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.

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?

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.

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.”

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

• 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.

• 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

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?

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.

• 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.

• 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.

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

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.

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.

• 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.

• 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.”

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?

• 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.

• 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.

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.

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.

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

• 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

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

Artificial intelligence is everywhere right now, in boardrooms, strategy meetings, and product roadmaps. Organisations are investing heavily in machine learning, automation, and generative AI, all with the same promise: unlock new revenue and work smarter.

In the latest episode of the Don’t Panic It’s Just Data podcast, EM360Tech’s Trisha Pillay explores this challenge with Chief Technology Officer Paul Brownell and Sergio Morales, Data and AI Engineering Leader from Growth Acceleration Partners. Their discussion unpacks why so many AI initiatives fail to translate into revenue and why the real starting point isn’t the model itself, but the data, governance, and engineering practices that make meaningful outcomes possible.

But here’s the uncomfortable truth and that is many AI strategies look powerful on paper, but the real financial impact is often unclear. This disconnect, called the revenue data gap, highlights an issue many organisations overlook. AI doesn’t create value on its own especially without strong data foundations, governance, and engineering discipline, even the most ambitious AI strategy will struggle to deliver measurable results.

For many organisations, the excitement surrounding AI can create a tendency to jump straight into experimentation. Teams begin exploring tools, deploying models, or building prototypes without first defining how those initiatives will produce tangible business outcomes.

According to Brownell, this is where the first major disconnect appears. Many enterprises approach AI with what he describes as a “shiny object” mentality. They recognise that AI is powerful, but they have not yet defined where the value will actually come from. As a result, organisations may launch projects that generate interesting insights or technical demonstrations but fail to translate into revenue growth or cost reduction.

Brownell emphasises the importance of establishing a data hypothesis before pursuing any AI initiative. A data hypothesis outlines the relationship between the data an organisation holds and the business value it expects to extract from it. In practical terms, it asks a simple but critical question: If we analyse this data, what decision or action will it enable, and how will that affect revenue?

Without this hypothesis, organisations often find themselves exploring large volumes of data without a clear objective. Some companies may not even know where their most valuable data resides or whether it is reliable enough to support analytical models. Data quality, therefore becomes another major component of the revenue data gap. 

While AI is often portrayed as a revolutionary technology, Morales points out that the engineering challenges behind it are not entirely new. Many of the same principles that guided earlier technology transformations such as cloud adoption or microservices architecture still apply to modern AI deployments.

In fact, Morales argues that organisations struggling with AI today are often experiencing the consequences of earlier architectural decisions. Systems built years ago were rarely designed with advanced analytics or AI in mind. As a result, critical data may be trapped inside legacy applications, scattered across departments, or stored in formats that make integration difficult. These limitations become highly visible once organisations attempt to deploy AI at scale.

Another major challenge lies in what Morales describes as the velocity mandate. Businesses increasingly expect technology teams to deliver results quickly, particularly when AI initiatives are positioned as strategic priorities. However, building the infrastructure required for reliable AI systems can take significant time and effort.

Morales explains that organisations do not necessarily need to choose between speed and stability. Instead, they can adopt a pragmatic approach that focuses on incremental progress. This strategy allows organisations to create early successes that build confidence across the business. Once stakeholders see tangible results from initial projects, it becomes easier to secure the support and investment needed for broader data transformation efforts.

One of the most practical tools discussed is the concept of data contracts. Though less flashy than AI models, they ensure data flows reliably between systems. At their core, data contracts define a dataset’s structure and expectations; schemas, formats, and validation rules. Morales describes them as a way to embed governance directly into data pipelines, automatically catching violations before they disrupt downstream processes. This prevents silent errors that can skew analytics and decisions. 

Data contracts aren’t a cure-all, though. Their effectiveness relies on clear organisational ownership and communication around each dataset. In large companies, data often comes from multiple systems managed by different teams, each with distinct priorities. Brownell explains that data contracts create a shared framework for collaboration, letting teams integrate and analyse information confidently. Implementation can be gradual: start with critical datasets for a specific use case and expand governance as needed. This iterative approach improves data reliability without requiring a full infrastructure overhaul.

While AI tools continue to evolve, the fundamentals of data management remain unchanged. Organisations must understand their data, govern it effectively, and design infrastructure that allows information to move reliably between systems. Closing the revenue data gap, therefore, requires more than deploying new AI models. It demands a strategic approach that begins with clear business objectives, continues through data engineering practices, and is reinforced by governance frameworks such as data contracts.

