Is “Frankenstein Data” Slowing Down AI Transformation in Insurance Enterprises?

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.” 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. 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 https://www.linkedin.com/company/stibosystems/ Stibo Systems X: @StiboSystems https://x.com/StiboSystems Stibo Systems YouTube: @StiboSystemsGlobal EM360Tech YouTube: @enterprisemanagement360 EM360Tech LinkedIn: @EM360Tech https://www.linkedin.com/company/em360/?originalSubdomain=uk EM360Tech X: @EM360Tech https://x.com/EM360Tech #MasterDataManagement #DataGovernance #AIinInsurance #EnterpriseTech #BigData #DataStrategy #AIReadiness #InsuranceTechnology #cioinsights #StiboSystems #frankensteindata