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AI investment is growing fast, but proving its value remains one of the biggest challenges facing data leaders today. Dashboards are built, models are deployed, and yet when the budget question arrives, most teams still can't clearly demonstrate return on investment.
Speaking on Don't Panic, It's Just Data with host Christina Stathopoulos, Nadiem von Heydebrand, CEO and co-founder of Mindfuel, identified where most organisations go wrong: the interface between data teams and the business. According to von Heydebrand, the reason is straightforward: no use case, no value.
"We get a demand, we believe we've understood it, and we start executing immediately," he explained. Months pass, and nobody can answer why the project exists or what problem it was supposed to solve in the first place. The fix isn't more technology. It's better use case management.
The 3 Pillars of Effective AI Use Case ManagementOne of von Heydebrand’s core principles is straightforward: before you build anything, you need to really understand the business challenge you're trying to solve. "You have to fall in love with the problem, not with the solution," he said. This matters more than ever in the era of generative AI. With token costs attached to every AI interaction, building the wrong solution isn't just a wasted effort; it's an ongoing financial drain. Use case management has moved from being a nice-to-have to an operational necessity. Good use case management, according to Nadiem, rests on three pillars:
Demand exploration: Don't assume you understand the problem. Engage stakeholders, ask deeper questions, and uncover the real business challenge before a single line of code is written.Value management: Every use case needs a value hypothesis. What outcome is expected if this problem is solved? As Nadiem puts it: "The solution itself has a value of zero. Value lives in the problem space."Value tracking: Once live, track performance against the original hypothesis. Define a realistic ROI timeframe and review it consistently.
Adoption Metrics Are Not Proof of ValueOne of the most common mistakes? Measuring AI success through usage and adoption data alone. "I have enough examples where usage is high, and value is zero or even negative," von Heydebrand warned.
Clicks and logins are a proxy. Business outcomes are the goal. If there's no correlation between the two, the metric is misleading.
Output vs. Outcome: The Shift That MattersThe most important distinction in the conversation was the difference between output and outcome. Data teams have historically been measured on output like model accuracy, number of dashboards, and features delivered. But output without impact is just activity. Outcome means the value created for the recipient of your work. Organisations that make this mindset shift from measuring what they produce to measuring what they change are the ones that change their data functions from cost centres into genuine value generators.
For leaders under pressure to prove ROI from AI initiatives, Mindfuel’s CEO advises a pragmatic approach: start now, start small, and be honest. As Stathopoulos summarised: "It all comes back to being intentional about what you build and why." For more information, visit mindfuel.ai, the platform built to help data and AI teams demonstrate, manage, and maximise business value.
Connect with the guest:
Nadiem von Heydebrand: LinkedIn | Mindfuel
TakeawaysThe importance of structured use case managementLinking AI initiatives to business valueThe impact layer and value tracking in AI projects
Chapters00:00 – Introduction to Data and AI Impact Management
03:16 – The Challenge of Connecting AI to Business Outcomes
11:38 – Understanding Use Case Management
17:40 – The Missing Value Layer in Data and AI Initiatives
22:23 – Evolving Mindsets in Data and AI
27:36 – Advice for Leaders on Proving AI ROI
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Podcast: Don’t Panic! It’s Just Data
Guest: Michael Marolda, Senior Product Marketing Manager for Agentic RAG at Progress Software
Host: Shubhangi Dua, Podcast Producer and B2B Tech Journalist at EM360Tech
Generative AI has been brewing in the enterprise tech industry for at least three years now. AI pilots are launching every other day, internal copilots are deployed across enterprise divisions, and now teams themselves are experimenting with large language models (LLMs) to automate business workflows. Such additions have sped up research and notably improved productivity.
While the excitement is valid, the truth beneath is often disregarded. Many enterprise AI systems produce answers that sound convincing, even when they are completely wrong.
In the recent episode of the Don’t Panic! It’s Just Data podcast, Michael Marolda, Senior Product Marketing Manager for Agentic RAG at Progress Software, sat down with host Shubhangi Dua, Podcast Producer and B2B Tech Journalist at EM360Tech.
Marolda argued that the problem is not necessarily with the AI models themselves. The real issue is with the enterprise data foundations supporting them.
“Your AI is only as good as the knowledge it has access to,” Marolda explained during the conversation. The question is what the gap is alluded to in the AI enterprise tech space.
Also Read: Build vs. Buy: The Reality of Production-Grade RAG
What’s the Hidden Risk Costing Enterprises?According to Marolda, around 80 per cent of enterprise data remains unstructured. This includes PDFs, contracts, emails, audio files, presentations, scanned documents, videos, and handwritten notes. This is the kind of information that traditional AI systems struggle to process reliably.
While enterprises are heavily investing in AI infrastructure and model testing, many still do not have systems capable of organising, retrieving, and validating this scattered knowledge. The outcome often turns into a situation where AI tools begin to generate responses without the necessary business context, despite excellent prompt engineering.
“We’ve seen enterprises rush into AI implementations,” Marolda said. “But many pilots fail to scale because the information isn’t grounded in real business data.” It ultimately poses major operational risks for companies, especially in highly regulated industries.
During the podcast, Marolda mentioned a high-profile case involving an airline chatbot that provided customers with incorrect policy information, leading to legal consequences for the company. The issue was not due to malicious intent or a technical failure at the model level — it was due to unreliable data grounding.
