Category: Podcast Archives

Let's talk Data!

Real-Time Insights and Data Observability

Broadcast Date: March 16th, 2026 Join this episode of InsideAnalysis as host Eric Kavanagh interviews Sid Banerjee of Medallia about how organizations can extract meaningful, real-time intelligence from their data to better understand events as they unfold and drive smarter decisions. They are joined by Kunal Agarwal of Unravel Data, who explains why observability has…
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From Tokens to Vectors: The Substrate of AI

Broadcast Date: March 12th, 2026 Join this episode of DM Radio as host Eric Kavanagh speaks with Pinecone CEO Ash Ashutosh about the technical foundation behind modern AI systems.Explore how large language models convert text, images, and other data into vectors – mathematical representations that enable machines to understand meaning and relationships. Find out more…
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Connecting the Commerce Chain: Pricing, AI, and Enterprise Agility

Broadcast Date: March 9th, 2026 Join this episode of InsideAnalysis as host Eric Kavanagh speaks with Geoff Webb and Jason Smith from Conga about the growing complexity of managing pricing, quoting, contracts, and revenue operations. Find out more about findings from Conga’s global research on the challenges organizations face in connecting these critical processes. Learn…
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Resilient Data: From Ransomware to Revenue to Risk

Broadcast Date: March 5th, 2026 Join this episode of DM Radio as Eric Kavanagh interviews Eric Herzog of Infinidat as he explains why cyber storage resilience is critical as ransomware attacks surge. Learn more how real-time data cleansing protects sales performance and email deliverability with Jason Gladu of Convertr. They are joined by Matt DeLauro…
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$10 Million Questions: Do You Have the Right AI Infrastructure?

Broadcast Date: February 26th, 2026 The forcing function of Enterprise AI continues to shape boardroom discussions, and drive budgeting decisions. The demand for action is palpable. But aligning ideal AI Infrastructure with specific workloads is an acute challenge. Fine-tuning a model is different than inferencing; training looks nothing like RAG. What can your team do…
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