If you would like to learn more visit: https://www.growthaccelerationpartners.com/

00:00 Introduction to AI Ambitions and Revenue Gaps

02:31 Understanding the Revenue Data Gap

05:47 Challenges of Legacy Architecture in AI

09:11 Closing the Revenue Data Gap

12:29 The Velocity Mandate in AI Implementation

16:42 Strategic and Technical Alignment for AI

18:31 Engineering Considerations for AI Initiatives

22:03 The Role of Data Contracts in AI Success

28:55 Practical Takeaways for AI Implementation

• The revenue data gap is a common challenge that organisations face when implementing AI.
• It’s crucial to define a clear data hypothesis and ensure data quality to drive measurable business impact.
• Data contracts work only if teams know who owns datasets, how to maintain them, and how changes are communicated.
• Balancing the velocity mandate with governance is key. 
• Engaging stakeholders and mapping the value chain ensures that AI initiatives are aligned with business needs, ultimately leading to revenue growth.

Enterprise AI budgets are climbing, but the data foundations beneath them remain uneven. In this episode of Don’t Panic, It’s Just Data, Kevin Petrie, VP of Research at BARC, and Nathan Turajski, Senior Director, Product Marketing at Informatica, examine the findings of the CDO Insights 2026 report, which argues that executive confidence in AI may be outpacing organisational readiness. The study centres on what it describes as a growing “trust paradox” as Chief Data Officers are accelerating AI initiatives even as data quality, governance maturity, and AI literacy struggle to keep up. 

The report exposes a striking disconnect. Turajski points out that while around 65 per cent of data leaders believe employees trust the data powering AI, 75 per cent say upskilling in data and AI literacy is essential. In other words, confidence is high, but readiness is lagging.

This is the trust paradox where employees increasingly rely on AI outputs, while data leaders remain cautious about the quality, governance, and lineage behind those results. The risk is not scepticism but rather overconfidence. When AI-generated answers are accepted without scrutiny, flawed data can quietly scale poor decisions. For CDOs, the challenge is cultural as much as technical.

The harsh reality is that AI experimentation is no longer confined to innovation teams. It’s spreading across marketing, operations, finance, and customer experience. As a result, scaling from pilot to production requires more than a model and a use case. To make AI work at scale, organisations need a data strategy that ensures consistency across domains, clear and transparent governance, measurable business impact, and sustainable management of their data assets.

Turajski explains that organisations are increasingly investing in data management and governance, with 86 per cent expanding data initiatives and 39 per cent prioritising upskilling. Metadata integration also helps unify distributed environments, providing the context AI needs to deliver reliable, trustworthy outputs. 

 Organisations need to remember that AI systems amplify whatever they are given, so if inputs are inconsistent, incomplete, or poorly defined, outputs will reflect those weaknesses which are often at scale. Data quality challenges frequently arise from duplicated or conflicting records, inconsistent definitions across business units, poor lineage visibility, and limited ownership accountability. 

For example, a retailer might describe the same product in multiple ways across systems. Without standardisation, AI tools trained on that data produce fragmented insights, and when this occurs across thousands of products and regions, the distortions multiply. The takeaway from data leaders is clear: AI performance cannot be separated from disciplined, high-quality data management.

Both Petrie and Turajski stress that technology alone won’t close the gap. Upskilling employees in data literacy, AI fluency, and governance awareness ensures AI experimentation evolves into measurable, real-world results from improved customer experience to faster, more accurate analytics. The 2026 CDO Insights findings position data leaders at the centre of AI transformation. Their mandate extends beyond infrastructure to trust architecture. The trust paradox isn’t a reason to slow down innovation. It’s a reminder that lasting results require as much discipline as ambition. In 2026, the organisations that succeed won’t be the fastest to adopt new technologies, but those that build the most reliable data foundations to support them.

To learn more about this, visit informatica.com

• The trust paradox highlights a disconnect between employee confidence in AI and leadership's caution.
• Data leaders recognise the need for upskilling in data and AI literacy.
• Building a trusted context is essential for effective AI adoption.
• The vendor landscape for data management is complex and requires careful navigation.
• AI is being used to enhance customer experience and loyalty.
• Measurable results from AI adoption are becoming a priority for organisations.
• Data governance must keep pace with AI use to mitigate risks.
• Successful organisations are leveraging unified data management platforms to drive AI value.

00:00 Introduction to the CDO Insights Report

03:13 Understanding the Trust Paradox in AI Adoption

08:34 Building Trusted Context for AI

14:11 The Importance of Data Quality and Completeness

20:28 Navigating the Vendor Landscape for Data Management

23:09 From Experimentation to Measurable Results

27:38 Recommendations for CDOs and CISOs

What if the real advantage in AI lies not in having more data, but in having less?