For enterprises using AI in customer service, HR, legal operations, finance, or internal knowledge systems, such errors are not rare. In fact, they’ve become a governance issue.
Is Modern RAG the Solution?Enterprises tend to rely on data lakes as centralised storage for vast amounts of information. However, Marolda makes a point about how storage is no longer enough in the age of AI. “A data lake is just cheap storage,” he explained. “A knowledge layer is what actually activates that information for AI.”
This difference is increasingly important as enterprises move from testing to operational AI deployment. Traditional storage systems can hold documents, but they cannot interpret relationships between data points, retrieve context semantically, or validate AI-generated outputs against source material.
An enterprise knowledge layer, on the other hand, is designed to fill that gap. Marolda tells Dua that modern retrieval-augmented generation (RAG) systems can process unstructured data, apply optical character recognition (OCR), convert speech to text from video and audio, and build semantic connections across enterprise content.
This enables AI systems to retrieve not just documents, but highly specific pieces of contextual information, including paragraph-level citations and timestamped video references.
For enterprise leaders, the implications are significant. Rather than viewing AI as a separate assistant, enterprises are increasingly seeing AI as a retrieval and reasoning layer built on top of their knowledge ecosystems.
How Should Enterprises Prioritise Efficiency Over Hype?The economics of AI was a critical discussion Marolda had with Dua. He noted that while many AI providers continue to push for higher token consumption and larger workloads, enterprises such as Progress Software are now beginning to value efficiency instead.
Unlike NVIDIA’s enterprise philosophy, as proposed by its CEO Jensen Huang, is a new compensation model where engineers receive annual AI token budgets worth half their base salary on top of regular pay. During a live interview on the All-In Podcast, recorded in San Jose, California, in March 2026 at Nvidia's GPU Technology Conference (GTC), Huang stated:
"If a $500,000 Engineer Did Not Consume At Least $250,000 Worth of Tokens, I'm Going To Be Deeply Alarmed."
“We’re actually trying to reduce token consumption,” he explained. Such an approach contrasts with broader industry trends focused on maximising AI use at scale. As enterprise AI budgets become more established, CIOs and CFOs are scrutinising infrastructure costs, energy consumption, and long-term operational sustainability.
It’s particularly relevant as enterprises pit multiple LLMs against each other for quality, relevance, and cost efficiency. According to Progress’s Sr. Product Marketing Manager, the next phase of enterprise AI adoption won’t be driven by model capability alone. It will be guided by practical governance, meaning identifying which systems produce the best results at reasonable costs.
Overall, successful AI adoption is not just about selecting the right model but, in fact, pivoting towards building the right knowledge architecture.
For instance, enterprises continue to invest in generative AI; the enterprises that thrive may be the ones that can effectively structure, govern, retrieve, and validate their institutional knowledge.
Key TakeawaysEnterprise AI hallucinations increase without grounded enterprise data.Agentic RAG helps enterprises reduce AI hallucinations and improve accuracy.Unstructured data is the biggest challenge in enterprise AI adoption.Enterprise knowledge layers improve AI governance and traceability.AI token reduction lowers enterprise AI infrastructure costs.RAG architecture helps enterprises scale trustworthy AI systems.
Chapters00:00 Introduction to Enterprise AI and Knowledge Layer02:13 Challenges with Unstructured Data in AI08:11 The Importance of a Knowledge Layer12:04 Trust and Governance in AI Solutions16:48 Progress's Unique Approach to AI Solutions19:15 Agentic RAG: A New Paradigm in AI Retrieval24:52 Real-World Applications of Agentic RAG26:39 Maintaining Quality and Performance in AI Systems28:01 Key Takeaways for IT Decision MakersFor more enterprise AI, Agentic RAG, data governance, and enterprise knowledge layer insights, follow Progress Software across its official channels:
Website: Progress SoftwareYouTube: @ProgressSWLinkedIn: Progress SoftwareX: @ProgressSWFor more
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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 ResetFor 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 DataAs 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 TakeawaysAI 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.
Chapters00:00 Introduction to AI Readiness in Insurance03:08 The Importance of Data Foundations06:02 Challenges of Fragmented Data09:06 Modernising Data Foundations for AI11:56 Real-World Use Cases in Insurance15:03 The Role of Master Data Management17:56 Aligning Business and Data Strategies21:06 Final Thoughts on AI and Data GovernanceFor 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
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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 TakeawaysReal-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.
Chapters00:00 Introduction to Data Governance and MDM02:06 The Shift to Real-Time Data05:27 Business Risks of Lacking Trusted Data08:20 Growth Through Mergers and Acquisitions15:29 The Role of MDM in AI Initiatives20:02 Transitioning to Proactive Data Management22:01 Advice for CIOs on Managing Product DataFor 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
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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 RealityThe 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 TakeawaysSemantic 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.
Chapters00:00 The Shift to Business Semantic Layers08:02 Data Sovereignty and Governance in Modern Strategies13:08 Foundational Capabilities for AI Systems18:11 AI Agents and Decision Making23:04 Practical Steps for Implementing Semantic LayersTo 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
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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?
TakeawaysAI 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.
