Don't Panic! It's Just Data

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.”
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
#MasterDataManagement #DataGovernance #AIinInsurance #EnterpriseTech #BigData #DataStrategy #AIReadiness #InsuranceTechnology #cioinsights #StiboSystems #frankensteindata
master data management, MDM, data governance, AI strategy, insurance, enterprise technology, big data, chief data officer, CDO, CIO, data quality, data unification, Stibo Systems, Scott Taylor, Mark Duffy, Mark Blake

Podcast: Don’t Panic! It’s Just Data
Guest: Jignesh Patel, Director of Product Strategy at Stibo Systems and Elsebeth Gundersen Jensen, Product Owner at Nets
Host: Dr Joe Perez, Data Analytics Expert and Amazon Bestselling Author
We’re living in times of an always-on digital economy where there’s no room for data errors. In the recent episode of the Don’t Panic It’s Just Data podcast, host Dr Joe Perez, Data Analytics Expert and Amazon Bestselling Author, sat down with Jignesh Patel, Director of Product Strategy at Stibo Systems and Stibo Systems’ customer, Elsebeth Gundersen Jensen, Product Owner at Nets.
Perez pointed out that even the smallest inconsistency can "ripple completely across an entire operation, instantaneously." This reality is prompting enterprise tech leaders to rethink how they manage, govern, and use data, especially with the rapid growth of AI adoption.
Overall, the guests send out a clear message – trusted, real-time data is now a crucial part of business infrastructure.
Also Watch: From Chaos to Launch: Your Product is Ready, Your Data Isn't
What is the Hidden Cost of Untrusted Data?
For large enterprises, especially those growing through mergers and acquisitions, fragmented data systems are almost unavoidable. Jensen noted that when combining multiple customer portfolios, inconsistencies often arise in even the simplest fields, like organisation numbers formatted differently in various systems.
“When you bring in different customer portfolios, you will also get this scattered data picture that you don’t want in a master data management system,” she explained.
According to Patel, the lack of trusted data impacts four key areas which includes customer experience, revenue growth, decision-making, and operational efficiency. Without a unified customer view, enterprises struggle to offer personalised experiences or spot cross-sell opportunities. Moreover, analytics based on unreliable data undermine executive confidence and increase compliance risks.
These issues are made worse by speed. Alluding to her observations, Jensen told Perez and Patel that modern customers expect contract changes or service interactions to be updated almost instantly. “They don’t want to wait a day,” she stated. “Everything should be faster, better, and accurate.”
Also Watch: Why is a Customer Data Strategy a Competitive Edge?
How are Enterprises Mastering Intelligence?
Traditionally, Master Data Management (MDM) has focused on creating the “golden record,” a single, reliable version of key business entities like customers or products. While this remains important, Patel believes this idea is changing quickly in the AI era.
“MDM is moving beyond data correctness towards what I call mastering intelligence,” he said. “AI systems rely on trusted context—understanding what entities are, how they relate, and the business rules that apply.”
This change is part of a larger transformation in enterprise architecture. Decision-making is no longer limited to human-driven dashboards; it is increasingly spreading across applications, analytics platforms, and AI agents acting in real time. In such a setup, inconsistent data does not just create errors but it can amplify it.
“AI doesn’t eliminate the need for MDM or data governance. It emphasises it,” stated Patel. For enterprises heavily investing in AI, this insight is vital. Without a strong data foundation, AI models might provide insights but not dependable results.
As enterprises move toward AI-driven and even agent-based business models, the need for trusted data will grow even more important. Patel highlights new questions from the C-suite – How will AI agents find my products? Why isn’t my business being recommended?
The answer increasingly depends on structured, high-quality data. “AI success is dependent on trustworthy data,” Director of Product Strategy at Stibo Systems says. “MDM and governance are the foundation for the next generation of intelligent business systems.”
For enterprise leaders, the key directive to note is in the race to implement AI, data trust is the competitive edge and not only the requirement.
Key Takeaways
Real-time trusted data is essential for enterprise AI success and operational resilience.
Poor data quality directly impacts customer experience, revenue growth, and compliance.
Modern Master Data Management (MDM) is evolving from “golden records” to AI-ready data intelligence.
Proactive data governance must replace reactive data cleanup to scale in real-time environments.
A unified data model is the foundation for accurate, consistent, and AI-driven business insights.

