I started my career in academia in the 90s. This was long before data scientist was crowned the sexiest job of the 21st century, though I was fascinated by the ways in which statistical techniques could turn data into predictions. I used statistical modelling and simulation methods to build a budget forecasting system for a local health authority; the type of thing that would be called machine learning today.
I worked at the BBC for a couple of years, using similar methods to predict the demand for technology services, after which I made a move agency-side to Wunderman. We would pride ourselves on our ability to create great marketing experiences by marrying creative thinking with deep customer insight based on analytics, and I loved the opportunity to use data and analytics to understand people better.
After a few years, I moved to Dunnhumby, where I ran the UK’s analytics and insight teams for Tesco and other clients. I later moved to MoneySuperMarket, where I became chief data scientist. It was great to be able to build a new team for an online business, where we could use data science to create personalised customer experiences.
I joined Aviva three years ago and I am delighted to be helping one of the UK’s oldest financial services companies become a “324-year old disruptor”. I’m responsible for ensuring that we maintain a class-leading capability by continually evolving our technology and data assets, as well as ensuring we offer our 500+ data scientists across 16 countries the best training and development opportunities.
Last year, I was honoured to be ranked number one in the DataIQ 100, and at the award event it was amazing to receive this accolade with so many current and former colleagues cheering me on from the audience.
Did I mention that I was ranked number one in last year’s DataIQ 100?
Well, it was Andy Warhol who said: “They always say time changes things, but you actually have to change them yourself.”
Last year, I predicted a greater focus on the ethical issues around the use of data: fairness, transparency, explainability and bias. We’ve certainly seen a huge focus on data and digital ethics, but I think there’s a lot more to come.
It’s an area that we’re hoping to contribute to ourselves. We recently announced a partnership with Cambridge University, through which we are researching a number of hot topics, including new ways to explain the way algorithms work. It’s a buoyant field of research, with several competing approaches, and I’ll be interested to see how the area evolves.
As well as the continued visibility of the ethical debates, I think we’ll see more and more collaboration between smaller start-ups and bigger, more established businesses.
We already see many examples of start-ups making creative use of new technology to analyse data in ways that are fast, innovative and that have the potential to create huge value for customers. But it’s typically bigger companies and brands that have trusted customer relationships and data-sets – it’s only through collaboration that consumers will see the real value.
It’s amazing to see the ways in which technology is revolutionising developing countries. Innovative thinking, combined with new financial models, has given vast areas of the world access to data and technology at an unprecedented rate, and we’re now seeing countries such as Kenya emerge as technology hubs. It’s always exciting to see an area continue to grow while including a more diverse set of perspectives than ever before.
As with many large organisations – particularly within financial services – we have a large number of legacy systems. Building data solutions in the cloud enables us to use contemporary technology without compromising security or privacy, but data migration can be a slow process. There are so many opportunities to use data to create better experiences for customers, so I’m sure many of our data scientists feel frustrated that we can’t attack them all, but we’re always making progress.