Chapters00:00 The Evolution of BI to AI03:11 Understanding AI's Role in Business Intelligence14:44 Navigating the Transition to AI20:03 Ensuring Data Quality and Governance24:44 The Future of Data EngagementTo learn more about how data products and AI agents are changing enterprise analytics, follow:
Starburst LinkedIn: @StarburstStarburst X: @starburstdataStarburst YouTube: @StarburstDataEM360Tech YouTube: @enterprisemanagement360EM360Tech LinkedIn: @EM360TechEM360Tech X: @EM360TechFollow: @EM360Tech on YouTube, LinkedIn and XStay 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
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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 AISuccessful 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 ReadinessAmbition 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 ProcurementProcurement 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
TakeawaysAI ambition vs. readiness in organisationsBarriers to AI adoption: culture, strategy, data, trust, governanceImportance of unified data models for AI effectivenessPractical AI applications in procurement: sourcing, contracts, invoicingHuman-AI collaboration and the future of work in procurement
Chapters00:00 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
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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.
The Revenue Data Gap in Enterprise AIFor 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.
Engineering the Foundations for AI That Delivers Business ImpactWhile 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.
Why Data Contracts and Governance Are Critical to AI SuccessOne 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.
What’s next for AI?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/
Chapters00:00 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
TakeawaysThe 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.
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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 Trust ParadoxThe 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.
AI Adoption Soars While Data Readiness LagsThe 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.
Data Quality and GovernanceTurajski 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.
Upskilling and Scaling AI AdoptionBoth 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
TakeawaysThe 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.
Chapters00:00 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
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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.
What is the AI Gold Rush & Why It’s Risky?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
Why Human-in-the-Loop is Imperative?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.
What Happens Five Years From Now?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:
Don’t confuse volume with value.Don’t replace governance with optimism.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.
Key Takeaways More data doesn’t guarantee better insights — clarity of purpose matters more than volume.AI doesn’t just scale intelligence; it scales errors if governance is weak.Unstructured data is powerful, but without context and oversight, it becomes a liability.Human judgment remains essential — especially in high-stakes domains like finance and healthcare.The most successful organisations move deliberately, not impulsively, in the AI gold rush.
Chapters00:00 Introduction to Data Quality and Its Importance02:43 The Rise of Unstructured Data05:42 Challenges in Ensuring Data Quality08:46 AI's Role in Data Quality Management11:30 Human Oversight in AI and Data Quality14:47 Opportunities in Data Quality17:32 Governance and Regulation in AI20:25 Real-World Applications and Case Studies23:27 Future of Data Quality and AI26:18 Key Takeaways for Leaders
About Herb BlecherHerb 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
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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.
Addressing the Myth of Data DemocratisationTracing 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
AI is the Bridge, Not the ShortcutWhile 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.
TakeawaysAI is reshaping the future of data analytics.Data democratisation remains a significant challenge for organisations.Trustworthiness in data outputs is crucial for effective decision-making.Integration of different user personas is essential for collaboration.Organisations can start using analytics tools without perfect data.Expert users can help build trust in data analytics.Natural language interfaces are key to making data accessible.The role of AI in data exploration is becoming increasingly important.Data quality and governance are critical for successful analytics.Successful AI adoption requires a step-by-step approach.
Chapters00:00 Introduction to AI and Data Analytics02:54 The Genesis of Hex Technologies06:04 Challenges in Data Democratisation09:10 AI's Role in Data Exploration12:14 Trust and Context in Data Analytics15:00 The Evolution of Analytics Tools18:10 Integrating Different User Personas21:09 The Importance of Contextual Understanding23:52 Data Preparation and Governance Challenges26:46 Incremental Adoption of AI in Organizations29:57 The Human Element in AI Adoption32:47 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
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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.
When AI Meets Legacy Digital CommerceAI 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 and Personalisation Are Still DisconnectedSearch 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.
Architecture Becomes the BottleneckThe 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.
A More Grounded Path ForwardThis 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.
TakeawaysMany 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.
Chapters00:00 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
About Vespa.aiVespa.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.
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In the recent episode of the Don’t Panic It’s Just Data podcast, Shubhangi Dua, Podcast Producer and B2B Tech journalist at EM360Tech, reports on the podcast shot live in London. Guest speakers, Pavel Dolezal, the CEO at Keboola, sit down with Vineta Bajaj, Group CFO, Holland & Barrett.
They get specific about how modern finance leaders move faster: start with one governed source of truth, then layer automation, and only then AI. They explore how the CFO role is evolving. From reporting numbers to also owning the non-financial “whys” behind them.
In the age of the AI boom, that shift turns every CFO into a product owner of data. But as Pavel Doležal puts it, without a clean, connected foundation, AI is just noise.
According to Vineta Bajaj, Group CFO of Holland & Barrett, the role of the CFO has fundamentally changed. Today’s CFO must act as a product owner for data, not just owning the numbers but also determining how data is defined, structured, and used throughout the business.
Finance and Data: A Complete ProductDrawing on her experience with Ocado Group, Rohlik Group (one of the fastest online grocery businesses in the world), and now Holland & Barrett, Bajaj points out that financial problems remain persistent across organisations.
Issues such as slow month-end closes, duplicated processes, delayed reporting, and limited decision-making speed are still common. These challenges are even greater in complex businesses that operate across multiple entities and countries. Differing charts of accounts, outsourced finance teams, and fragmented systems create added friction.
Bajaj stresses the answer isn’t "add another tool". CFOs should treat finance and data as a complete product, one that serves the business as its customer. This requires understanding finance processes, clearly defining financial and non-financial data, and prioritising what has the greatest impact on the business.
