Don't Panic! It's Just Data

Not only do many businesses have more data than they know what to do with, but they also often struggle to gain insights from some of the most valuable data in their possession, leading to many of their crucial data assets going unused. Whether it's issues with data quality, visualization, or management, getting lost in the sea of enterprise data at your possession can make it impossible to make smart, data-driven decisions that improve your business. The "Don't Panic! It's Just Data" podcast delves deep into the power of enterprise data. From groundbreaking vendor solutions to expert-backed best practices for making the most of your data assets, join us as we gather insights from leading tech vendors and professionals who depend on data daily.

Don't Panic! It's Just Data is a business podcast hosted by Unknown Host, with 178 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Unknown Host hosts Don't Panic! It's Just Data, a business show with 178 episodes published.

Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.

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  Episode #70

  How Does a Unified Data Platform Improve Financial Crime Compliance?

  Jul 28, 202625 minS1

  For a long time, financial crime prevention has been a massive issue for banks. This is because of several often complex navigation around rules and regulations, such as AML, KYC, CTF, and SAR. However, a bigger issue has emerged. As more and more transactions happen and financial crime networks get smarter, the bigger problem is the way banks handle data. In the recent conversation on the Don’t Panic It’s Just Data podcast , host Herb Blecher , Research Director, Data and Analytics, Enterprise Management Associates (EMA), sat down with guest Manish Andhy , Financial Services AI & Industry Executive at Teradata . They talked about how criminal enterprises are changing fast, like the internet, while compliance systems are still using old methods. Andhy tells Blecher, "The difference between how fast criminals can change and how slow compliance systems are is a big weakness." More than $3 trillion in money moves through the global financial system every year, but only a small part of it is caught. Banks have spent a lot of time and money building systems to stop money laundering. Many of them still have separate systems for onboarding, transaction monitoring, investigations and risk management. The strange thing is that big financial institutions have already spent a lot of money to solve this problem. They built data warehouses to get rid of systems and data lakes to put all the information in one place. In fact, they often created the same separate systems they were trying to get rid of. Also Watch: What is AI Hyper-Personalisation in CX and Why Does it Matter? Takeaways Over $3 trillion of illicit funds moves through the global financial system annually. Only about 1% of illicit funds are detected or intercepted. AML programs have grown in a fragmented, siloed way across enterprises. The pace of change in compliance has historically been slow. Traditional rule-based systems generate enormous amounts of false positive alerts. Data silos persist due to organizational and technology issues. Data products allow for a unified view of data without creating new silos. Generative AI can automate parts of the compliance process. Financial institutions must treat data as a strategic asset. Compliance can inform broader business decisions beyond regulatory obligations. Chapters 00:00 Introduction to Financial Crime and Data Analytics 05:06 Challenges in Financial Crime Compliance 10:05 Data Silos and Their Impact 15:12 The Concept of Data Products 19:57 Modernizing Financial Crime Strategies 24:59 Conclusion and Key Takeaways For more information on financial crime and how financial institutions should manage their data securely and compliantly, follow Terada across its official channels: Website: Teradata YouTube: @Teradata LinkedIn: @Teradata X: @Teradata For more information on enterprise tech analyst-led insights, please visit em360tech.com EM360Tech YouTube : @enterprisemanagement360 EM360Tech LinkedIn: @EM360Tech EM360Tech X : @EM360Tech #Teradata #FinancialCrime #AML #Compliance #DataStrategy #AIinBanking #FinTech #DataSilos #DataManagement #DonTPanicItsJustData #EM360Tech #B2BMarketing #B2BPodcasts

