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Join this episode of DM Radio to hear from Ara Kharazian, Lead Economist at Ramp and Barr Moses, CEO and Co-Founder of Monte Carlo AI. We'll discuss the rapid rise of AI agents, and the challenges of maintaining control over what they do. We'll also examine the narrative that AI is taking away jobs. Spoiler alert: Ramp data says otherwise, especially for heavy adopters! Don't miss this chance to hear from the source of real-world data at-scale.

As AI moves into an increasingly agentic era, enterprises need more than powerful models - they need trusted data, meaningful context, and control over how AI systems access and use information. Join this episode of DMRadio as Adam Gunther of Vertica at Rocket Software discusses how the company's MCP server connects LLMs directly to enterprise data, while Vertica's analytics and in-database machine learning capabilities help ground probabilistic AI with deterministic insights. He will be joined by Meeta Vouk of Rocket Software and Patrick Osborne of HPE, who explore the growing role of unstructured data and the need for "provable governance" as AI agents become more autonomous. The conversation covers blending structured and unstructured data, monitoring model drift, controlling agent access, and supporting secure AI deployments across cloud, on-premises, and sovereign environments.

AI Agents are moving fast, tackling all manner of enterprise workflows, and revolutionizing how things get done. Agentic trust is suddenly a mandate, as organizations employ these worker bees in new and innovative ways. The big question now? How can these Agents be supervised, managed and optimized? Register for this episode of DM Radio to learn from veteran Analyst David Linthicum will share key insights into the hard challenges of AI Supervision. Joining the show will be Lior Gavish, CTO and Co-Founder of Monte Carlo Data, who will demonstrate how their platform provides critical functionality for monitoring and managing AI Agents. Attendees will learn: How to establish visibility into agentic behavior across workflows, systems, APIs, and data sources How to enforce guardrails and policies at runtime - not just during development and testing How to detect and contain rogue or unexpected agent behavior before it spreads across the enterprise How to build an Agentic Governance framework that balances autonomy, accountability, security, and business value How to not get hacked, a la the Hugging Face fiasco

*]:pointer-events-auto R6Vx5W_threadScrollVars scroll-mb-[calc(var(--scroll-root-safe-area-inset-bottom,0px)+var(--thread-response-height))] scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" data-turn-id= "request-6ab6efe8-174c-83ed-98be-d365d09dbca8-2" data-turn-id-container= "request-6ab6efe8-174c-83ed-98be-d365d09dbca8-2" data-testid= "conversation-turn-16" data-turn="assistant"> div:has([data-free-thinking-preview-answer=true])+:is(.text-message,.relative:has(>.text-message))]:-mt-2 grow"> Join this episode of DM Radio as host Eric Kavanagh speaks with Michael Leworthy of Everpure and Philip Dutton of Solidatus about breaking down enterprise data silos and building a stronger foundation for governed AI. Together, they discuss managing data where it lives, AI's growing need for direct access to raw data, and the importance of data lineage, metadata, context, and governance. Learn how organizations can map data across legacy and modern systems - and maintain visibility and control as AI agents increasingly interact with enterprise data and generate code.

Your data model is fast becoming AI infrastructure. As enterprises deploy agents and LLMs, tried and trusted data models are providing the structure and context AI needs to understand what corporate data really means. Sometimes called ontologies, these proven constructs are trending again because of the critical role they play in grounding Enterprise AI. Register for this special episode of DM Radio to hear Host Eric Kavanagh explain why AI architecture begins and ends with a data model. He'll be joined by Jamie Knowles of Idera, who will demonstrate how ER/Studio is filling the gap between powerful AI technologies and traditional enterprise Information Architectures. Attendees will learn:* Why the Entity-Relationship model matters more than ever * How data models enable trustworthy outputs from LLMs * What metadata can provide in terms of connective tissue * Where to begin your AI journey based upon your business needs

AI agents may be new, but the data problems they encounter are not. Before an agent can answer a question or take action, it needs to know what data exists, what it means, and how trustworthy it is. A single errant join, stale table, ambiguous definition, or missing access rule can quietly turn an otherwise capable agent into a confident source of bad decisions. And when agents string multiple steps together, even strong accuracy rates can deteriorate quickly. The good news? Much of the infrastructure needed to solve this problem already exists, if organizations rethink how data is collected, understood, and governed. Instead of repeatedly moving data through pipelines and warehouses, one creative solution uses an immutable, replayable fact log, continuously profiling data as it arrives and capturing schema changes, lineage, transformations, distributions, and other signals along the way. Tune into this DM Radio episode as host Eric Kavanagh talks with Mike Kowalchik of Matterbeam about why infrastructure originally designed to make data easier for humans may prove even more valuable for AI agents, and how evidence-backed inference, agent-specific governance, and reusable data context can help turn promising prototypes into trustworthy production systems. Visit https://matterbeam.com/ for details!

As AI moves from experimentation to enterprise deployment, the bottleneck is shifting from compute to the data infrastructure that powers it. Join this episode of DMRadio as host Eric Kavanagh speaks with Adrian Johnson of Hitachi Vantara about high-performance data access, on-premises infrastructure, and the growing demands of agentic AI.  Explore AI guardrails, data silos, knowledge graphs, Apache Iceberg, and how organizations can modernize legacy environments while keeping sensitive data secure and under control.