The Holland & Barrett CFO further emphasises that CFOs cannot pass this responsibility off to IT or BI teams. When data ownership is outside finance, it becomes someone else’s problem. However, when finance takes ownership of master data and its definitions while working closely with commercial and operational teams, it creates a single source of truth that the entire organisation can trust.
Also Watch: The Real Future of Data Isn’t AI — It’s Contextual Automation
How to Build the Foundation for Real-Time Financial Intelligence & AIAnalytics, automation, and AI only work if the foundations are solid. Before adding AI assistants or real-time dashboards, CFOs must ensure that finance processes are clean, standardised, and automated. Poorly coded purchase orders, late journal entries, and inconsistent definitions can undermine even the most advanced technology.
At Holland & Barrett, this perspective led Bajaj to create a dedicated data function within finance. It ensures accountability for master data, definitions, and governance. The aim is not just to speed up reporting, but to gain deeper insights by linking financial outcomes with non-financial factors such as foot traffic, pricing, customer behaviour, and external influences like weather.
This integrated viewpoint allows finance teams to go beyond explaining variances and focus on the key business question: why performance changed and what happens next. It also opens up self-service analytics, reducing reliance on central BI teams and enabling decision-makers to act in real time.
Bajaj views AI as a powerful tool but not a shortcut. It prompts organisations to quickly address long-standing data and process issues. When data is well-defined and trusted, AI can facilitate scenario modelling, forecasting, and faster decision-making. Without proper discipline, AI merely adds to the confusion.
Ultimately, the future CFO must take an active role. They should engage with data, map out processes, ask difficult questions, and create a clear plan. Those who do will move faster than traditional finance models allow and help their organisations thrive in an AI-driven future.
Key TakeawaysThe CFO’s role is evolving from reporter to product owner of data.Slow month-end and fragmented processes block fast decision-making.Finance must own data definitions to create a single source of truth.Financial and non-financial data must be connected to explain the “why.”AI only delivers value when financial data and processes are already clean.
Chapters00:00 Introduction: The Modern CFO and Data01:05 What is the New Role of the CFO?03:34 The 3 Biggest Problems in Finance (Month-End, Reporting, Decisions)06:21 Why Every CFO is a Product Owner of Data09:37 Data Ownership: Should Master Data Sit in Finance or IT?13:30 The 3 Steps to Unleash Data Power (Process, Standardisation, Data Lake)17:08 How AI is Forcing Speed and Change in Finance20:25 The Future: Keboola's AI Assistant Roadmap21:36 Wrap-up and Final Thoughts
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We live in a world where technology moves faster than most organisations can keep up. Every boardroom conversation, every team meeting, even casual watercooler chats now include discussions about AI. But here’s the truth: AI isn’t magic. Its promise is only as strong as the data that powers it. Without trust in your data, AI projects will be built on shaky ground.
In this episode of Don’t Panic, It’s Just Data podcast, Amy Horowitz, Group Vice President of Solution Specialist Sales and Business Development at Informatica, joins moderator Kevin Petrie, VP of Research at BARC, to tackle one of the most pressing topics in enterprise technology today: the role of trusted data in driving responsible AI. Their discussion goes beyond buzzwords to focus on actionable insights for organisations aiming to scale AI with confidence.
Why Responsible AI Begins with DataAmy opens the conversation with a simple but powerful observation: “No longer is it okay to just have okay data.” This sets the stage for understanding that AI’s potential is only as strong as the data that feeds it. Responsible AI isn’t just about implementing the latest algorithms; it’s about embedding ethical and governance principles into every stage of AI development, starting with data quality.
Kevin and Amy emphasise that organisations must look at data not as a byproduct, but as a foundational asset. Without reliable, well-governed data, even the most advanced AI initiatives risk delivering inaccurate, biased, or ineffective outcomes.
Defining Responsible AI and Data GovernanceResponsible AI is more than compliance or policy checkboxes. As Amy explains, it is a framework of principles that guide the design, development, deployment, and use of AI. At its core, it is about building trust, ensuring AI systems empower organisations and stakeholders while minimising unintended consequences. Responsible data governance is the practical arm of responsible AI. It involves establishing policies, controls, and processes to ensure that data is accurate, complete, consistent, and auditable.
Prioritise Data for Responsible AIThe takeaway from this episode is clear and that is responsible AI starts with responsible data. For organisations looking to harness AI effectively:
Invest in data quality and governance — it is the foundation of all AI initiatives.Embed ethical and legal principles in every stage of AI development.Enable collaboration across teams to ensure transparency, accountability, and usability.Start small, prove value, and scale — responsible AI is built step by step.Amy Horowitz’s insight resonates beyond the tech team: “Everyone’s ready for AI — except their data.” It’s a reminder that AI success begins not with the algorithms, but with the trustworthiness and governance of the data powering them.
For more insights, visit Informatica.
TakeawaysAI is only as good as its data inputs.Data quality has become the number one obstacle to AI success. Organisations must start small and find use cases for data governance.Hallucinations in AI models highlight the need for vigilant data oversight.Reputational damage from AI failures can be severe for organisations.Metadata plays a crucial role in data management and governance.Collaboration between data, AI, and development teams is essential.Data governance is a must-have, not a nice-to-have. Organisations need to enable their lines of business for effective AI implementation.Everyone is ready for AI, except for the quality of their data.
