In an inspiring interview, Ingrid Verschuren, Senior Vice President of Data Strategy at Dow Jones, shares her unconventional path from linguistics to data strategy and her role in shaping the industry’s future.

What inspired you to pursue a career in data strategy, and how did you get started?

It was never my intention to pursue a career in data. I come from a linguistic background, starting my career manually indexing news stories in Dutch, German, Spanish and Portuguese into the business intelligence platform now known as Factiva. As the data and technology landscape has evolved, so has my role. Twenty-five years on, I’m currently based in Barcelona and lead our global Data Strategy team, which collects, maintains and enriches the data that powers our different products at Dow Jones. 

Something I’ve always felt passionately about is taking ownership of your career. If you don’t share your aspirations, it is very challenging for people around you to help and support you. For example, early in my career, I made it very clear to my manager that I would be willing to relocate for the right opportunity—no matter where it was. It didn’t mean I was expecting something immediately, but voicing my intentions allowed my manager to support me. 

As a woman in a tech-heavy environment, what challenges have you faced in your career, and how have you overcome them?

Throughout my career, clients or colleagues have assumed I don’t truly understand the technology I work with. And even now, I often find I’m the only woman in the room. But I haven’t let that hinder my growth. Having said that, I believe being around women enables other women to be their authentic selves more. This points to the importance of female leadership when creating a supportive culture for women.

I’m very proud to have built a senior leadership team within Data Strategy that is majority female, which cannot fail to inspire the next generation of women in leadership, helping them recognise and achieve their full potential.

Can you talk about a time when you had to break down stereotypes in your career, and how did you go about it?

There have been several instances where I’ve noticed cultural or gender bias. But sometimes, you can use stereotypes to your advantage! For example, during lengthy negotiations with a partner, the conversation had become stagnant, and we were not making any progress. I played on my Dutch “directness” to drive the discussion forward, which ultimately enabled us to come to an agreement. This is a great example of where building a diverse and inclusive workforce produces really positive results and is something I certainly see here at Dow Jones. 

How has Dow Jones’s hybrid-first approach facilitated diversity and fostered a purpose-driven organisational culture?

I have always been a strong supporter of hybrid working. My team implemented this approach as far back as 2006. I believe this has contributed significantly to my ability to build a predominantly female leadership team, as it encourages and enables women to balance work and family life.

It’s not just about where you are working but when you are working, as a truly flexibility-first culture allows people to fit work commitments around other needs and requirements. Offering greater flexibility also promotes a culture of compassion, which reduces the likelihood of negative competition or “elbowing” and, in turn, fosters a greater sense of belonging. 

What advice would you give to women starting their tech or data strategy careers?

Try not to be intimidated. Whenever you feel out of your depth or experience imposter syndrome (especially if you find yourself in a room full of men), you may soon realise you are more than other people around the table. This is something I felt at the very start of my career, and it has taken me a while to overcome. 

Ultimately, you need to own the choice to be confident. I highly recommend seeking a mentor or sponsor—even if they are not your direct manager. Talking to somebody who has faced similar barriers can be invaluable to helping you improve self-awareness and reframe your thoughts early in your career. 

How do you ensure that the data collected by your team is accurate and reliable, and what steps do you take to mitigate bias?

High-quality data goes to the heart of Dow Jones’s mission to be the world’s most trusted source of journalism, data and analysis to help people make decisions. We’ve found that a hybrid approach between automation and human intelligence works best when it comes to cleansing, structuring and maintaining our data.

Machines work faster than humans ever could. AI is able to scour a tremendous amount of data very quickly, recognising patterns and extracting relevant information. However, they lack the ability to understand context or linguistic nuances, increasing the risk of false positives, oversight and bias. Our people are, therefore, the experts who guarantee the quality of our data. 

Our global team of subject matter experts plays a vital role in training the machines. An AI model isn’t smart by itself—the machine is only as smart as you train it to be. Our people are also able to understand and evaluate the data better than any machine—spotting inconsistencies, misinformation and things that AI may have missed. At Dow Jones, we call this collaboration between machines and humans “authentic intelligence.” I truly believe that this unique blend of AI, machine learning and natural language processing, matched with our researchers’ expertise, is the source of our data quality. 

What trends do you see in data strategy, and how do you stay up-to-date with the latest developments?

As AI systems have become smarter and more sophisticated, there are many more opportunities to uncover unique and timely insights from vast amounts of text, transforming unstructured news data into actionable insights. Natural Language Processing (NLP) has been a real game changer for us. Every news article ever produced contains many data points about people, companies, entities and events.

In aggregate, this contributes to a huge pool of information that—when properly normalised, labelled and structured—contains valuable insights and signals about the world around us that can help inform business decisions, from identifying trading and investment opportunities to managing business risks. 

NLP models are no longer limited to English, enabling our researchers to index more than 600,000 news articles every day. Although developing models for different languages can be challenging, particularly for non-Latin scripts, it is an important step that global businesses like ours need to take.

How do you balance the demands of leading a global team with other aspects of your life, such as family and personal interests?

I believe work-life balance is something very personal. Just because something works for you doesn’t necessarily mean it will work for someone else. For example, before the pandemic, my job required a lot of travel, which would not have felt like balance, for some people. But I loved it, and I’m lucky to have a very supportive family, which means I never feel guilty when I need to prioritise my career. 

For anybody balancing their career with personal and family commitments, the most important thing to remember is that you cannot be in two places at the same time—and you need to learn to let go of that guilt. Understanding and embracing this truth is essential if you truly want to achieve work-life balance. 

How has the pandemic impacted Dow Jones’s data strategy, and what changes have you made in response?

Even before the pandemic, we have been looking to find ways to work more efficiently while remaining sharply focused on data quality—and the current economic landscape is accelerating this trend. Our global news database, Factiva, contains over two billion articles, growing by 600,000 new articles daily.

The evolution of AI enables us to process more data more quickly. This frees up time for our researchers to focus on enhancing that data. This is particularly important in our Risk & Compliance database, which contains millions of profiles covering politically exposed persons, adverse media, state-owned companies, and sanctioned individuals and entities. When a decision produces a legal or regulatory effect, missed information or misinformation could have huge implications for businesses.

What future developments do you see in data strategy, and how is Dow Jones preparing for them?

AI is never static—the technology available today is far more sophisticated and accessible than five years ago. Any organisation that remains stagnant risks getting left behind. Generative AI is the most significant recent development in this space. As with any AI system, however, it is only as good as the data we put into it. Even minor flaws will degrade the quality of the output, which will undermine the promise of this technology. As the world becomes increasingly complex and trusted, high-quality and licensed news and information, have never been more important. 

The adoption and full potential of generative AI remains to be seen, but our philosophy at Dow Jones is steadfast: AI is more valuable when combined with human intelligence. Our “authentic Intelligence” approach will help us manage these risks internally and for our customers in the coming years.