In this episode of the Don’t Panic, It’s Just Data podcast, host Shubhangi Dua, Podcast Producer and B2B Tech Journalist at EM360Tech, sits down with Herb Blecher, Research Director of Data and Analytics at Enterprise Management Associates (EMA).

This conversation challenges a common belief in enterprise tech – that gathering everything ensures insight. Blecher, alluding to the modern-day AI craze, cautions the enterprise audience that just because you can access vast amounts of unstructured data doesn’t mean you should.

Unstructured data now fills the enterprise tech space — voice calls, financial documents, customer chats, images, logs, and emails. “With AI and machine learning, we’ve finally figured out how to access and organise it.”

However, Blecher offers a stark reality check. AI doesn’t just increase insight; it increases error. When machines transition from calculating numbers to interpreting tone, images, and incomplete context, the chances for mistakes rise significantly. A blurry comma in a financial document, a misread abbreviation, a misplaced decimal. In low-stakes situations, this is inconvenient. In finance or healthcare, it can be disastrous.

The danger lies not just in faulty outputs, but in confidently flawed outputs. AI doesn’t hesitate as humans do. It doesn’t say, “This seems off.” It fills in gaps, often convincingly. That confidence, Blecher argues, makes governance essential.

The real issue companies face isn’t a lack of data; it’s a lack of careful thought.

Also Read: AI is Making “As-Code” Inevitable

Governance over hype is the key takeaway from the conversation. AI generating and using data at the same time creates a new situation. In the past, including financial troubles that Blecher experienced directly, human judgment acted as the final protection. Now, companies risk losing that safeguard in their rush to automate.

Dua puts it simply – humans are leaders; AI is the helper.

The enterprises that succeed with unstructured data aren’t the fastest; they are the most thoughtful. They clearly define their questions first, build feedback loops, monitor continuously, and foster a culture of scepticism.

What are the failures? They often look like ambitious automation without safeguards—from flawed document scanning to high-profile AI rollouts like McDonald's testing automated drive-through ordering, where conversational nuance proved more challenging than anticipated.

Tone, ambiguity, and context remain distinguishing human areas.

Will AI solve data quality issues? No, it will not. However, Blecher believes that data quality problems are here to stay. “What will change is the range of questions we try to answer. As AI develops, companies won’t stop dealing with edge cases; they’ll broaden the edge.”

The future doesn’t promise easy automation. It promises increased capability, increased capacity, along with increased responsibility.

For CFOs and IT leaders investing in AI-driven data strategies, EMA’s Research Director of Data and Analytics has a final message:

1. Don’t confuse volume with value.
2. Don’t replace governance with optimism.
3. Don’t give up scepticism in a gold rush.

AI’s potential is huge. But more data doesn’t always mean better data. In a world eager to gather everything, restraint could be the most radical strategy of all.

1. More data doesn’t guarantee better insights — clarity of purpose matters more than volume.
2. AI doesn’t just scale intelligence; it scales errors if governance is weak.
3. Unstructured data is powerful, but without context and oversight, it becomes a liability.
4. Human judgment remains essential — especially in high-stakes domains like finance and healthcare.
5. The most successful organisations move deliberately, not impulsively, in the AI gold rush.

1. 00:00 Introduction to Data Quality and Its Importance
2. 02:43 The Rise of Unstructured Data
3. 05:42 Challenges in Ensuring Data Quality
4. 08:46 AI's Role in Data Quality Management
5. 11:30 Human Oversight in AI and Data Quality
6. 14:47 Opportunities in Data Quality
7. 17:32 Governance and Regulation in AI
8. 20:25 Real-World Applications and Case Studies
9. 23:27 Future of Data Quality and AI
10. 26:18 Key Takeaways for Leaders

Herb leads EMA's Data and Analytics practice. He brings more than two decades of experience building solutions across financial services, data product development, and enterprise analytics.

His perspective is shaped by leading national data initiatives for U.S. mortgage servicers and government agencies, as well as driving product innovation and strategy in fast-moving technology environments.

Herb's research spans enterprise data and analytics, including data architecture and platform modernisation, analytics and integration, governance, and AI/ML platforms.

#AI #DataAnalytics #TechPodcast #B2BTech #DataQuality #UnstructuredData #AIGoldRush #HumanInTheLoop #AICorporate #HerbBlecher #EMAPartners #CFOs #ITLeaders #DataStrategy #DontPanicItsJustData #EM360Tech #PodcastClips #DataInsights

For years, enterprises have discussed data democratisation as if it were an inevitable end goal. An assumption was made that turning on dashboards and training the business would lead to insight following naturally. But according to Barry McCardel, Co-Founder and CEO of Hex Technologies, the reality has been much more complicated.