Chapters
00:00 Introduction to Data Governance and MDM
02:06 The Shift to Real-Time Data
05:27 Business Risks of Lacking Trusted Data
08:20 Growth Through Mergers and Acquisitions
15:29 The Role of MDM in AI Initiatives
20:02 Transitioning to Proactive Data Management
22:01 Advice for CIOs on Managing Product Data

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
Follow: @EM360Tech on YouTube, LinkedIn and X
#MDM #DataGovernance #EnterpriseAI #DataQuality #TrustedData #AIStrategy #RealTimeData #DigitalTransformation #StiboSystems #TechPodcast

Podcast: Don’t Panic It’s Just Data!
Guest: Adrian Estala, VP, Field Chief Data & AI Officer, Starburst
Host: Doug Laney, Research & Advisory Fellow at BARC and Author of Infonomics & Data Juice
After years of heavy investment in data lakes and warehouses, many enterprises still face a frustrating reality. Insights continue to remain slow, fragmented, and hard to trust.
In the recent episode of the Don’t Panic It’s Just Data podcast, host Doug Laney, Research & Advisory Fellow at BARC and Author of Infonomics & Data Juice, is joined by Adrian Estala, VP, Field Chief Data & AI Officer at Starburst. They sat down to discuss why more enterprises are adopting a new architectural approach, the business semantic layer, to speed up AI adoption.
What’s the Core Issue in AI Data Enterprise?
The core issue, Estala argues, is not a lack of infrastructure but an inconsistency between how data is organised and how enterprises think. “No one’s really there yet,” he says, reflecting on a decade of backend optimisation. “We don’t know what ‘perfect’ architecture means, especially in the AI age.”
The semantic layer, sometimes called a “context layer,” represents a shift from technical complexity to business usability. Typically, the system requires non-technical users to interpret schemas and pipelines; however, Starburst provides an abstraction that shows data in familiar business terms, along with metadata and governance rules.
“If you build it right,” Estala explains, “when a CFO walks in the room and sees their semantic layer, it makes sense to them.”
For an enterprise, this is more than just a usability improvement. It reduces duplication, eliminates conflicting metrics, and reduces reliance on IT teams for routine analysis. As Laney notes during the discussion, the goal is not to replace existing systems but to make them “that much more accessible” by layering business meaning on top.
Also Watch: AI Is Replacing BI — Here’s What CIOs Need to Know
Sovereignty, Governance & the European Reality
The conversation is even more acute in regions like Europe, where data sovereignty has become a major concern. Regulatory pressure has led enterprises to rethink not only where data is stored but also how it is accessed and shared.
Estala describes a federated model where data stays within national boundaries while still being usable globally. Organisations set up local clusters in countries like Switzerland or the United Kingdom, build data products locally, and apply strict rules for what can be shared centrally.
“I can decide which data products are approved to be shared,” he says, alluding to compliance mechanisms that ensure sensitive information cannot be traced back to individuals.
This creates a system that satisfies both regulators and business leaders. Executives no longer need to worry about jurisdictional complexities; they work with a unified view of data that has already been filtered, governed, and approved. “For them, it just feels like it’s already been brought together,” Estala adds.
As AI agents and copilots continue to gain popularity, the discussion also spotlights limitations. One such limitation is trust. Without confidence in the underlying data, even the most advanced AI tools struggle to provide meaningful value.
“If they don’t trust the answers, it’s just a cool toy,” Estala says, describing a common pattern where initial excitement fades once users doubt the reliability of outputs.
The semantic layer also tackles this discrepancy by embedding governance, lineage, and business rules directly into data products. Starburst helps enterprises clearly define which data is exposed to AI systems and under what conditions, making it easier to explain and justify decisions.
Currently, Estala observes, AI mainly speeds up existing workflows instead of transforming them. Executives are asking the same questions they always have, but getting answers faster and from broader datasets. The real change, he suggests, will come when trust allows leaders to ask entirely new questions and rethink decision-making.
How to Drive Business Value in 90 Days?
For CIOs and CDOs eager to move past experimentation, the Chief Data and AI officer outlines a focused, business-led approach. Rather than launching large-scale transformations, he suggests starting with a single domain and building momentum from there.
The first phase focuses on collaboration, bringing business stakeholders into the design of the semantic layer and defining the data products that are most important. “We design it with the business team in the room,” he explains, stressing ownership from the start.
The next stage shifts to enablement, as teams begin to use and expand these data products themselves. This is where self-service takes root, reducing dependence on IT and promoting more exploratory use of data.
By the final phase, enterprises are ready to introduce AI agents on top of a trusted foundation. At that stage, technology becomes almost secondary. “Once you get to a semantic layer that you trust, adding an agent is easy,” Estala says.
As enterprises continue to adopt AI at larger scales, their competitive edge will come from algorithms and from how effectively they organise, govern, and contextualise their data. In this sense, the semantic layer is quickly becoming the backbone of modern, AI-driven decision-making.
Key Takeaways
Semantic layers make governed data accessible for enterprise AI.
Data sovereignty drives federated, compliant data architectures.
Trusted AI needs governed, metadata-rich data products.
Semantic layers deliver business value within 90 days.
Virtual layers reduce duplication and speed up analytics.

