Julia Mittermayr on Data Quality, Bold Leadership, and Building a More Transparent Insights Industry

Editor’s Note: The following interview features a 2026 Greenbook Future List honoree, Julia Mittermayr. The Greenbook Future List recognizes leadership, professional growth, personal integrity, passion, and excellence in the next generation of consumer insights and marketing professionals within the first 10 years of their careers.

Julia Mittermayr, Chief Operating Officer at ReDem and a 2026 Future List Honoree, has helped scale a data quality startup into an international business while advocating for greater transparency across the insights industry. From launching collaborative initiatives like Quality Days to navigating ReDem’s international growth and acquisition, she champions decisive action, open knowledge sharing, and stronger industry-wide approaches to research quality.

Outside of insights, what are your passions and interests?

Outside of the insights industry, I am someone who really enjoys pushing myself physically and mentally. A big part of that is training: I spend a lot of time in the gym and especially enjoy strength training. I like the discipline behind it, the progress, and the fact that improvement comes from consistency rather than shortcuts. It is a great balance to professional life because it keeps me grounded and focused. Beyond sport, I love traveling and discovering new places, cultures, and ways of thinking. I am also genuinely fascinated by Roman history. I find it incredibly interesting how much we can still learn from the ambitions, structures, and strategic thinking of that time.

Who is your career role model or source of inspiration?

My biggest career role model is my grandmother. She built a manufacturing company in Austria in the spice industry and successfully drove its international expansion, which is something I find deeply impressive. What inspires me most is not only what she achieved, but how she achieved it. She was a true powerhouse: ambitious, decisive, and exceptionally strong in execution, yet at the same time honest, warmhearted, and deeply respectful toward her employees. She had the rare ability to turn ideas into action and to combine high standards with genuine support for the people around her. Especially in what was an even more male-dominated leadership environment than today, she showed what strong and effective leadership could look like. She also had a curiosity about the world and traveled to places I can only dream of, which makes her even more inspiring to me on a personal level.

Since starting your career in MRX, what would you consider to be your greatest accomplishment?

Since starting my career in MRX, my greatest accomplishment has been helping expand ReDem internationally and supporting its development from a growing data quality startup into a company with real global traction and a successful exit. Building a foothold across markets, proving the relevance of our solution beyond our initial home base, and helping position the company on an international stage has been incredibly rewarding. What makes this especially meaningful to me is that it was not only about commercial growth, but also about helping push forward a topic I care deeply about: improving data quality in research. A major milestone in that journey was the successful sale of ReDem to Rep Data just one month ago, a company that shares our deep commitment to data quality as well as our values and long-term ambitions. Seeing the company reach that point after all the effort, ambition, and persistence that went into scaling it is something I am very proud of.

If you could go back in time to when you first started your career, what advice would you give to your younger self?

First, get into action early instead of over-planning. You do not need to have everything figured out before you start. Some of the most valuable opportunities come from stepping into situations before you feel fully ready and learning as you go. Progress is rarely linear, and uncertainty is often a sign that you are stretching into something meaningful. Second, be intentional about your environment. The people you work with matter enormously. Surrounding yourself with ambitious, kind, and driven individuals accelerates your learning and shapes your standards much more than any structured plan or role description ever could. Third, take risks. Do not be afraid to step outside your comfort zone and say yes to opportunities that may feel uncertain at first. Sometimes that means taking on a role that seems riskier in the beginning, or even accepting a short-term pay cut because you truly believe in what you are doing and want to help build something meaningful. If you are fully committed, willing to work hard, and genuinely care about the impact you want to create, those risks are often the ones that shape your career the most. Looking back, I think some of the most worthwhile decisions are not the safest ones, but the ones that require courage, conviction, and a willingness to bet on yourself.

What advice do you have for folks hoping to start a career in insights?

If you are starting a career in insights, my biggest advice would be to be brave early on. Speak up, share your perspective, and do not wait until you feel you have the perfect answer. Some of the most valuable contributions come from asking questions, challenging the status quo, or offering a different angle. Very often, there are no stupid questions, only missed opportunities to challenge the status quo, contribute a new angle, or move a discussion forward. Secondly, lean in proactively. Take ownership beyond what is formally expected of you, get involved in topics that might feel slightly outside your role, and do not be afraid to stretch yourself. The insights industry rewards curiosity and initiative, and you will learn much faster by actively engaging rather than waiting to be asked.

Tell us about any advocacy/volunteer/association work you're doing within the industry. What issues are you trying to solve? Why is this work critical for the industry?

One example of industry-related work I have initiated is “Quality Days,” an independent, non-commercial format focused on sharing knowledge around data quality. I created this format to bring together different stakeholders across the industry, including agencies and brands, to exchange perspectives on how survey fraud is evolving and what approaches are proving effective in practice. By giving external speakers a platform, it enables participants to learn directly from real experiences and from each other. The main issue this addresses is the increasing complexity of data quality challenges. Fraud is evolving quickly, and many organizations are facing similar problems, yet there are limited opportunities to openly share learnings across companies. This is why formats like this are important. They create space for more transparency, faster learning, and a more collective approach to tackling data quality. Given how critical reliable data is for decision-making, I believe strengthening this exchange across the industry is essential.

If you could change one thing about insights, what would it be?

If I could change one thing about the insights industry, it would be how we approach data quality. I believe there is still a lack of transparency around what is actually happening behind the scenes. At the same time, I would like to see much more collaboration across companies and across different parts of the ecosystem. Data quality is not a problem that can be solved in isolation. It requires agencies, panel providers, and technology players to work together more openly, share learnings, and align on standards. Without that level of transparency and collaboration, it will remain difficult to address the issue at its core.

What challenges do you see facing newer MRX professionals as technology advances?

One challenge newer MRX professionals will face is understanding where synthetic respondents and AI can genuinely add value, and where real human input is still essential. As technology advances, it becomes easier to generate fast and scalable feedback, but that does not automatically mean it is the right fit for every research objective. I think one of the key questions will be: what can you realistically give to AI respondents, and where do you still need real people because you want authentic opinions, emotions, or behavior? Getting that balance right will become increasingly important. The risk is not the technology itself, but using it in the wrong context.