Chapters00:00 The Importance of Responsible AI and Trusted Data
02:49 Defining Responsible AI and Data Governance
05:40 Challenges in Data Quality and Governance
08:51 Real-World Examples of Data Quality Issues
11:51 The Role of Employees in Data Governance
14:41 Successful AI Outcomes Through Responsible Data Practices
17:42 The Risks of AI Governance and Reputational Damage
20:42 Collaboration Across Data, AI, and Development Teams
23:34 The Future of Metadata and Data Management
26:42 Key Takeaways for Data and AI Leaders
About InformaticaInformatica, founded in 1993, is an enterprise data management company headquartered in Redwood City, California. The company provides software products for data integration, data quality, master data management, and data governance. With approximately 9,000 global customers across various industries, Informatica has positioned itself as a significant player in the data management market.
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“What is the true value of our data and AI initiatives?”
Too often, we drive all our energy into tools, processes, and outputs, but forget to ask ourselves how what we build actually makes a difference. For enterprises, this means looking beyond AI models and dashboards to see how our data drives real, measurable impact. Understanding the difference between output and outcome is what separates activity from transformation.
In this episode of Don’t Panic, it's Just Data, host Doug Laney and Nadiem von Heydebrand, CEO and Co-founder of Mindfuel, explore how organisations can turn data and AI efforts into actionable business outcomes. They discuss the concept of the “value layer”, a framework connecting data initiatives to business needs, emphasising the importance of understanding business problems before developing solutions.
Nadiem stresses that prioritising initiatives and fostering strong collaboration between business and data teams are critical to unlocking maximum value from data and AI efforts.
Why Data and AI Impact Management MattersMany organisations are investing heavily in data and AI, but turning these investments into real business value remains a challenge. This is because a critical gap exists between technical execution and business outcomes. Data and AI teams work on initiatives without first clarifying what business problems they're solving or how success will be measured.
Data and AI Impact Management bridges this gap by establishing the “value layer" between business strategy and technical platforms. This approach starts with structured demand management for use cases, enables systematic prioritisation based on actual value potential, and tracks initiatives throughout their lifecycle to ensure they deliver impact against business goals. This shift, from building solutions in search of problems to solving qualified business problems with purpose-built solutions, transforms data and AI teams from technical support functions into strategic partners who deliver value, stronger strategic alignment, and lasting competitive advantage.
Nadiem says, “Applying a product mindset within data initiatives is key, and it's the foundational effort to be able to drive value.”
He also notes that not every use case delivers direct financial impact, and the value layer helps clarify demand, manage use cases effectively, and uncover each initiative’s business value
For more insights and solutions, visit Mindfuel
TakeawaysOrganisations struggle to connect data initiatives to business outcomes.The value layer is essential for linking data to business demands.Understanding the actual business problem is crucial for success.Value management encompasses the entire lifecycle of initiatives.A product mindset helps focus on outcomes rather than outputs.Not all data use cases have direct dollar values.Data and AI impact management creates transparency for data teams.Establishing a product mindset is key for data products.Connecting processes to the operating model enhances effectiveness.Collaboration between business and data teams is vital for unlocking value.
Chapters00:31 Introduction: Don't Panic, It's Just Data
01:37 The Missing Piece: Introducing the Value Layer
07:11 Value Management Lifecycle
10:46 Product Mindset in Data Initiatives
14:10 Distinguishing Value and Impact
17:04 Impact Management and Investment Justification
19:34 Mindfuel's Three-Step Guide to Impact Management
21:00 Conclusion and Key Takeaways
About MindfuelMindfuel is a data and AI impact management platform that gives data, analytics and AI teams a single source of truth to prioritise high-impact use cases, connect initiatives to business outcomes, and demonstrate ROAI. It replaces scattered tools and reactive, manual processes with a structured approach to managing use cases and data and AI products. This enables organisations to reduce business case bias, eliminate inefficiencies, and clearly communicate the value of AI initiatives, driving enterprise-wide trust, transparency, and impact.
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As AI becomes a central pillar of business decision-making, enterprises face a new challenge, and that is making their data AI-ready. It’s no longer enough to collect and digitise information. For organisations, data must be structured, contextualised, discoverable, and usable—both by humans and intelligent systems.
AI can only deliver if your data is truly ready, but most enterprises are drowning in fragmented, incomplete, or slow-to-update data. In this episode of Don't Panic, It's Just Data, host Doug Laney and Sushant Rai, Vice President of Product of AI and Data Strategy at Relito, explore how modern data unification strategies are changing enterprises, enabling AI to deliver faster, more reliable insights. They focus on the shift from traditional Master Data Management (MDM) to next-generation AI-ready data cores, uncovering the risks of fragmented data and the strategies to overcome them.
Why AI-Ready Data MattersAI, especially large language models (LLMs), is changing how people interact with data. Analysts, executives, and frontline teams now expect natural language queries and instant, actionable insights.
Sushant explains:
"AI performs at its best when it has full context, empowered with the right data. This allows AI agents to make decisions and take actions on behalf of your business."When you embed intelligence into your data layer, AI can help you manage and scale your data without drowning your teams in manual work. This will only work if your data is structured, clean, governed, and constantly updated, everything that makes it truly AI-ready.
The Data Scale ChallengeThe volume of data being turned over daily is staggering.
As Sushant notes:
"The amount of data getting generated every single day is so massive that there’s no way to keep up without AI. Even the largest organizations, with massive data stewardship teams, can’t catch up manually."This gap is driving the change in the modern data platforms, where AI automates stewardship, enriches data continuously, detects anomalies, and maintains quality in real time.
