Tech Deep Dive

Why Companies Spend Millions on Data (And Still Get No Value)

Max Clark is joined by Ed Bailey, Field CISO at Cribl, to discuss why many companies invest heavily in collecting data but struggle to turn it into meaningful outcomes. They explore common mistakes in data strategy, the gap between tools and skills, and what organizations should consider before collecting more data.What you’ll learn:Why companies are spending millions collecting data without clear outcomesThe gap between data collection and real decision-makingHow tools, skills, and strategy need to alignWhy collecting more data doesn’t automatically create valueQuestions leaders should ask before expanding their data footprintHow to rethink data strategy to support real business resultsGuest: Ed Bailey, Field CISO at CriblCribl Website: https://cribl.ioEd LinkedIn: https://www.linkedin.com/in/baileyedward/Cribl LinkedIn: https://www.linkedin.com/company/cribl/Cribl X: https://x.com/cribl_ioWebsite: https://cribl.io

From 400 Calls in Queue to Zero Hold Time

What happens when your contact center hits peak demand… and the queue explodes?In this episode, Max sits down with Michael Cibelli (PolyAI) to break down how one major brand went from 400+ callers waiting in queue to zero hold time—without wrecking customer experience.You’ll learn why traditional IVR trees (“Press 1 for…”) fail, what replaces them, and how modern Voice AI can contain calls end-to-end (not just “deflect” them). They also unpack the real-world risks—like AI hallucinations, compliance in regulated industries, and why context transfer matters more than most CX teams realize.What you’ll learn:Why IVR systems create customer rage (and broken experiences)The difference between call deflection vs call containmentHow Voice AI scales CX without scaling headcountHow to prevent “repeat yourself” transfers that destroy trustThe hidden cost of agent churn + seasonal hiringWhy BPOs are paying attention to Voice AI right nowHow analytics from customer calls can unlock real business ROIIf you’re leading CX, operations, IT, or customer support—this is the playbook for building a contact center that stays fast, consistent, and on-brand when demand spikes.🎧 Subscribe for more conversations on IT, CX, AI, and operational leverage.

Why Contact Center AI Projects Fail After the Demo

The demo was perfect. Then deployment happened. If your contact center AI “worked” in a pilot but fell apart in production - this episode explains why.Max Clark sits down with Jim K. Tennant (VP, Channel Sales at Observe.AI) to unpack the real reasons contact center AI projects fail after approval: weak data foundations, missing guardrails, messy knowledge sources, agent adoption breakdowns, and bots that can’t recover when customers go off-script. They also dig into what actually works—call analytics → automated QA → agent assist → targeted automation, and how to reduce churn and handle time without wrecking CSAT.Watch this before your AI pilot turns into an expensive failure.

The Azure Bill That Ended Cloud

What if the real problem isn’t Azure… it’s putting the wrong workloads there?In this episode, Max Clark sits down with William Sellers, Solutions Architect at DartPoints, to dissect a real-world migration from the energy sector: an internal research app that went into Azure “to move fast,” scaled to nearly $30K/month, and finally forced leadership to draw a line.You’ll hear how technical debt, predictable workloads, and opaque vCPU economics quietly turned a good idea into an unsustainable cloud bill—and what it actually took to unwind it.They break down:Why predictable, IaaS-style workloads get financially punished in hyperscale cloudHow finance, infrastructure, and application owners finally aligned around leaving AzureThe real definition of “minimal downtime” when you’re moving a 100+ TB VM and a Zerto bug derails your planHow DartPoints used private cloud to stabilize costs, simplify billing, and reduce complexityWhat VMware/Broadcom uncertainty really means for platform decisions—and why many teams now say, “we don’t care about the hypervisor; just make the VMs run.”If you’re responsible for infrastructure spend and you haven’t evaluated whether your workloads belong in Azure, you’re already behind.This episode gives you the decision framework, the warning signs, and the migration realities—so you don’t end up defending a runaway cloud bill with no exit plan.

DSPM vs DLP: The Data Blind Spots Killing Your Cloud Security

If you’re betting on “the cloud is secure” and a few DLP rules to save you, you’re exposed. Most breaches aren’t a tool failure, they’re a visibility failure. You can’t protect data you can’t see.In this episode, Max Clark sits down with Sirena Ong from Secuvvy to break down Data Security Posture Management (DSPM): what it is, how it differs from DLP, and why every CISO is suddenly waking up to data blind spots across SaaS, cloud, endpoints, and AI workloads.They dig into:Why “shared responsibility” and cloud-native tools still leave massive data visibility gapsThe real difference between DSPM (data at rest) and DLP (data in motion)—and why you need bothHow sensitive data quietly sprawls across SaaS, file shares, endpoints, Snowflake, and AI training pipelinesUsing DSPM to quantify risk, justify security budget, and avoid getting crushed on cyber insuranceWhat actually happens when you run DSPM in a 1,000-user environment for the first time (yes, you will find things you don’t like)If you own cloud, data, or security risk, this isn’t optional anymore.What you don’t know about your data will hurt you—get visibility before it becomes a board-level incident.