Insights

Women in Data: Interview with Caroline Worboys, COO & Founder of Outra

Interview by Lydia Brand, Data and Analytics Consultant at X4 Technology.

I had the pleasure of interviewing Caroline Worboys, a Digital, Data and AI Executive, Advisor and NED, to delve into her 35+ year career and discuss some of the biggest challenges and exciting developments happening right now.

With the number of professions in data constantly evolving, we delved into the challenges employers face in finding the right experience and skills, how data professionals must consider where they’re best placed in a business and how we must continue to talk about the wide-ranging opportunities in data, as these can be suited to all types of people, not just the super technical.

What’s been the most memorable moment in your career to date?

The most memorable moment in my career to date was when I sold my first business. It was both nerve-racking and exciting all in one because it was a move forward, but I wasn’t quite sure what it would result in. I ended up spending eight good years at News International and significantly growing the business.

What kind of improvement, change or growth do you want to see in the data industry?

There are now so many roles in data that encompass many different disciplines and as it expands as an industry, you find more offshoots coming from it. It ranges from the highly technical, to those who are more strategic and those who are very creative in data visualisation. However, there is also a great and growing need for people who understand the context of data and how to use it to drive new value across marketing and the wider business. An ability to be logical and see patterns or trends is an advantage. Being able to tell stories with data is key.

It’s difficult for organisations to build a hybrid team of technical, soft skills and personalities, in order to unlock that value. The trick is finding the balance for the stage of maturity you are at with your data strategy and really considering the right team mix. However, I am encouraged as we are seeing more people understand what they have to offer beyond just technical skills. Many individuals are actively choosing and making good decisions about the future direction of their career, be that leadership, consultancy, engineering or a super league model builder. Thankfully, it is being recognised that just like other roles within organisations, some of the most talented people want to be externally facing, some want to focus on code and others just love engineering.

Most employers can’t afford to employ everybody at once, so it’s about people being much more precise about the value they bring. There’s not going to be very many commercial employers who really understand all the elements on someone’s CV and how all those technologies talk to each other, so that’s where a lot of the data scientists have to get better at articulating how they fit in to the puzzle, to stand out from the others.

What would you say are the biggest challenges facing data professionals right now in your industry?

There has always been a challenge with data sources, that’s no different today, but there are more sources, they move faster, are more prone to change and they are all big data sources. It is critical to focus on the outcomes required by the organisation and mapping this back to how this source data is transformed in order to create reliable predictions and models. Without this, driving new value is less likely. Having a clear data strategy is now more critical than ever.

From a people point of view, I’d find staff who like, understand and are curious about your industry. There are people who have reached a stage where they say, “I’m a data scientist” or “I’m a data engineer” and they don’t really mind which industry they’re in, whereas we need to slant towards people saying “I am a data engineer and I’m an expert in consumer data” for instance.

People need to be encouraged to understand where they fit in the bigger picture. People have developed the skills of using the tech, but there’s this gap where someone might have 4 years’ experience in data science, but they haven’t led a team before or worked closely alongside the engineers or leadership team. They might have just done their bit and never understood what the actual outcome should be. Engaging them to be interested in what outcome their work drives, is the best approach, it brings in ideas across the business.

Reports show that as few as 15% of data scientists are female. What advice would you give to businesses who are struggling to attract and retain senior female talent?

I support Women in Data and they hold an annual conference for thousands of people and have some very good statistics on the people coming into the industry and they have done a great deal of work around getting girls into data. There are definitely a lot more initiatives like that, which are encouraging more females into the industry. Then there is DataIQ, which has been doing a wonderful job of raising the profile of senior Executives within the data industry and has identified strong female leaders, who were previously without a presence.

It comes back to, what does data mean? I’ve been doing it for 35 years and no one knew at all what data was back then, it was a small discipline and it was full of men. Clearly, it has changed, and become digital, there are more channels and techniques but a lot of the principals of using data to drive new value, are not radically different. So, I think it’s sharing stories of success with data and inspiring people to seek jobs they never knew existed. TV voting involves vast amounts of data, but when we started that back in early 2000, the data wasn’t even digital. The process and principal is still the same now and most people at the time didn’t even think of that as a job in data.

“Data science has an image problem.” This has been said a lot in the news recently, what would be your advice to employers who want to change this perception and create a more inclusive culture in their business?

I haven’t had much experience of data science having too much of a bad impression. My only experience is that it has become quite ‘buzz word’ driven. Like anything that tends to be new, everyone who was an analyst has now become a data scientist. However for me, as simply put as possible, data science is about working with big data, from multiple sources and over a large period of time to predict outcomes or find patterns that humans could not. This in turn helps the organisation create more compelling outcomes or market differentiation. However, I do believe data science can attract bad press if an organisation employs one or two staff and expects them to be able to do everything, from model building to engineering, project management and data strategy.

What excites you most about the recent developments and the future of data science and AI?

Over the years I have provided consultancy and service to hundreds of clients and I have seen how data fuels the digital economy. In the past, presenting data has always been about confirming and describing what the data tells us has happened. Now I am very excited, as finally with the advances in cloud computing and data science, we are revealing new insights and patterns that are genuinely revealing and challenge corporate bias. This leads us to new ideas and new challenges and ultimately better outcomes for customers, businesses, supply chain, medical research the list goes on.

One good outcome from Covid-19 has been the introduction of graphs and predictions into everyone’s living room and mobiles. If you had said to someone before this, “It’s like a bell graph” a lot of people would not have understood what you meant, but now you can say, “You know, a graph like Covid-19, it predicts what may happen” and even my mother can go “Oh yes, I get that now” so, there’s been a subtle education process. I am also convinced that it has brought STEM into the realm of genuinely exciting with real-life application of data.

For businesses after Covid-19, they will have to do things completely differently. How we all work and where we all work will change. Predicting next year sales for example, based on simple trends over the last 3 years, just got a whole lot harder. This creates great opportunities for the application of data science and for other roles in the market.

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