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Senior engineering attention is becoming one of the scarcer resources in data center operations. Hiring matters, of course. But adding people does not instantly reproduce years of site-specific knowledge. A recurring cooling issue is a good example. The organization may have solved something very similar before, yet the next investigation can still start with someone hunting through trends, reconstructing operating conditions and trying to understand why the previous team reached its conclusion. That is expensive engineering time spent rediscovering context. As portfolios grow, I think operators should look at engineering capacity more broadly: not only how many qualified people they have, but how effectively the knowledge already inside the organization can be applied again. That is one of the reasons I find Operational Intelligence so interesting. More on the argument here: https://lnkd.in/gakr9vHH

📍 etalytics will be in London for the DCD Global Awards 2025 We’re proud that our joint project with Equinix – applying AI-based cooling optimisation in a live data centre environment – has been shortlisted for this year’s Energy Impact category. 🏆 While in London, our Co-Founder Dr. Thomas Weber will be available for meetups with data centre teams in the area. If you’re attending the awards ceremony or you’re simply nearby, let’s grab a coffee ☕ and chat about: 🔹 AI-driven cooling control 🔹 Digital twin in live operations 🔹 Cutting cooling energy without hardware changes 🔹 Practical optimisation approaches for UK data centres Want to connect while we’re in London? Just drop us a message or reach out to Thomas directly: 👉 https://lnkd.in/eB-hasSj #DCDawards #DataCentres #CoolingOptimisation #DigitalTwin #EnergyEfficiency #London

What does AI optimization actually do inside a live data center? At NTT Global Data Centers’ Rhine-Ruhr 1 site, it delivered a 19.1% reduction in cooling energy while maintaining full operational reliability. ➤ Digital twin of the cooling system ➤ Real-time AI control ➤ Proven results This optimization approach is now available to operators in the US. Want to see how the pilot worked and why NTT is rolling it out to additional sites? 🔗 Read the success story.

NTT Global Data Centers reduced cooling energy by 19.1% using AI-driven optimization. A digital twin of the water-glycol system enabled real-time AI control with full safety fallback at their Rhine-Ruhr 1 site. This optimization approach is now available to operators in the US. No hardware changes needed. Want the full breakdown of the pilot project and results? 🔗 Read the case study.

A cooling plant doesn't operate at full load all the time. As demand changes, so does the most efficient operating point for pumps, chillers and cooling towers. Our case study looks at how AI-based optimization improved the operation of existing cooling equipment, without requiring construction measures.

At Merck KGaA, Darmstadt, Germany, electricity use for cooling was reduced by 21% in one optimized system segment during the first three months of autonomous operation. AI-based software coordinated the operation of existing chillers, pumps, and cooling towers. The project involved no construction work and did not interrupt ongoing production. Read how the optimization was implemented.