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  Episode #69

  Context, Cost, & AI: Strategies to Optimise Agent Performance & ROI

  Jul 15, 202632 minS1

  After months of experimenting with large language models (LLMs) , enterprises are moving from isolated pilots to broad deployments. However, as adoption speeds up, many tech leaders find that the biggest challenge isn't selecting the right AI model but managing the cost of optimising it. While AI models might be getting more expensive owing to geopolitical tensions and expanding use cases, the added challenge is that the price per token continues to rise. The problem is that enterprise AI is using a lot more tokens as companies scale new applications across different departments. In the recent episode of the Don’t Panic It’s Just Data podcast , host Kevin Petrie, VP of Research at BARC, sat down with Eudald Camprubi , Co-Founder of Nuclia (acquired by Progress) and Software Fellow at Progress and Michael Marolda , Senior Product Marketing Manager, Agentic RAG. They talked about how the next phase of enterprise AI will focus less on model selection and more on context engineering, retrieval strategies, and governance. What Does the Future of Enterprise AI Rely on? As enterprises progress beyond experimentation, success will rely less on picking the next game-changing model and more on using existing models wisely. That means providing precise context instead of excessive context. Enterprises should select the right model instead of just the latest model. That means they should treat observability, retrieval strategies, and governance as foundational capabilities rather than second priorities. Camprubí said that enterprise leaders should be wary of industry hype. "Don't trust everything you read on LinkedIn.” In enterprise AI, measurable results will determine which enterprises effectively scale intelligent systems. Listen to the full episode of Don't Panic! It's Just Data to hear Michael Marolda and Eudald Camprubí discuss Agentic RAG , token optimisation, context engineering, and the future of enterprise AI at scale. Takeaways Token costs are climbing, making cost management critical for enterprises. Context is essential for effective AI implementation and user satisfaction. Enterprises must focus on providing the right context to LLMs to avoid hallucinations. Modularity in AI platforms allows for flexibility and adaptability in solutions. Quality metrics are vital for evaluating AI outputs and ensuring reliability. The latest AI models are not always the best choice for every use case. Understanding user intent is crucial for effective data retrieval. FinOps teams are increasingly involved in managing AI costs and strategies. Enterprises should consider RAG as a service to reduce maintenance burdens. Collaboration among stakeholders is essential for successful AI implementation. Chapters 00:00 Introduction to AI and Data Context 03:26 Understanding Token Costs in AI 09:03 The Importance of Context in AI 14:37 Exploring the Context Layer and Retrieval Strategies 22:31 Model Selection and Cost Management in AI 29:11 Key Takeaways for AI Leaders For more enterprise AI, Agentic RAG, data governance, and enterprise knowledge layer insights, follow Progress Software across its official channels: Website: Progress Software YouTube: @ProgressSW LinkedIn: Progress Software X: @ProgressSW For more information on enterprise tech analyst-led insights, please visit em360tech.com EM360Tech YouTube : @enterprisemanagement360 EM360Tech LinkedIn: @EM360Tech EM360Tech X : @EM360Tech #EnterpriseAI #ContextEngineering #AgenticRAG #AIROI #TokenCosts #AIStrategy #DataManagement #DonTPanicItsJustData #ProgressSoftware #Nuclia