Chapters
00:00 The Shift to Business Semantic Layers
08:02 Data Sovereignty and Governance in Modern Strategies
13:08 Foundational Capabilities for AI Systems
18:11 AI Agents and Decision Making
23:04 Practical Steps for Implementing Semantic Layers

To learn more about how data products and AI agents are changing enterprise analytics, follow:
Starburst LinkedIn: @Starburst
Starburst X: @starburstdata
Starburst YouTube: @StarburstData
EM360Tech YouTube: @enterprisemanagement360
EM360Tech LinkedIn: @EM360Tech
EM360Tech X: @EM360Tech
Follow: @EM360Tech on YouTube, LinkedIn and X
Stay connected for more expert insights, podcast episodes, and enterprise data strategy discussions.
#SemanticLayer, #DataGovernance, #EnterpriseAI, #DataStrategy, #DataArchitecture, #AIatScale, #Compliance, #DataSovereignty, #ContextLayer, #AIagents, #DataProducts, #SelfServiceAnalytics, #CIO, #CDO, #Starburst, #AdrianEstala, #DougLaney, #DontPanicItsJustData, #EM360Tech, #TechPodcast

Podcast: Don’t Panic! It’s Just Data
Guest: Adrian Estala, VP, Field Chief Data & AI Officer, Starburst
Host: Shubhangi Dua, Podcast Producer, Host and B2B Tech Journalist, EM360Tech
"AI is replacing BI,” stated Adrian Estala, VP and Field Chief Data & AI Officer at Starburst.
When Shubhangi Dua, host of Don’t Panic, It’s Just Data, put the statement back to Estala, the tension was intentional. In enterprise tech, few systems are as ingrained as business intelligence (BI) dashboards. For two decades, they have been the common language of decision-making – static reports, polished charts, and visuals that meet compliance standards.
However, Estala insists that the change isn't about removing dashboards. It's about staying relevant. “BI isn’t going away,” he explains. “It’s evolving.”
How AI is replacing BI?
A transformation to AI begins with something deceptively simple – a business semantic layer. Instead of forcing executives to understand data through IT-designed schemas, enterprises are creating context-rich data products using business language. A CFO sees finance terms, not table joins. A loans team sees portfolios, not pipelines.
Once this foundation is established, teams can plug the same governed, reusable data product into their business intelligene (BI) tools. This leads to improved performance and consistency rises too.
However, the growth doesn’t stop here; businesses typically ask for more. When a conversational agent is added next to a legacy dashboard, using the same trusted data product, the behaviour changes quickly. Leaders start asking questions in natural language, exploring trends they have never charted before. They make forecasts in seconds and adjust their thinking while on the go.
What was once a static reporting experience transforms into an interactive analytical dialogue. In one major bank, Estala recalls, a CEO challenged himself to avoid opening a dashboard for two weeks. He didn’t need to; the agent managed everything for him.
Also Watch: Are You Scaling Intelligence — or Just Scaling Errors?
Takeaways
AI is replacing BI, but it's more about evolution than replacement.
Organisations are moving towards data products for better analytics.
Engaging business teams early is crucial for successful AI implementation.
Conversational agents are transforming how teams interact with data.
Data quality and governance are essential in the transition to AI.
Business semantic layers help bridge the gap between IT and business needs.
Organisations can achieve significant impact with AI in a short time.
Don't wait for perfect architecture; start with a Pathfinder approach.
Business teams can drive innovation when they understand their data.
The future of data engagement lies in combining AI with traditional BI tools.