AI is transforming the workplace - not necessarily by eliminating jobs, but by fundamentally changing how people work and the skills they need. Join this episode of DM Radio as Eric Kavanagh speaks with Roberta Gamble, Chief Research Analyst at FOURCASTERS, about the real impact of AI on jobs, marketing, communications, and the evolving role of the knowledge worker. Learn why AI is making basic tasks easier while pushing professionals toward broader, more versatile skill sets and how it's being applied to corporate communications and the public sector, where better technology could dramatically improve everyday interactions with government services.

Apache Iceberg is rapidly becoming the architectural foundation for the modern enterprise data estate, enabling organizations to decouple storage from compute while preserving openness and governance at scale. By bringing database-grade capabilities like ACID transactions, schema evolution, time travel, and intelligent metadata management to low-cost object storage, Iceberg transforms the traditional data lake into a shared, high-performance platform that can power analytics, AI, machine learning, and real-time applications alike. The result is a more flexible data architecture that ideally eliminates redundant data copies, simplifies operations, and dramatically reduces vendor lock-in. On this episode of DM Radio, we'll explore why so many organizations are embracing what some call the "Iceberg Revolution," and what it means for the future of enterprise data architecture. We'll examine how Iceberg enables a composable data stack, supports multiple compute engines against a single source of truth, and lays the groundwork for semantic layers, knowledge graphs, and agentic AI.

What does it take to move quantum computing from the lab to the enterprise? Join this episode of DMRadio to hear host Eric Kavanagh interview Yuval Boger, Chief Commercial Officer at QuEra Computing. Learn how the industry is shifting its focus from scientific milestones to solving real business challenges - from discovering new materials to optimizing supply chains - and what that means for the future of quantum.

Join this episode of DM Radio as host Eric Kavanagh sits down with Sovan Bin, CEO and Founder of Odaseva, to explore the growing challenges of managing and securing Salesforce data in today's AI-driven world. Drawing on his experience as a Salesforce architect, Sovan shares how conversations with enterprise customers revealed critical gaps in data governance, security, and control as organizations moved from on-premises systems to the cloud. Find out how those challenges inspired the creation of Odaseva and why protecting mission-critical CRM data has become more important than ever in an era of increasingly sophisticated cyber threats. Tune in to learn how organizations can strengthen Salesforce data management, improve governance, and build a more resilient security strategy while continuing to innovate in the cloud.

Join this episode of DM Radio as host Eric Kavanagh explores how organizations are turning complex enterprise data into actionable intelligence. First, he interviews Darren Fullwell of IP Fabric, who explains how network digital twins give IT teams complete visibility into their environments. Next, Rivers Morrell and Dr. Reza Nehzati of HeyDonto, joined by Mark Madsen of Third Nature, discuss Data Field Theory and a new approach to preparing structured and unstructured data for trustworthy AI. Hear practical insights into enterprise networking, AI, data architecture, and the future of intelligent systems in this episode of DM Radio.

Recorded live at the Accelerate conference, this episode of DM Radio explores why data - not applications - is becoming the foundation of enterprise AI.  Join host Eric Kavanagh as he speaks with Chadd Kenney of Everpure about the shift toward data-centric architectures and the importance of shared context for powering intelligent AI agents and real-time decision-making. Next, Shawn Rosemarin of Everpure explains why context is to AI what memory is to humans, illustrating how connected, meaningful data enables more accurate, trustworthy outcomes. Together, these conversations reveal why contextual intelligence is emerging as the key ingredient for the next generation of enterprise systems.

The forcing function of AI continues to reshape how organizations think about data architecture. It's no longer enough to simply centralize information in warehouses and lakes. AI systems require context, lineage, relationships, governance, and semantic understanding to deliver trustworthy outcomes at scale. The solution? An AI-ready knowledge architecture. On this episode of DM Radio, we'll examine the nexus of semantic intelligence and metadata management as core components of an enterprise knowledge graph. Eric Kavanagh will host Sebastian Schmidt of Digital Science, who will demonstrate why a solid data foundation and intelligence layer - such as provenance tracking - are crucial for AI trust. They will show how AI Agents, RAG Architectures, and next-generation discovery platforms will only coalesce around trusted, contextualized data. Attendees will learn: Why semantic intelligence is becoming the control plane for enterprise AI How metadata, lineage, and provenance improve trust and reduce hallucinations Why knowledge graphs outperform siloed architectures for AI-driven discovery How AI-ready architectures support RAG, agentic AI, and contextual reasoning at scale

AI agents are now standard in enterprise workflows but most organizations have no idea they've created a governance time bomb. When agents connect to Salesforce, Snowflake, Tableau, and a dozen other systems through separate MCPs, the result is a fragmented audit trail with no single record of who accessed what data, under whose authorization, or how an answer was generated. Under GDPR, HIPAA, SOX, and the EU AI Act, that's big regulatory exposure. Join Shawhin Mosadeghzad and Terrence Sheflin as they examine why multi-MCP architectures create compliance gaps that traditional data governance tools weren't built to address, and why the fix has to happen at the access point. We'll discuss how a single governed semantic layer acts as the sole MCP for AI data access, enforcing row- and column-level security, logging every query with full user attribution and deterministic lineage, and satisfying the individual attribution requirements regulators are actively closing in on without sacrificing architectural control or LLM flexibility.