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  Episode #68

  How Enterprise Data Architecture Must Evolve for the Age of AI

  Jun 15, 202632 minS1

  Most enterprises believe they have a data problem. In reality, it is an architecture problem in disguise, and the rise of agentic AI is making that distinction impossible to ignore. That is the central argument Karthik Ranganathan , CEO of Yugabyte , makes in this episode of Don’t Panic! It’s Just Data , hosted by Scott Taylor of MetaMeta Consulting. The conversation traces 30 years of infrastructure evolution in 30 minutes. From Oracle’s dominance as the monolithic backbone of enterprise applications, to the NoSQL revolution of the mid-2000s, and the cloud-native era of the 2010s, it builds toward the rise of agentic AI. In this new phase, systems do not just store and retrieve data; they act on it autonomously. “The challenges of current architectures under pressure are no longer theoretical. Agentic systems expose every seam, every silo, every bottleneck you've been quietly managing around,” says Raghanathan . Why Current Architectures Crack Under Agentic Pressure Taylor opens the episode by asking the question many enterprise data leaders are quietly asking: Are today’s architectures actually fit for AI workloads? Raghanathan’s answer is measured. Most are not, and the reasons are structural rather than superficial. The core issue is fragmentation. Decades of 'good enough' tooling decisions have produced estates where relational databases sit in silos next to document stores, vector indexes live apart from transactional systems, and data pipelines patch the gaps. For traditional applications, this messiness is manageable. For agentic AI, systems that must reason across context, execute multi-step decisions, and maintain coherent state across interactions, it’s a fundamental blocker. Raghanathan identifies three compounding failure modes: siloed knowledge stores that prevent AI systems from drawing on the full breadth of enterprise information; disconnected memory systems that can't persist context reliably across agent runs; and non-deterministic outputs from large language models (LLMs) that make it difficult to design stable data models around AI-generated results. Together, these problems don't just slow down AI projects; they erode the trust enterprises need to deploy agentic systems at any meaningful scale. Knowledge vs. Memory One of the episode's sharpest conceptual moves comes when Raghanathan draws a clean line between knowledge and memory in AI systems, two concepts that get conflated constantly, and at high cost. Knowledge, in this framing, is the structured, long-term body of facts and context an AI system can draw on: product catalogues, customer histories, domain documentation, and enterprise policies. Memory, by contrast, is the short-term, session-aware state that lets an AI system track what just happened, what's been decided, and what step comes next in a workflow. Most current architectures treat these as interchangeable or ignore memory entirely, forcing every agent interaction to start cold. The result is factually capable AI but contextually amnesiac; it knows the company's product catalogue but forgets it already recommended three options to this customer ten minutes ago. This is the gap Meko, YugaByte's knowledge-memory engine , is designed to close. By building a unified layer that handles both the persistent knowledge graph and the operational memory of active agent sessions, Meko allows enterprises to run agentic workflows without patching together vector databases, caches, and relational stores by hand. It's an architectural bet that the knowledge-memory distinction is not a nuance but a first-class design requirement and that enterprises ignoring it will pay the integration tax repeatedly. Bridging AI Capability To Business Value Taylor pushes Raghanathan on the perennial tension between AI capability and business value, a conversation that lands differently now that agentic systems are making decisions, not just recommendations. Raghanathan's view is that the critical role of context in AI workflows is still being underestimated by most enterprises. He cites YugaByte customers who have moved from proof-of-concept AI deployments to production-grade agentic systems by making one architectural shift, treating context as infrastructure, not as application logic. When context management is embedded in the data layer, versioned, auditable, and available across agent boundaries, the reliability bar for AI systems rises dramatically. For enterprises ready to act, Raghanathan’s guidance is clear: start by auditing existing architectures for knowledge and memory silos before evaluating AI tooling, invest in unified data models that support both relational and non-relational workloads, and treat the context layer as a core engineering concern rather than an afterthought added to LLM integrations. If you would like to find out more, visit yugabyte.com or connect with Karthik Ranganathan on LinkedIn. Takeaways Evolution of data infrastructure for agentic AI. Limitations of current architectures and silos. The role of knowledge and memory in AI systems. Strategies for enterprise data architecture in the AI era. Chapters 00:00 Introduction to Data and AI Infrastructure 03:02 The Evolution of Data Infrastructure 05:53 Understanding Agentic Applications 08:55 The Architecture War in Data Management 11:58 Knowledge vs. Memory in AI 15:00 Operational Challenges in Multi-Agent Environments 17:58 The Importance of Context in AI Workflows 20:49 Bridging the Gap Between AI and Business Value 24:10 Customer Success Stories with YugaByte 26:47 Rethinking Data Architecture for Enterprises

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  Episode #67

  No Use Case, No Value: Why Managing AI Use Cases is Key to Demonstrating Business Value

  Jun 2, 202633 minS1

  <p>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. </p><p>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.</p><p>"We get a demand, we believe we've understood...

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  Episode #66

  Why Enterprise RAG Systems Still Produce AI Hallucinations

  May 22, 202629 minS1

  <p>Podcast: Don’t Panic! It’s Just Data</p><p>Guest: Michael Marolda, Senior Product Marketing Manager for Agentic RAG at Progress Software</p><p>Host: Shubhangi Dua, Podcast Producer and B2B Tech Journalist at EM360Tech</p><p>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.</p><p>While the exci...

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