Don't Panic! It's Just Data

Podcast: Don’t Panic! It’s Just DataGuest: Adrian Estala, VP, Field Chief Data & AI Officer, StarburstHost: 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

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.comTakeawaysAI 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 procurementChapters00:00 AI Ambition vs. Readiness05:02 The Procurement Landscape and AI Adoption09:10 Data Foundations for AI Success13:03 Unified Data Models in Procurement16:43 The Human Element in AI Integration25:57 Real-World Applications of AI Agents32:22 Key Takeaways for Leaders in AI Adoption

Artificial intelligence is everywhere right now, in boardrooms, strategy meetings, and product roadmaps. Organisations are investing heavily in machine learning, automation, and generative AI, all with the same promise: unlock new revenue and work smarter.In the latest episode of the Don’t Panic It’s Just Data podcast, EM360Tech’s Trisha Pillay explores this challenge with Chief Technology Officer Paul Brownell and Sergio Morales, Data and AI Engineering Leader from Growth Acceleration Partners. Their discussion unpacks why so many AI initiatives fail to translate into revenue and why the real starting point isn’t the model itself, but the data, governance, and engineering practices that make meaningful outcomes possible.But here’s the uncomfortable truth and that is many AI strategies look powerful on paper, but the real financial impact is often unclear. This disconnect, called the revenue data gap, highlights an issue many organisations overlook. AI doesn’t create value on its own especially without strong data foundations, governance, and engineering discipline, even the most ambitious AI strategy will struggle to deliver measurable results.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 Gaps02:31 Understanding the Revenue Data Gap05:47 Challenges of Legacy Architecture in AI09:11 Closing the Revenue Data Gap12:29 The Velocity Mandate in AI Implementation16:42 Strategic and Technical Alignment for AI18:31 Engineering Considerations for AI Initiatives22:03 The Role of Data Contracts in AI Success28:55 Practical Takeaways for AI ImplementationTakeawaysThe revenue data gap is a common challenge that organisations face when implementing AI.It’s crucial to define a clear data hypothesis and ensure data quality to drive measurable business impact.Data contracts work only if teams know who owns datasets, how to maintain them, and how changes are communicated.Balancing the velocity mandate with governance is key. Engaging stakeholders and mapping the value chain ensures that AI initiatives are aligned with business needs, ultimately leading to revenue growth.

Enterprise AI budgets are climbing, but the data foundations beneath them remain uneven. In this episode of Don’t Panic, It’s Just Data, Kevin Petrie, VP of Research at BARC, and Nathan Turajski, Senior Director, Product Marketing at Informatica, examine the findings of the CDO Insights 2026 report, which argues that executive confidence in AI may be outpacing organisational readiness. The study centres on what it describes as a growing “trust paradox” as Chief Data Officers are accelerating AI initiatives even as data quality, governance maturity, and AI literacy struggle to keep up. The 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.comTakeawaysThe 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 Report03:13 Understanding the Trust Paradox in AI Adoption08:34 Building Trusted Context for AI14:11 The Importance of Data Quality and Completeness20:28 Navigating the Vendor Landscape for Data Management23:09 From Experimentation to Measurable Results27:38 Recommendations for CDOs and CISOs

What if the real advantage in AI lies not in having more data, but in having less?In this episode of the Don’t Panic, It’s Just Data podcast, host Shubhangi Dua, Podcast Producer and B2B Tech Journalist at EM360Tech, sits down with Herb Blecher, Research Director of Data and Analytics at Enterprise Management Associates (EMA). This conversation challenges a common belief in enterprise tech – that gathering everything ensures insight. Blecher, alluding to the modern-day AI craze, cautions the enterprise audience that just because you can access vast amounts of unstructured data doesn’t mean you should.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” InevitableWhy 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 LeadersAbout 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