Industry Leaders on the Future of Data Science with Gavin Allan


In our new blog series, The Future of Data Science from Industry Leaders, we asked industry leaders from across the UK what they expect to see from the industry in 2022.

In this edition, Gavin Allan, a data scientist at a large UK bank, shares his thoughts on how businesses can prepare for the coming year.

Gavin’s work runs the gamut from data science workshops with businesspeople, exploring how data science can be applied to problem solving, modelling and implementation and data visualisation.

Gavin’s advocacy for data visualisation comes from the practice’s ability to transform complex analysis into an easy-to-understand format that’s usable for businesses.

Gavin, thanks for joining us. You work in financial services for a large bank here in the UK. From your perspective, what should businesses and data scientists expect from 2022?

Speaking more broadly, data science is maturing as a business function, and because of this I expect to see more scrutiny both internally, from within organisations, and externally, from industry regulators.

Internally, I see this meaning a stronger spotlight placed on the commercials of data science and ensuring return on investment.

Externally, industry regulators are increasingly turning their attention to data science and as a result internal governance over how data is used will continue to grow. This is a change happening across all sectors but in some, the pace of change is more rapid than others. The challenge will be balancing the need for data governance without stifling innovation.

How can businesses balance the need for data governance without stifling innovation?

Within organisations, I expect to see a broadening of skillsets typically contained within a data science function to encapsulate more project management – to bring delivery rigour – and data product skills.

By data product, I mean people with a commercial focus and technical awareness, but not necessarily a technical skillset, who can engage with the business and design data science products that deliver bottom line value – these products are then built out by data scientists.

This is crucial as the elusive data science unicorns that combine business and technical skills are few and far between so while there will be some crossover between data science and data product, I think it’s important to have both.

All of this is taking place against the backdrop of work from anywhere and global workforces. What’s your advice for people managing remote teams?

Don’t overlook the importance of non-work-related conversations that enable colleagues and teams to build relationships and understandings of one another. It can be all too easy to let this fall by the wayside in place of project related meetings so it’s important to be actively building in space for it – it improves communication and results in a more effective team.

The number of fully remote roles has greatly increased, and I don’t see this falling back to how it was before where there was generally a need to always be close to the office. The reason being, for employers, not being restricted on location opens the talent pool significantly which is especially helpful for Data Science roles where specific skillsets can be harder to find.

I also see the new model that is emerging of split working between the office and home as a positive for data scientists. It’s important to have time to engage with business stakeholders and of course, your team, but it is also vital to have ‘focus time’ in which to develop and to research new methods. The new hybrid working model is going to help provide this space.

What are the biggest challenges that data scientists face?

I think how data science is viewed by the business side of organisations is a key challenge. On one side of the fence there are those who see it incorrectly as a silver bullet to all problems and on the other there are those who are either sceptical or distrustful of it – there is a happy medium that needs to be found between the two. I do think how data science is portrayed in the media plays a big role in creating these preconceptions.

I see it as a key part of our role in the Data Science industry to build trust with businesspeople and work with them to better understand what we do. Building this trust and understanding means business stakeholders are far more likely to be supportive of data science projects and crucially, act on their outputs. We need this action to deliver commercial value from data science.

What’s your advice for new data scientists entering the industry in the current environment?

Over the past few years, I’ve learnt a lot of what I know by learning from others in my team. With remote working this doesn’t come as naturally as you need to go out your way to initiate conversations. If you’re starting out in the industry don’t be afraid to ask questions and put in time to learn from others – it is expected, and you will stand to gain a lot by doing so.

More broadly, my advice to new data scientists is that you don’t need to, and can’t, know everything. My approach has been to have a high-level understanding of a wide variety of techniques and then to dive deeper into individual methods as and when I need to use them. Having business awareness and knowing which technical approach to fit to which business problem will set you apart.

Diversity and Inclusion conversations are, rightfully, happening more and more. What can the industry do to diversify and be more inclusive of diverse talent?

Absolutely, I think a key part is pro-actively reaching out to D&I groups and putting in the time to hold events with their members to de-mystify what data science actually is. For those not in the industry, I think data science can be quite an intimidating prospect, when it doesn’t need to be. These events help make it more accessible and show that you don’t need to have a PHD in Data Science to consider pursuing a career in the industry!

Separate to this, I’m involved in D&I mentorship programmes where I mentor those interested in pursuing a career in data – this is something I’d highly recommend if you’re not involved currently as the learning goes both ways and I’ve learnt a lot by being involved in the programme.