Want to learn more about modern data unification and AI-ready platforms? Visit Reltio.com for insights, resources, and case studies.
TakeawaysData unification provides a trusted, real-time view of key business elements.Organizations must balance speed and trust in data management.Classic MDM is evolving into modern data unification platforms.Real-time data access is crucial for AI and analytics.AI can enhance data quality and governance processes.Successful data initiatives require clear business outcomes and ownership.Data unification should be viewed as a business platform, not just an IT project.AI agents will play a significant role in automating data governance.Organizations need to focus on both structured and unstructured data.The future of data management involves continuous unification and enrichment of data.
Chapters00:00 Introduction to Data Unification and AI
07:52 The Importance of Data Unification in Enterprises
15:44 AI and Data Quality Management
23:20 Organizational Success Factors for Data Initiatives
25:16 Future Trends in Data and AI
About ReltioAt Reltio, we believe data should fuel your success in the enterprise AI era. Reltio Data Cloud™ is the agentic data fabric for the enterprise—powering real-time data intelligence and AI transformation. Reltio’s cloud-native SaaS platform delivers unified, trusted, and context-rich data across domains in real time. With Reltio, organizations gain 360-degree views of customers, products, suppliers, and more—mobilized in milliseconds to any application, user, or AI agent. Trusted by the world’s largest enterprises across life sciences, financial services, healthcare, technology, and more, we help organizations fuel frictionless operations, drive innovation, and reduce risk.
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While the role of a chief data officers (CDOs) was traditionally focused on regulatory compliance, it has now expanded to empowering the consistent and effective use of data across organizations to improve business outcomes. One of the most effective ways for CDOs to demonstrate their value is by developing a data strategy that is closely aligned with business goals, processes, and outcomes.
In the latest episode of Tech Transformed, host Kevin Petrie, VP of Research at BARC, speaks with Brett Roscoe, Senior Vice President and GM of Cloud Data Governance and Cloud Ops at Informatica, about the evolving role of CDOs. Their conversation explores how CDOs are transitioning from data stewards to strategic leaders, the importance of data governance, and the challenges of managing unstructured data.
The Role of the CDO in the Agentic EraAs Roscoe notes, “CDOs are now pivotal in AI strategy,” reflecting how the role has grown from compliance oversight to guiding enterprise initiatives that directly support organizational goals.
In this day and age, CDOs are tasked with ensuring that data is both accessible and reliable, providing a foundation for informed decision-making across business units. This includes establishing policies for data quality, access, and governance, which Roscoe highlights as essential: “data governance is foundational for AI.” At the same time, unstructured data ranging from documents and emails to multimedia adds complexity that requires careful management to make it useful while minimizing risk. “Unstructured data presents challenges,” he adds, emphasizing the need for structured oversight to fully leverage these assets.
AI StrategyAlthough technology and analytics are evolving rapidly, the CDO’s role in aligning data with strategic initiatives is critical. By connecting data assets to business processes, CDOs help ensure that initiatives are informed by reliable, well-governed information and can deliver measurable results.
For anyone looking to understand the evolving responsibilities of CDOs, the importance of governance, and strategies for handling unstructured data, this episode of Tech Transformed provides a detailed and practical discussion.
For more insights, follow Informatica:
X: @informaticaInstagram: @informaticacorpFacebook: https://www.facebook.com/InformaticaLLC/LinkedIn: https://www.linkedin.com/company/informatica/
TakeawaysCDOs are now central to shaping AI strategies and driving business growth.Robust data governance is crucial for the successful deployment of AI technologies.Unstructured data presents unique challenges and opportunities for AI development.A balance between centralized governance and federated operations is essential.Securing executive support is vital for the success of CDO-led initiatives.Engaging business stakeholders enhances the impact of AI projects.Demonstrating ROI through clear metrics is key to sustaining AI investments.AI governance must extend beyond data to include models and agents.New measures are needed to ensure the quality and governance of unstructured data.CDOs must navigate the tension between fostering innovation and maintaining governance standards.
Chapters00:00:00 Introduction to the Podcast and Guests
00:03:00 Brett Roscoe's Background and Role
00:06:00 The Evolving Role of CDOs
00:09:00 Data Governance as a Foundation for AI
00:12:00 Challenges with Unstructured Data
00:15:00 Governance Frameworks for AI and Data
00:18:00 Centralization vs. Decentralization in Data Governance
00:21:00 CDO Strategies for Success
00:24:00 Conclusion and Future Outlook
About InformaticaInformatica, founded in 1993, is an enterprise data management company headquartered in Redwood City, California. The company provides software products for data integration, data quality, master data management, and data governance. With approximately 9,000 global customers across various industries, Informatica has positioned itself as a significant player in the data management market.
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The challenge all organisations, big and small, face is answering and implementing solutions to solve this key question: How can finance and accounting teams work faster, smarter and more accurately?
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, speaks with Kevin Gibson, CPA and Principal Solutions Engineer at insightsoftware. They talk about the constantly changing nature of financial reporting.
Additionally, they discuss the pros and cons of modern financial reporting and the importance of connecting financial data with familiar tools like Excel. The conversation also touches on the future of financial reporting technology and the need for organisations to adapt to changing data access needs.
Uncertainty in a Data-Driven World“With all this uncertainty, companies are being asked to look at their data in different ways. They want to pivot it, slice it, and dice it,” Gibson tells Taylor, encapsulating the theme of this episode. “They’re being told to do more with the data — what does it mean, how do we read it, how do we understand it, how do we analyse it?”