In the recent episode of the Don’t Panic, It’s Just Data podcast, McCardel joined host Kevin Petrie, VP Research and Head of Data Management at BARC, to talk about why access alone has never been enough. He also discussed how artificial intelligence (AI) is forcing the analytics community to rethink the purpose of data.

The conversation dives into a familiar issue: how can organisations empower non-technical users without compromising data trust or overwhelming the technical teams responsible for it?

“We’ve spent a decade pretending the problem was solved by self-service,” McCardel says. “But what we actually did was move complexity around instead of removing it.”

As AI becomes part of analytics platforms, that complexity is finally being addressed. This includes long-standing beliefs about roles, ownership, and teamwork.

Tracing many of the analytics issues faced by organisations in the present day, McCardel alludes to the early self-service BI, which promised that business users could explore data on their own. This was supposed to allow analysts and engineers to focus on more important tasks. In reality, the outcome often included duplicated logic, inconsistent metrics, and a widening trust gap between teams.

“Access without context is chaos,” McCardel tells Petrie. “If everyone can answer questions, but everyone answers them differently, you haven’t democratized anything; you’ve just created noise.”

This issue has grown more urgent as organisations expand. Different roles—data engineers, analysts, data scientists, and business stakeholders—approach data with distinct goals and skills. Traditional tools forced everyone into the same interfaces, often designed for one group while ignoring the needs of the others.

Petrie notes that many companies responded by adding layers of control, but this approach had drawbacks. Stricter guidelines slowed insight generation and pushed business users back into reliance on centralised teams.

McCardel argues that the main problem isn’t a lack of governance or tools but a lack of shared understanding. “We’ve treated analytics like a handoff,” he explains. “The data team builds it, the business consumes it. That model doesn’t work when questions are fluid, and decisions are continuous.”

He believes AI is revealing the limits of that model and providing a path forward.

Also Watch: “Data Teams Suffer from Fragmentation” | Charles Schaefer @ Big Data LDN 2025

While much of the industry conversation about AI in analytics focuses on automation and natural language querying, the CEO of Hex is cautious about viewing AI as a quick fix. “If AI just gives you faster wrong answers, that’s not progress,” he points out.

Instead, he presents AI as a bridge that helps different roles collaborate in the same analytical space without flattening their expertise. In this view, AI helps translate: it turns business questions into structured analysis, brings relevant context to the surface, and makes assumptions clear instead of hidden in code.

This is where McCardel sees platforms like Hex playing an important role. Instead of separating technical and non-technical users into different tools, Hex is designed to support collaboration within a single environment. Analysts can create rigorous, transparent logic, while business users can interact with the results, ask follow-up questions, and understand how conclusions were made.

“The goal isn’t to turn everyone into a data scientist,” McCardel clarifies. “It’s to let each person contribute at their level without breaking the chain of trust.”

Trust, he stresses, is essential in modern analytics. As more insights come from AI, organisations will need clear lineage, better validation, and shared visibility into how answers are created. Black-box analytics may be quick, but they are also fragile.

“We’re moving away from the idea that insight is a product you deliver,” McMardel added. “It’s a conversation you participate in.”

As AI changes analytics workflows, the challenge for organisations won’t be just adopting the technology. It will redesign how people collaborate around data. The co-founder of Hex suggests that democratisation was never about removing experts from the process. It was about making expertise visible, accessible, and usable.

And that, finally, may be something worth not panicking about.

1. AI is reshaping the future of data analytics.
2. Data democratisation remains a significant challenge for organisations.
3. Trustworthiness in data outputs is crucial for effective decision-making.
4. Integration of different user personas is essential for collaboration.
5. Organisations can start using analytics tools without perfect data.
6. Expert users can help build trust in data analytics.
7. Natural language interfaces are key to making data accessible.
8. The role of AI in data exploration is becoming increasingly important.
9. Data quality and governance are critical for successful analytics.
10. Successful AI adoption requires a step-by-step approach.

