more to convince future generations that Stem can be fun, fulfilling and useful. Maths and science aren’t all about dry formulae: they are practical and powerful, driving amazing innovation everywhere.

Analytics for everyone

Success in data science depends on a wide range of complementary skills. As data and analytics move closer to the top of the corporate agenda, the ability to communicate simply and inclusively will be crucial.

The real power of analytics is that it democratises data for everyone, from the boardroom to the factory floor and everyone in between. That means that the demand for more skilled staff in analytics is increasing day by day. Despite the rise of Artificial Intelligence, the crucial role human analytics teams play in extracting valuable insights from raw data cannot be ignored.

Businesses need to ensure they are recruiting as widely and with as much diversity as possible to ensure they get the most skilled people for the job.

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