The issue is that, as enterprises invest in digital transformation, finance teams struggle most with limited access to the data they need to support their analysis.
“The ideal state,” Gibson adds, “would be: I can get what I want, when I want, and how I want it — without asking questions. But let’s be honest — that doesn’t exist today.”
However, the good news is the data exists, Gibson says. The ugly part is that organisations can’t get to it. Many of the data accessibility issues have been attributed to cloud migration.
“When you move your data to the cloud, you think: it’s cheaper, it’s more secure, it’s easier to maintain. But here’s the problem: you don’t control it anymore. Some cloud providers make access difficult or costly. So finance teams feel stuck,” he explains.
Also Watch: Stop Fighting Excel: How to Turn Your Spreadsheets into a Real-Time Reporting Powerhouse?
Real-Time Access on ExcelFor decades, finance professionals have relied on Excel, which Gibson refers to as the “largest data warehouse in the world.” “There are 1.1 billion users of Excel today,” he says. “And let’s be honest, I haven’t met an accountant yet who says they hate it.”
Finance prospers in Excel, but IT often views it as a risk. This leads to a constant back-and-forth between usability and control. Gibson believes that the solution is to equip both sides- finance and IT with real-time, governed data inside Excel.
That’s where insightsoftware comes in. “We can connect directly to these systems and give finance teams back their real-time access — not just to pieces of data, but all of it,” says Gibson. “Literally every piece of data can be accessed.”
With tools like Spreadsheet Server, finance professionals can work in Excel — their “comfort food,” as Gibson calls it — while drawing directly from live ERP data in the cloud.
“We give them insight — that’s what our software does. It gives them visibility into their data. Excel isn’t going away, and our job is to make it work even better.”
To learn more, watch or listen to the podcast on EM360Tech.
Also Watch: Struggling with ERP Data? How to Get Real-Time Reporting in Excel
TakeawaysFinance professionals are facing increased pressure to analyse data amidst uncertainty.The ideal state for finance teams is immediate access to data without barriers.Modern financial reporting has its good, bad, and ugly aspects, primarily revolving around data accessibility.Different industries have unique data needs and KPIs that impact financial reporting.Excel remains a critical tool for finance professionals despite the rise of cloud-based solutions.Organisations must find ways to connect their financial data with familiar reporting tools.The future of financial reporting will continue to evolve around Excel and data accessibility.Companies are beginning to realise the limitations of cloud-based systems and may revert to on-prem solutions.AI is emerging as a significant factor in data access and reporting.C-level executives should engage with finance teams to ensure they have the tools they need.
Chapters00:00 Introduction to the Podcast and Guest00:59 Understanding the Role of Finance in Uncertain Times04:00 The Good, Bad, and Ugly of Financial Reporting08:13 Types of Data and Industry-Specific Challenges10:00 Connecting Financial Data with Reporting Tools13:59 The Future of Financial Reporting and Technology17:58 Key Takeaways for C-Level Executives
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"A flaw of warehouses is that you need to move all your data into them so you can keep it going, and for a lot of organisations that's a big hassle,” says Will Martin, EMEA Evangelist at Dremio. “It can take a long time, it can be expensive, and you ultimately can end up ripping up processes that are there."
In this episode of the Don’t Panic It’s Just Data podcast, recorded live at Big Data LDN (BDL) 2025, Will Martin, EMEA Evangelist at Dremio, joins Shubhangi Dua, Podcast Host and Tech Journalist at EM360Tech. They talk about how enterprises can enable the Agentic AI Lakehouse on Apache Iceberg and why query performance is critical for efficient data analysis.
"If you have a data silo, it exists for a reason—something's feeding information to it. You usually have other processes feeding off of it. So if you shift all that to a warehouse, it disrupts a lot of your business," Martin tells Dua.
This is where a lakehouse comes into play. Organisations can federate their access through a lakehouse data approach. They can centralise access to the respective organisation’s lakehouse while keeping their data in its original location. Such a system helps people get started quickly.
In terms of data quality, if you access everything from one location, even with separate data silos, you can see all your data. This visibility allows you to identify issues, address them, and enhance your data quality. That’s beneficial for AI, too, Martin explains.
Lakehouse Key to AI Infrastructure?Lakehouse has been recognised for unifying and simplifying governance. An imperative feature of a lakehouse is the data catalogue, which helps an organisation browse and find information. It also secures access and manages permissions.
"You can access in one place, but you can do all your security and permissions in one place rather than all these individual systems, which is great if you work in IT,” reflects Martin. "There are some drawbacks to lakehouses. So, a big component of a lakehouse is metadata. It can be quite big, and it needs managing. Certain companies and vendors are trying to deal with that."
With AI and AI agents, it’s become even harder to optimise analytics on a lakehouse. However, this has been improved as technical barriers are disappearing. Martin explains that anyone can prompt a question; for instance, an enterprise CEO could ask questions about the data and demand justifications directly.
In the past, a request would have to be submitted, and then a data scientist or engineer would create the dataset and hand it over. Now, engineers' roles have changed to focus on better optimisation. They help queries run smoothly and ensure tables are efficient. Agents cannot assist with that.
Also Listen: Dremio: The State of the Data Lakehouse
Optimise LakehouseVendors such as Dremio provide services to manage and optimise lakehouses. They offer autonomous features to help set up the workflow. Martin says that in many cases, Dremio learns from the clients’ actions and improves their system. “This is evident in our reflections, which are optimised datasets that speed up performance,” he added.
