This is a repost of the original article which you can find on the DataIQ 100 website for 2020.

Path to power

My background is in the manufacturing industry, where I first started working with medium sized German firms to improve their output or productivity. This seemed like an obvious move at the time, having a degree in manufacturing engineering, where I was able to carry on the technical problem solving that I’d enjoyed so much in academia.

I joined in 2015, shortly after its launch with a strong skillset in tough problem solving as an “analyst” but not really as a “data professional”. What was different about this new environment was its start-up culture, where change is the norm (and a necessity) rather than an optional extra. That, combined with the beginnings of a necessarily data-centric business, was the beginning of my immersion in the world of data. Since then, I’ve built out the analytics, data science and data engineering functions for, all focused around that central ambition of helping people make great decisions.

What is the proudest achievement of your career to date?

We know that the data and analytics industry has had historically poor diversity. What continually surprises me is that we’ve built a relatively diverse team in our data function at, not just in the headline things like gender and background, but also in arguably more important things for an analytics function like diversity of thought. While I’m not sure precisely how we’ve achieved that, it’s still something that makes me very proud.

Who is your role model or the person you look to for inspiration?

This is a hard question, as I don’t think there are particular “role models” that I’ve looked to for inspiration. What I have had are a large number of people I have turned to for practical advice, and each one was most useful at a particular stage of my journey. If I had to name two, I think Gaurav Gupta played a massive role in my thinking toward careers and workplace culture while I was at Stroud, and more recently Caroline Carruthers has shaped a lot of my thinking around data strategy and how to bring people along that journey.

Did 2019 turn out the way you expected? If not, in what ways was it different?

No, but I think that’s what I expected. One of the beautiful things about working in a small and fast-growing business is that change is the thing you can always rely on. Of course, I had an expectation for what I thought 2019 would bring, but by the time we got to February it was already way off.

What do you expect 2020 to be like for the data and analytics industry?

I hope that we’ll see less and less emphasis on tools and technology. It feels like there is a slowly growing trend of people who are tired of new tools and are realising that the hard problems we are faced with are cultural. I’m expecting that the conversation within the industry will move more in that direction, perhaps as a sign that data is maturing as a field.

Data and technology are changing business, the economy and society – what do you see as the biggest opportunity emerging from this?

We talk a lot about democratisation of data, but I think data will also democratise many personalisation services that have previously only been available at great expense or to a subset of the population.

Old examples are things like personal insurance broking and estate agents have been online for a while, newer examples are in customised food delivery, mortgages and clothing. What I’m excited about are the next wave of personalised services being available at scale in fields like nutrition and healthcare – where arguably the potential benefits are huge. What we’re doing at right now is scratching the surface of this in pet nutrition but this is only the beginning of something much bigger.

What is the biggest tech challenge you face in ensuring data is at the heart of your digital transformation strategy?

As a business “born in the cloud”, we don’t have a separate digital transformation strategy at Data has been at the heart of what we do since day one – and without it our product wouldn’t function, and we wouldn’t be able to make the kind of decisions at the speed that we do. The biggest tech challenge is therefore to remain lean, and not get seduced by more and more tools and technologies as we grow.

In a perfect world that means choosing wisely up front, but in reality, we never have perfect knowledge and therefore we need to get out of things just as fast as we get in. That means being really disciplined with legacy. Every single piece of legacy software we don’t retire slows down how quickly we can change and innovate. As we grow, the pace that we on-board new tools will only increase, but so must our ability to off-board tools.