In this Employee Spotlight, Jonathan Rodriguez shares how #Edged uses data to help infrastructure scale faster, operate smarter and perform efficiently over time.
I believe in the Endeavour vision: building the next generation of more efficient, intelligent infrastructure. I’m a mechanical engineer by training whose master’s thesis was in cogeneration systems, so maximizing system efficiency has been a core value of how I think. Early exposure to digital twins showed me the power of combining time-series data with real-world context to gain real-time insight. That led me to pivot my career toward technology. Data centers are complex, interconnected systems where optimizing one layer often impacts another, and solving that multidimensional challenge is both intellectually rewarding and operationally critical. At Endeavour, we’ve had the opportunity to tackle these problems, and in doing so, it became clear there was an opportunity to formalize EdgedIQ, both to support Edged data centers and the larger Endeavour portfolio and to help other organizations make smarter, data-driven infrastructure decisions at scale.
At its core, the EdgedIQ team brings expertise in modeling infrastructure and asset portfolios, connecting that with real-time telemetry data, and then having pathways to plug that into existing organizational systems so that higher quality analytics and insights can be unlocked. Our team has been working in the digital twin space for over 15 years, integrating facility systems, equipment, and real-time telemetry so operators can understand what their assets are doing and how to use them more effectively. We think about this as MOMO—how do you Monitor, Operate, Manage and Optimize—across single sites and entire portfolios. Over time, we’ve expanded beyond mission-critical environments into infrastructure and real estate portfolios, leveraging technologies such as IoT to be cost effective. We’ve also stayed in tune with modern data architectures such as MQTT for interoperability, as well as knowledge graphs that set up tools like LLMs. The result is a strong data foundation that enables better decisions today and unlocks AI-driven insights tomorrow.
Everything starts with data-driven decision-making. Claims around efficiency, sustainability, or performance only matter if they’re grounded in data. Infrastructure systems naturally degrade over time as equipment ages, processes drift and utilization changes. And without continuous measurement and analysis, inefficiencies compound. EdgedIQ equips Edged with the data and context needed to understand how systems are actually performing, identify wasted or stranded capacity, and proactively optimize for maximum utilization, efficiency, and cost over time. It’s not about a one-time optimization; it’s about maintaining peak performance as conditions change.
There’s truth to the saying, “you can’t manage what you don’t measure.” Reducing energy use or emissions requires more than just raw telemetry, it requires context. In a given moment, what equipment is running? What are the environmental conditions? Where is power being sourced from at a given moment? EdgedIQ allows operators to analyze facilities from multiple perspectives and make informed decisions that reduce not just energy consumption, but real-world environmental impact. And when key metrics aren’t already being measured, like noise, air quality, or light pollution, we can always extend the existing digital model with IoT sensors, enabling transparent, data-backed claims about community impact.
All EdgedIQ capabilities ultimately center around building, contributing to, and accessing a shared digital twin; a single source of truth synchronized with real-world operations. Tools like the Edged Customer Portal, Operations Portal, MQTT PUSH, APIs, and reporting all interact with the same underlying data model, just viewed through different lenses depending on the user. That shared foundation enables better coordination between customers, vendors and operators, while still enforcing strict security boundaries so each party only sees what’s relevant to them. Leveraging a knowledge graph as one of the underlying technologies in the digital twin has made it easier to manage complex access requirements across customers, vendors and operating entities, allowing us to collaborate on a unified intelligence platform without compromising security or compliance.
We’re building AI capabilities and understanding on top of a strong foundation of data. As rack densities rise and cooling architectures become more granular, we need to model infrastructure at finer levels from individual racks to possibly servers and liquid cooling circuits, in order to extract insight and optimize this space. How do you quality control and organize all that data without increasing the complexity for users? That’s driving more automation and AI-assisted data management behind the scenes. At the same time, interoperability remains essential. No single platform can solve every challenge, so EdgedIQ is designed to make data easier to connect and share, allowing operators, customers, and partners to work from a common, trusted view and operate more efficiently together.
I’ve valued the intellectual freedom and trust that comes with working at Endeavour, especially the flexibility to balance work and family life. I have two kids who keep me grounded with their endless curiosity and awe when they discover how the world works; it’s a constant reminder to stay curious myself and appreciative of that perspective. That matters, especially knowing that the infrastructure decisions we make today will shape the world they grow up in. I enjoy exploring ideas and tinkering at home; that shows up in me reading leadership and engineering material, playing chess, experimenting with how to use AI, and staying hands-on, whether that’s house projects, learning new technologies, or even applying new cooking techniques on the grill. It helps keep the creative spirit going and my thinking sharp.
