Leverage Data Management for Business Value in Insurance: A Conversation with Cynozure

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Suki Duper:
Thank you, Antony, for joining us today. I’m sure we’ll have a great conversation about data management. My name is Suki Duper, and I’m the Head of International Business at Tamr. Could you start by introducing yourself and sharing your background in this space?

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Antony Marlow:
Thanks, Suki. It’s great to be here. I’m Antony Marlow, Director of Data Management Practice at Cure, a data consultancy organization. I’ve been in this field for over 20 years, starting as a business analyst, which laid the foundation for my career. My focus has always been on helping organizations build strong data capabilities to unlock value.

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Suki:
That’s excellent, and your experience is perfectly aligned with today’s topic. Data and AI are huge subjects right now, and many organizations are navigating the challenge of leveraging AI while ensuring their data quality is up to par. It’s like a chicken-and-egg problem—you need good data to get value from AI, but improving data quality is often a significant hurdle.

Let’s start with your perspective: How should organizations approach their data to extract real value?

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Antony:
Great question, Suki. When engaging with any stakeholder—whether it’s data professionals, business leaders, or industry peers—I always start by understanding their landscape, challenges, and opportunities. Interestingly, I often avoid mentioning the word “data” early in these conversations.

Why? Because data can confuse or overwhelm people. Instead, I focus on their business goals and challenges. Active listening is key here—we, as data professionals, almost need to act like therapists, helping stakeholders articulate their needs and challenges in their language.

Another critical point is understanding the broader business and industry context—how the organization operates, its value chain, customers, suppliers, and competitors. Many data teams lack this context, which can hinder their ability to drive impactful conversations and solutions.

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Suki:
I completely agree. It’s too easy to dive into data and technology without understanding how they’ll be applied or adopted. Take the insurance industry, for example—both of our organizations work closely with this sector. Historically, insurance focused on big-ticket items like homes and businesses. Today, the scope has expanded to include smaller items like phones and personal belongings.

To address these evolving demands, it’s crucial to understand the entire business landscape before implementing data-driven solutions. What’s your take on why now is such a critical moment for organizations to act?

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Antony:
The rise of AI has put data management under the spotlight. Many organizations have underinvested in efficient data management, and AI exposes the cracks. Poor data quality can lead to tools like co-pilots or chatbots producing undesirable results, damaging brands and creating risks.

It’s essential to address the foundational aspects—governance, security, and organization of data—before deploying advanced technologies. Data is the bedrock of AI success, and organizations that neglect it will face significant challenges.

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Suki:
Exactly. At Tamr, we’ve been in the AI space for over 12 years, even before it became a trend. Early on, there was fear around AI, but now there’s almost an overenthusiasm to implement it everywhere. However, as you mentioned, poorly managed data leads to AI hallucinations and inaccurate outcomes, which can harm businesses.

This highlights the importance of governance at a granular level. Leaders must understand how data impacts their value chain, not just focus on buzzwords like AI or GPT.

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Antony:
Agreed. Many organizations struggle with the chicken-and-egg dilemma—do they fix their data first or focus on high-level AI initiatives? Our approach is to advocate for starting small with achievable, end-to-end projects. This allows organizations to learn, demonstrate value, and build momentum.

In industries like insurance, data maturity is often lower compared to others. By focusing on clear business outcomes, such as improving customer service or streamlining underwriting, we help organizations achieve tangible results.

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Suki:
Absolutely. The traditional “boil the ocean” approach to data management has repeatedly failed. Starting small but valuable is the way forward. Solve one problem, get everyone aligned, and then scale gradually.

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Antony:
Exactly. Another misconception is that solutions like AI or Master Data Management (MDM) are panaceas. They’re not. Developing data capabilities is an ongoing journey, not a one-off project. Success lies in embedding data management into daily operations and aligning it with business objectives.

One analogy I love is this: Imagine someone carrying eight pints of beer at a bar, and you offer them a tray to help. Their response might be, “No, I’ve already got enough to carry.” They miss the point that the tray would make their job easier. Similarly, data management tools are there to ease the burden, not add to it.

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Suki:
That’s a fantastic analogy. As professionals, our role is to provide the tools and guidance organizations need to succeed without overwhelming them. By learning from other industries, we can help accelerate their maturity and avoid common pitfalls.

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Antony:
Absolutely. By leveraging our expertise across sectors, we can help organizations achieve results faster and with fewer missteps. For example, in insurance, improving underwriting accuracy and speed to market are key goals. With high-quality data and the right processes, organizations can achieve both efficiency and effectiveness.

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Suki:
Exactly. It’s not about striving for 100% data accuracy—except in certain regulatory cases—but rather making data fit for purpose. By focusing on the right outcomes and layering technology intelligently, organizations can unlock significant value.

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Antony:
Completely agree. The goal isn’t perfection; it’s about achieving data quality that’s “good enough” to drive business performance. Technology plays a key role in automating repetitive tasks and enhancing efficiency, but only after the foundational elements are in place.

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Suki:
Well said. Antony, it’s been a pleasure discussing these insights with you. I’m sure our listeners and viewers will walk away with plenty of food for thought. We’ll include contact information for both of us at the end so they can reach out with questions.

Thank you so much for your time today, and I look forward to continuing this conversation in the future.

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Antony:
Thank you, Suki. It’s been a real pleasure.

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