Chapters
00:00 The Evolution of BI to AI
03:11 Understanding AI's Role in Business Intelligence
14:44 Navigating the Transition to AI
20:03 Ensuring Data Quality and Governance
24:44 The Future of Data Engagement

To learn more about how data products and AI agents are changing enterprise analytics, follow:
Starburst LinkedIn: @Starburst
Starburst X: @starburstdata
Starburst YouTube: @StarburstData
EM360Tech YouTube: @enterprisemanagement360
EM360Tech LinkedIn: @EM360Tech
EM360Tech X: @EM360Tech
Follow: @EM360Tech on YouTube, LinkedIn and X

Stay connected for more expert insights, podcast episodes, and enterprise data strategy discussions.
#AI #BI #AIvsBI #AIAgents #BusinessIntelligence #DataProducts #EnterpriseAnalytics #DataStrategy #Starburst #DontPanicItsJustData #AdrianEstala #ShubhangiDua #SemanticLayer #CIO #CDO #TechPodcast #DataGovernance #Dashboards

We’re living in an age where new technology promises to improve everything with faster decisions, smarter workflows, and better outcomes. But behind that promise lies a quieter reality, and that is many organisations have that ambition, but readiness often lags behind. In this episode of Don’t Panic! It’s Just Data, host Christina Stathopoulos, Founder of Dare to Data, speaks with Pascal Bensoussan, Chief Product Officer at Ivalua.
In this episode, they look at the growing excitement around AI and the reality many organisations face when trying to use it. While ambition is high, readiness often falls short. Focusing on procurement, the conversation explores why many AI initiatives struggle to move beyond early stages and what’s needed to turn that ambition into real, measurable value.
Data: The Backbone of AI
Successful AI depends on high-quality, unified data. Fragmented sources, unclean data, and siloed systems make it difficult to build reliable AI applications. As Bensoussan explains: “Fix your data foundation. Without that, you can’t get started with AI. Don’t jump into an AI frenzy hoping it will sort itself out. First, you need a unified transactional and master data model that captures relationships, ensures semantic coherence, and creates a system of truth you can trust.”
A unified data model enables AI to work effectively, increasing both its success rate and depth. Organisations should start with use cases that provide tangible value rather than trying to do everything at once. Governance frameworks, monitoring, and maintenance are critical to ensure reliability, security, and meaningful outcomes.
Employee trust is another key factor. Users need confidence in AI outputs, and organisations must address scepticism about how AI might impact roles. Building that trust often requires broader cultural change, which can be one of the hardest barriers. Many teams are used to traditional methods and resist adopting new technologies. By combining solid data foundations with practical, focused use cases and a clear strategy, companies can guide teams through this change, ensuring AI initiatives don’t stall and deliver measurable results.
Understanding AI Ambition vs. AI Readiness
Ambition and readiness are not the same. AI ambition refers to the enthusiasm organisations have for integrating AI into operations, driven by the promise of efficiency and insight. AI readiness, on the other hand, measures whether an organisation can actually deploy AI effectively at scale.
According to MIT research, 95 per cent of enterprise AI projects fail to move from proof of concept to production. Bensoussan calls this the “GenAI divide”: “The ambition is there because the promise is incredible, but the readiness is often missing because often the foundation is cracked.”
Without a clear strategy or roadmap, even organisations with abundant resources can struggle to implement AI successfully. Starting with targeted, achievable use cases helps teams gain confidence, build trust, and generate measurable results before scaling more widely.
AI in Procurement
Procurement provides a unique lens for understanding AI adoption. Positioned at the intersection of data, compliance, risk, and finance, it offers significant opportunities but also considerable complexity. One major challenge is that unstructured data like contracts, risk assessments, and supplier communications must be integrated with transactional records, a process that is often time-consuming and difficult. Fragmented systems only add to the challenge, limiting AI’s ability to deliver meaningful, actionable insights.
Bensoussan emphasises that seeing the entire process from supplier discovery to payment is essential. A comprehensive view ensures that AI-driven insights are reliable, actionable, and fully traceable, allowing organisations to understand why specific decisions are made and to make more strategic choices.
AI in procurement is not about replacing humans; it is about augmenting them. By automating mundane tasks like data retrieval and report generation, professionals can focus on higher-value work, strategic thinking, and deeper evaluation. AI also enables richer insights, helping teams develop more effective strategies and make informed decisions. By addressing data challenges, building trust, and starting with targeted use cases, organisations can turn AI ambition into measurable value. With the right preparation and focus, AI can strengthen procurement operations, enhance decision-making, and unlock new levels of efficiency.
For more information, visit www.ivalua.com
Takeaways
AI ambition vs. readiness in organisations
Barriers to AI adoption: culture, strategy, data, trust, governance
Importance of unified data models for AI effectiveness
Practical AI applications in procurement: sourcing, contracts, invoicing
Human-AI collaboration and the future of work in procurement

Chapters
00:00 AI Ambition vs. Readiness
05:02 The Procurement Landscape and AI Adoption
09:10 Data Foundations for AI Success
13:03 Unified Data Models in Procurement
16:43 The Human Element in AI Integration
25:57 Real-World Applications of AI Agents
32:22 Key Takeaways for Leaders in AI Adoption