Iain Brown is the head of data science at SAS UK and Ireland. He is also an adjunct professor, one of Data IQ’s top 100 most influential people in data and was recently chosen by ONALYTICA as one of the 50 influencers to follow on the topic of artificial intelligence. He has over 32,000 followers on LinkedIn and 110,000 followers on Twitter as of December 2022. I caught up with him to talk about data science, and the challenges and pleasures of teaching it.
Iain, how did you become a data scientist?
Funnily it was never really part of the plan to become a data scientist. I have always had a love for mathematics and a curiosity to explain how and why things are. This led me to pursue applied statistics at university, which led to a postgraduate degree in Operational Research, which is simply applied statistics for business problems, and then onto a PhD. However, at that point no one was talking about data science: that was a term that hadn’t yet been invented. I wanted to work in business and find ways in which I could use data analytics to understand how things worked and could work better, and I ended up falling into data science purely out of luck!
Besides being head of data science in SAS UK and Ireland, you teach marketing data science at the University of Southampton. Why did you decide to teach?
I completed my own PhD at the University of Southampton, so I know the university well. Going into the world of work, I was always keen to give something back. One of my fellow PhD students is now a professor at the University of Southampton, and about six years ago, she told me that they were looking for someone to teach an applied marketing analytics course, so I jumped at the opportunity. I was able to redevelop the course, drawing on my knowledge and experience over the last 15 years of applying these techniques in the real world. I’ve turned it into a very practical use case-led course. I think it’s incredibly important to build strong relationships with universities, because we need to nurture and develop the next generation of curious minds to solve tomorrow’s problems.
How do you describe data science to your students when you first start teaching them?
A lot of students are not aware of the potential of data science and what a career in this field would entail. There are plenty of courses on offer, but they don’t always join the dots between mainstream subjects like math’s and stats, science and engineering and what data scientists do. Coming from a data science background and working in that space, I always start by explaining the value data scientists provide, both to organisations and more generally through how it effects our day to day lives. I try to bring data science to life by showing students its practical applications, how data is transformed into intelligence and how businesses, public sector bodies or healthcare providers can use those insights to make decisions that affect people’s lives.
From your experience, how has the use of data science changed in business?
I’ve been in my current role just over 11 years now. If I go back to when I started, it was an uphill struggle to explain to businesses the benefit of using data based decisioning over more traditional gut feel. There wasn’t a common acceptance as to why you would use data to make decisions instead of drawing on personal experience and knowledge. Fast forward to today and most organisations appreciate the value of data and if anything, they want to do more with it. But even now I would there are huge opportunities to do more with data with 70%-80% still underutilized in unstructured form (e.g., free form text, conversations, or images). Most of my work today is exploring new use cases to get more out of data. The focus has changed from why should we do it to what else can we do with the data?
What is the data science project you are the proudest of so far in your career and why?
One of the projects I personally most enjoyed was working with a large online digital retailer, helping them to understanding the patterns and behaviours of customers and to make accurate real-time personalised recommendations to their customers. Working on that was incredibly rewarding and added a lot of value to the business. However, I’m prouder of the work my team has worked on for Data4Good projects, be it using computer vision to transform kidney treatment for the NHS or using AI to save mothers and babies through early diagnosis of pre-eclampsia. Being involved in an organisation that puts societal benefits at the forefront of our work is fantastic. It goes beyond the business value that organisations can achieve, and towards the human-centric value that all of us can benefit from when the right people, the right skills, the right data and the right capabilities are put together to address key societal challenges.
Under your LinkedIn profile https://www.linkedin.com/in/iainljbrown/ we can find many interesting insights, articles…if students asked me to give them an advice what to read/follow in the topic of Data Science and Data Analytics I would recommend your LinkedIn for sure but what are your recommendations for other sources of knowledge?
I’m a big fan of podcasts, medium blogs and YouTube series. I’ve found these are great ways to ingest information and to catch up with it on the go. I can certainly recommend the following; Lex Fridman; Data Science Central; and StatQuest with Josh Starmer.
Thank you, Iain.
