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April 20-23, 2026
Walter E. Washington Convention CenterWashington, D.C.

2026 Event Schedule

Edge to Insight: Unlocking the Full Value of Data in Your Digital Infrastructure

Arlen Nipper  (President & CTO, Cirrus Link)
Michael Geuss  (Principal DCIM Engineer, QTS)
Christopher Markstein  (Strategic Industries Manager, Data Center Market Lead, Inductive Automation)
Matt Sarcopski  (Vice President, Data Center Management, QTS Data Centers)
Jason King  (Director, Data Centers, RoviSys)
Location: 202B
Date: Thursday, April 23
Time: 10:15 am - 11:00 am
Pass Type: AFCOM Solution Provider, All Access Conference, Industry Conference, Standard Conference - Get your pass now!
Track: Emerging IT & Data Center Technologies
Session Type: Conference Session
Vault Recording: TBD
Audience Level: All Audiences

In modern data centers, critical OT systems generate unprecedented data volumes, but much of its value remains locked in disconnected silos or hampered by legacy architectures. Realizing the full potential of real-time insights, predictive analytics, and intelligent automation requires an evolution in how we approach digital infrastructure.

This panel brings together leading experts to explore the transformative power of modern data architectures in bridging the edge-to-cloud gap. We will deconstruct the core components of a future-proof data strategy, illuminating a clear pathway to unlock hidden value through a contextualized flow of information. The discussion will explore how foundational concepts like a Unified Namespace (UNS) create a single source of truth for all operational data.

Experts Arlen Nipper (Cirrus Link) and Travis Cox (Inductive Automation) will detail the pivotal role of MQTT/Sparkplug B in enabling efficient, metadata-rich communication from edge devices. To ground these concepts, panelists from a major data center operator will provide a real-world perspective, sharing their journey of collecting and contextualizing data to drive tangible benefits such as energy optimization. Finally, we will connect these layers to the ultimate goal of enabling advanced AI and machine learning with clean, contextualized data.