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Nlyte Newsbytes - Issue 1

Published on July 7, 2026,

by Lindsay Hanna

In this issue:

  • AI: The Data Center’s Double-Edged Sword
  • How AI is Transforming Data Center Management
  • Fortifying Edge Data Centers
  • Calculating DCIM Return on Investment
  • Edge PUE Optimization for Energy Efficiency

AI: The Data Center’s Double-Edged Sword

A double-edged sword with the heading "The AI Paradox" and on each side of the sword the words "The AI-Fueled Crisis" and "AI as the Solution"

Artificial Intelligence is revolutionizing data center operations, but not without risk. This thought-provoking piece explores how AI can both optimize and complicate infrastructure management. Discover the opportunities and challenges IT Ops professionals must navigate in this evolving landscape.

Learn more: AI: The Data Center's Double-Edged Sword - AutomatedBuildings.com

How AI is Transforming Data Center Management

Why Nlyte Placement and Optimization AI? Move faster, reduce risk, and maximize the value of your data center infrastructure. Increase Agility Rapidly plan and model large-scale infrastructure changes in minutes, not weeks. Reduce Risk Predict resource shortfalls and conflicts before they happen, ensuring project success and service uptime. Improve Efficiency Maximize the use of your existing capacity to defer capital expenditures and lower operational costs. Enhance Decision-Making Shift from reactive problem-solving to a proactive, data-driven strategy for your data center’s future.

Smarter Infrastructure Starts Here Discover how Nlyte Placement and Optimization with AI is transforming data center management. From intelligent asset placement to predictive forecasting and automated validation, this solution helps IT Ops teams plan smarter, deploy faster, and reduce risk—turning complexity into clarity.

Learn more: AI Data Center Optimization with Nlyte Placement & Forecasting

Fortifying Edge Data Centers

Understanding the Attack Surface on Edge Data Centers The attack surface in an edge computing environment is vast and multifaceted. It extends far beyond the traditional network perimeter and encompasses every layer of the technology stack.

As edge computing expands, so does its vulnerability. This blog dives into the unique security challenges of decentralized environments and explores how Zero-Trust models and hardware-based controls can protect billions of connected devices. Learn how Nlyte’s Device Management platform helps secure the edge.

Learn more: Fortifying Edge Data Centers | Nlyte

Calculating DCIM Return on Investment

This eBook analyzes the lessons learned from the DCOI experience to establish a comprehensive framework for DCIM payback analysis. The central thesis is that DCOI proved that a DCIM investment is not a cost center but a value-generating initiative with a clear and compelling financial justification. The initiative taught us that by providing the automated monitoring, centralized data repository, and advanced analytics necessary to meet the mandate, DCIM creates a "single source of truth" that is foundational to modern IT operations. Based on a conservative financial model detailed herein—a model built on the lessons from DCOI—a typical mid-sized federal data center can expect to achieve a full payback on its DCIM investment within a period of 18 to 36 months.

The U.S. Federal Government’s Data Center Optimization Initiative (DCOI) served as a powerful catalyst for understanding the real-world value of Data Center Infrastructure Management (DCIM). More than just a compliance mandate, DCOI revealed how DCIM can deliver measurable returns, strategic advantages, and long-term modernization benefits.

This free eBook highlights the most important DCIM payback lessons from DCOI—and why they matter to your organization.

Learn more: DCIM Payback Lessons from DCOI Initiative

Edge PUE Optimization for Energy Efficiency

dge data centers are typically smaller, modular, and deployed in diverse environments. They often operate with dynamic workloads and lower utilization rates, which can skew PUE readings. For example, if the IT load is minimal but the cooling system is still running at full capacity, the PUE will appear artificially high—even if the system is operating efficiently for its size. To address this, many operators are turning to partial PUE (pPUE). This metric focuses on specific subsystems or modules within the facility, offering a more granular and actionable view of energy performance. By isolating the energy usage of individual components, pPUE helps identify inefficiencies that might be hidden in the overall PUE score.

Traditional energy metrics don’t always fit the edge. This blog explores how Power Usage Effectiveness (PUE) must evolve for modular, remote environments—and how strategies like right-sizing infrastructure and using high-efficiency components can drive smarter, greener edge operations.

Learn more: Edge PUE Optimization for Energy Efficiency | Nlyte

 

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