Podcast Series: Don’t Panic It’s Just Data

Guest: Mark Duffy, Senior Director, Artificial Intelligence & Analytics at Cognizant and Mark Blake, FSI Industry Practice Lead, Stibo Systems

Host: Scott Taylor, The Data Whisperer and Principal Consultant, MetaMeta Consulting

Artificial intelligence (AI) is prevalent in the insurance industry now, but many firms are not seeing the results they expected. The issue isn’t with the AI models; it’s pertinent to the data.

In the recent episode of the Don’t Panic It’s Just Data podcast, host Scott Taylor, The Data Whisperer and Principal Consultant at MetaMeta Consulting, is joined by Mark Duffy, Senior Director, Artificial Intelligence & Analytics at Cognizant and Mark Blake, FSI Industry Practice Lead at Stibo Systems. 

The data industry experts address a key misunderstanding about enterprise AI – that companies can innovate their way out of poor data quality. “Some people think AI is a quick fix for data governance,” said host Scott Taylor. “If I need better data, I just use AI.” Experts warn that this belief is what’s holding insurers back. 

How Frankenstein Data is Impacting AI?

Despite significant investments in AI, cloud, and analytics, many insurers remain stuck in pilot mode. According to Mark Blake of Stibo Systems, the problem is the infrastructure. “AI itself isn’t the challenge,” he said. “It’s the ability to scale it, and that comes back to fixing the data.”

In reality, most insurance enterprises face fragmented, siloed data across systems. Customer, policy, claims, and product data often don’t align. This results in what Taylor calls “Frankenstein data,” where inconsistent records lead to unreliable outputs.

For AI to function effectively at scale, insurers need trusted, governed, and unified data. That’s where data governance and master data management (MDM) come in.

“For us to truly gain benefits from AI, the end user really has to trust the data,” stated Mark Duffy of Cognizant. “That trust comes from having the right data foundation in place.”

Also Watch: Can Your MDM Strategy Survive the Shift to Real-Time AI Decision-Making?

How Master Data Management (MDM) Unlocks Scalable AI?

One of the key drivers of AI success in insurance is multi-domain master data management, a system that connects core business data across the enterprise. “You always have to have a starting point,” Blake explained. “Then you expand horizontally across the enterprise.”

The “horizontal data layer” enables insurers to unify key entities like customers, products, and partners—often referred to as the “nouns of the business.” When these are standardised, AI models can work consistently and accurately.

The business impact is substantial, including more accurate underwriting decisions, reduced claims leakage, improved customer experience and retention and better cross-sell and upsell opportunities. 

Duffy shared a real-world example in which enhancing data management directly sped up AI adoption. “It gave them trust in the data,” he said. “They could run models faster and gain more value because they weren’t constantly fixing issues.”

Instead of spending 80 per cent of their time cleaning data, teams could finally focus on using it.

Why AI Is Coercing a Data Strategy Reset

For years, data governance struggled to gain executives' support, but now AI has shifted that.“There’s been a refocus,” Blake said. “They’re looking at data in a way they maybe haven’t done historically.”

Today, AI is a priority for boards, driving alignment among CIOs, CDOs, and IT enterprise leaders. “Every C-suite executive wants to do more AI,” Duffy said. “But they’ve realised they can’t do that without the data foundation.”

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Still, some enterprises believe AI can fix poor data quality. Experts warn that this is a mistake. “You can use AI to support data quality,” Duffy said. “But you’re not going to use AI to build an MDM solution.”

What’s the Solution to Frankenstein Data

As insurers develop their AI strategies for the next 12 to 24 months, one key ideology was spotlighted – success depends less on speed and more on structure. “Go back to the root cause,” Blake said to Taylor. “Fix that, and then you can move forward with confidence.”

In other words, AI highlights the need for strong data foundations; it doesn’t eradicate them. For insurers serious about AI transformation, that’s no longer optional—it’s where they must begin.

Also Watch: From Chaos to Launch: Your Product is Ready, Your Data Isn't

Key Takeaways

  • AI in insurance fails without strong data governance and quality foundations.
  • Master Data Management (MDM) is critical for scaling AI across insurance enterprises.
  • Fragmented “siloed data” is the biggest barrier to AI adoption in insurance.
  • Trusted, unified customer and policy data improves AI accuracy and business outcomes.
  • AI cannot fix bad data—insurers must modernise data management first.

Chapters

  • 00:00 Introduction to AI Readiness in Insurance
  • 03:08 The Importance of Data Foundations
  • 06:02 Challenges of Fragmented Data
  • 09:06 Modernising Data Foundations for AI
  • 11:56 Real-World Use Cases in Insurance
  • 15:03 The Role of Master Data Management
  • 17:56 Aligning Business and Data Strategies
  • 21:06 Final Thoughts on AI and Data Governance

For more information, please visit em360tech.com and stibosystems.com.

To learn more about AI in the MDM space and how they’re progressing enterprise analytics intelligently, follow:

Stibo Systems LinkedIn: @StiboSystems

Stibo Systems X: @StiboSystems

Stibo Systems YouTube: @StiboSystemsGlobal

EM360Tech YouTube: @enterprisemanagement360

EM360Tech LinkedIn: @EM360Tech

EM360Tech X: @EM360Tech