“In other situations, we handle tasks like file compaction and garbage collection, which are often less exciting for engineers. Now, there’s no need for engineers to manage those tasks, which benefits everyone.”
As a lakehouse provider, Dremio is Iceberg native. They began their journey as a lakehouse provider and continue down this road. Now the industry has shifted gears to focus on lakehouses too, first with Snowflake and now even Databricks, which has developed its format with Delta Lake.
The ultimate goal is to incorporate more features—permissions, governance, and fine-grained access control. “These capabilities are things vendors typically sell, but they will soon become widely available for free,” Martin tells Dua.
Learn More: Visit dremio.com for more information on open data lakehouse technology.
Key TakeawaysAgentic AI and Apache Iceberg are current hot topics.Lakehouses offer quicker, less disruptive data access for AI compared to data warehouses.Centralised access in a lakehouse improves data quality and simplifies AI integration.Lakehouses, with their data catalogues, ease governance and permission management for AI agents working with sensitive data.Apache Iceberg is resolving metadata format issues, though metadata management remains an overhead.Dremio, an Iceberg-native provider, champions open source and interoperability, offering autonomous optimisation features to free engineers from mundane tasks.Beyond technology, a robust data strategy is crucial for organisational data improvement.Agentic AI will evolve to handle more delegated, multi-step tasks with less supervision.The open-source ecosystem will see consolidation and improved features, making advanced catalogue and governance tools widely available.Ultimately, for IT decision-makers, the quality of data is paramount for all analytical endeavours, including AI.
Chapters0:00 - Introduction to Agentic AI0:35 - Discussing Big Data London, Hot Topics: Agentic AI and Apache Iceberg1:37 - Data Lakehouse vs. Data Warehouse for AI2:30 - Data Quality and AI with a Lakehouse3:18 - AI Agents and Sensitive Data: Governance with a Lakehouse4:19 - Challenges and Solutions in Lakehouse Technology (Apache Iceberg)5:47 - Dremio's Use Cases and Interoperability7:40 - Dremio's Standout Features and Autonomous Optimisation9:39 - The Importance of Data Strategy10:29 - Future of Agentic AI11:34 - Future of the Open-Source Ecosystem12:51 - Final Takeaway for IT Decision Makers: Data Quality is Critical13:51 - Conclusion
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At Big Data LDN (BDL) 2025, Keboola CEO Pavel Dolezal presented a new data agent designed for all business users, not just engineers. With a mission to make AI, automation, and data easy to access, relevant, and useful across the organisation, Dolezal revealed that the data agent has been embedded with contextual intelligence and generative AI.
“While we typically assist data engineers with building the pipeline, we took the same data agent and built a different environment for it — a chat-like environment. By default, the chat has context, knows what to do, knows where not to go,” the Keboola CEO unveiled on the Don’t Panic It’s Just Data podcast.
In the EM360Tech podcast recorded live at BDL, Dolezal spoke to Christina Stathopoulos, the Founder of Dare to Data in the recent episode of the Don’t Panic It’s Just Data podcast. They talked about the new Keboola Data Agent and it plays a key role in AI-backed change and the growth of large language models (LLMs) in business.
Context for AI-Backed Data Strategy Matters More“Anyone can be technical now. It’s context that matters,” stated Dolezal, also the co-founder of Keboola. He presented a strong argument for why enterprise data strategies are falling short and how a new wave of smart tools will change that.
“The pipeline of what you can do is limitless if you build it for people in business,” he added. “You can't keep data and AI just in the hands of engineers anymore. That model doesn’t scale.”
As businesses face a growing number of data sources — sometimes over 300 SaaS platforms and more than 80 departments — managing, governing, and activating that data has become a challenge. The appealing promise of AI often adds another layer of complexity.
When AI Adds More ComplexityEnterprise leaders were told that AI would simplify data workflows. Instead, many found themselves managing disconnected tools and failed pilot projects.
“We all read the MIT study. 95 per cent of AI proofs of concept don’t make it to production,” the CEO of Keboola highlights. “Why? Because large language models (LLMs) need context. And enterprise data environments are anything but simple.”
At Keboola, context is crucial, he emphasised. It includes not just metadata but also event logs, debug trails, and orchestration details, including the complete story behind every data product.
“LLMs thrive on context. The more relevant context you provide, the better the outcome. But in today’s data stack, where your context is spread across 15 tools, that's nearly impossible.”
This is where Keboola’s new Data Agent comes in. It is a generative AI interface built directly into data workflows, capable of understanding and acting on both the structure and state of a company’s data.
Watch the podcast for further insights on EM360Tech.
Key TakeawaysFocus on context and domain knowledge rather than technical skills.Ease of acquiring technical skills compared to the importance of understanding business processes.Agents will run business processes both internally and externally.Need for infrastructure to support agents Challenges of provisioning ad hoc environmentsUse agents to automate business processes.Importance of data governance.
ChaptersIntroduction & Keboola's Mission: 0:00Challenges in Modern Data Management: 2:23Impact of Complexity on Teams: 4:04LLMs in Data: Potential and Pitfalls: 5:50Keboola Data Agent: Bridging the Gap: 8:12Evolution of Keboola Data Agent: 13:00Future Vision for LLMs & Agents: 15:05Key Takeaway for Leaders: 17:12
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