1. 00:00 Introduction to AI and Data Analytics
2. 02:54 The Genesis of Hex Technologies
3. 06:04 Challenges in Data Democratisation
4. 09:10 AI's Role in Data Exploration
5. 12:14 Trust and Context in Data Analytics
6. 15:00 The Evolution of Analytics Tools
7. 18:10 Integrating Different User Personas
8. 21:09 The Importance of Contextual Understanding
9. 23:52 Data Preparation and Governance Challenges
10. 26:46 Incremental Adoption of AI in Organizations
11. 29:57 The Human Element in AI Adoption
12. 32:47 Conclusion and Next Steps for Leaders

#DataDemocratisation #AIinAnalytics #SelfServiceAnalytics #FutureofData #DataStrategy #BusinessIntelligence #DataGovernance #DataTrust #NaturalLanguageQuery #EnterpriseAnalytics #HexTechnologies #BarryMcCardel #DontPanicItsJustData #KevinPetrie #BARC #CIO #ITLeaders #DataTeams #DataAnalysts #DataScientists #BusinessStakeholders #DataDemocratization #AIforDataTeams #analytics_tool #datastrategy #RethinkAnalyticsStrategy #blackbox #DataFragmentation

Digital commerce teams rarely lack ideas. Most understand how AI, data, and personalisation could improve customer experiences. The problem, as explored in this episode of Don’t Panic, It’s Just Data, is turning those ideas into something that works at scale, in real time, and without slowing the business down.

Hosted by Dana Gardner, Principal Analyst at Interarbor Solutions, the discussion brings together Jürgen Obermann, Senior GTM Leader EMEA and Piotr Kobziakowski, Senior Principal Solutions Architect from Vespa.ai. Rather than focusing on hype, the conversation centres on the everyday realities of modern e-commerce systems and why progress often feels harder than it should.

AI introduces new expectations around speed, relevance, and adaptability. As a result, many digital commerce platforms are built on foundations designed for a different era. Years of development have resulted in fragmented environments, often based on microservices that once provided flexibility but now introduce complexity.

As Jürgen explains, even small changes can trigger long delivery cycles. Engineering teams may need months to safely update systems, not because the ideas are difficult, but because the infrastructure has become fragile.

Search is where most e-commerce journeys begin, yet many platforms still rely on keyword-focused approaches that struggle to interpret intent. Customers expect results that reflect who they are, what they want, and why they’re searching. Delivering meaningful personalisation requires systems that combine signals, context, and ranking logic in real time. Without that, experiences remain generic even when data is available.

The conversation then turns to architecture. Traditional search stacks, particularly Lucene-based systems, often hit performance limits when vector operations and advanced ranking are introduced. These capabilities tend to be bolted on rather than designed into the core. Piotr highlights a deeper issue, which is fragmentation. Search, ranking, recommendation, feature stores, and inference engines often live in separate systems. Each integration adds latency, duplicates data, and slows innovation.

This episode of Don’t Panic, It’s Just Data offers a calm, practical view of AI in digital commerce. Progress comes not from adding more complexity, but from simplifying how systems work together. When search, personalisation, and recommendation are designed as part of a cohesive whole, digital commerce platforms become easier to evolve and better equipped to serve both customers and the business.

For more insights into modern search architectures and AI-native commerce platforms, visit Vespa.ai.

• Many teams see the potential of AI, but face practical blockers.
• E-commerce companies struggle with operational, customer experience, and business challenges.
• AI technologies enable sophisticated personalised search experiences.
• Architectural bottlenecks often hinder e-commerce systems' performance.
• AI-native architectures can significantly improve search capabilities.
• Real-time personalisation is crucial for enhancing user experience.
• Separate systems for search and recommendations create inefficiencies.
• Phased migration is essential for transitioning from legacy systems.
• AI's impact on revenue can be profound when implemented effectively.
• Vespa is a comprehensive platform that integrates various functionalities.

00:00 Introduction to AI-Driven Search in E-Commerce

01:38 Challenges in Adopting AI for Digital Commerce

04:02 Architectural Bottlenecks in E-Commerce Systems

07:39 Designing AI-Native Search Architectures

12:00 Advancements in Personalisation for E-Commerce

16:21 Inefficiencies of Separate Search and Vector Systems

19:24 Phased Migration to AI-Native Platforms

21:51 Business Implications of AI in Search

23:57 Advice for Technical Leaders in E-Commerce

Vespa.ai is an AI search platform designed for building and operating large-scale, real-time applications. It brings together big data processing, vector search, machine-learned ranking, and real-time inference within a single system, enabling teams to deliver intelligent search, recommendation, and retrieval-augmented generation (RAG) at enterprise scale.

With native tensor support, Vespa allows complex ranking and decision logic to run directly in production, rather than being bolted on as separate services. This architecture reduces latency, simplifies system design, and makes it easier to evolve AI-driven applications as data, models, and business needs change.