Why Knowledge Sharing Powers Data Center Optimization

Data centers now consume roughly 1-2% of global electricity—and that number is climbing fast. With AI workloads doubling energy demand and hyperscalers racing to build capacity, the pressure to do more with less has never been sharper. The industry knows it has a problem. What it hasn't fully solved is how to fix it at speed and scale without reinventing the wheel in every facility, organization, and sector.

The answer, increasingly, is deceptively simple: talk to each other.

Aveva's Arti Garg has been making exactly this case—that cross-sector knowledge sharing isn't a soft, feel-good idea but a hard operational necessity for anyone serious about data center optimization. She's right. The broader industry is only beginning to understand why.

What Data Center Optimization Actually Means

Optimization gets thrown around loosely. In practice, it means squeezing maximum useful compute from every kilowatt of power while minimizing waste—thermal waste, water waste, space waste, and capital waste. The metric most operators live by is Power Usage Effectiveness (PUE), where a perfect score of 1.0 means every watt goes straight to IT equipment. The global average still hovers around 1.5. Google's most efficient hyperscale facilities have pushed below 1.1. That gap represents enormous amounts of stranded energy—and money.

The difference between a 1.5 and a 1.1 PUE at a 100 MW campus isn't marginal—it's the difference between wasting 50 MW and wasting 10 MW. At average commercial electricity rates, that delta can easily exceed $20 million annually. Per facility.

But PUE is just one lens. Real optimization touches cooling architecture, server density, workload scheduling, renewable energy procurement, and increasingly, how AI itself is used to manage AI infrastructure. The technical complexity is real. So is the organizational challenge—data center operators are often siloed, protective of proprietary methods, and reluctant to share what's working for competitive reasons. That instinct is understandable. It's also slowing the industry down at exactly the wrong moment.

The Strategic Logic of Knowledge Sharing

Here's the non-obvious point most optimization conversations miss: the data center sector doesn't need to invent most of its solutions. Many already exist—in adjacent industries that have been running high-efficiency, thermally intensive, tightly monitored operations for decades.

Semiconductor fabrication plants manage precision thermal environments at a scale and consistency that most data centers haven't achieved. Chemical processing facilities have refined predictive maintenance to an art form. The aviation industry's approach to systems redundancy and failure mode analysis is more rigorous than anything most colocation operators have formalized. None of these sectors will hand over trade secrets—but the underlying principles, methodologies, and frameworks are transferable if people are willing to look.

This is the core of what Garg and teams like hers are pushing for: structured cross-sector learning as an operational discipline, not a conference panel afterthought. When a cooling engineer from a pharmaceutical cleanroom talks to a data center facilities manager about airflow dynamics, both come away with something they didn't have before. The pharmaceutical engineer understands redundancy pressures. The data center operator learns contamination control techniques that apply directly to raised floor environments.

The knowledge doesn't flow only from other industries into data centers, either. Data centers have developed some of the most sophisticated real-time infrastructure monitoring systems anywhere. That expertise is valuable to utility operators, manufacturing facilities, and smart building designers who are still catching up.

What Successful Sector Collaboration Actually Looks Like

Theory aside, where has cross-industry knowledge transfer produced measurable results in the data center space?

Microsoft's work on underwater data centers—Project Natick—borrowed heavily from submarine engineering. The insights around sealed environments, passive cooling through seawater immersion, and component longevity in controlled atmospheres came directly from defense contractors who build equipment designed to survive the ocean floor. The project demonstrated failure rates roughly eight times lower than land-based equivalents. That's not a coincidence. That's what happens when you pull expertise from a sector that has been solving adjacent problems under extreme constraints for fifty years.

Liquid cooling, which is rapidly moving from niche to mainstream as GPU-dense AI clusters push rack power densities past 40-50 kW, has benefited enormously from automotive and industrial cooling expertise. The companies getting liquid cooling right fastest are often those with engineering leadership that crossed over from automotive thermal management or industrial refrigeration—not people who grew up in the data center world.

On the energy side, the collaboration between data center operators and grid operators has produced demand response programs that benefit both parties. Data centers can modulate load during peak grid stress. Grid operators get flexible demand that helps balance renewables' intermittency. This didn't happen because it was obvious—it happened because someone in a room decided to explain how data center UPS systems and backup generators could function as a distributed grid resource. One conversation, years of mutual value.

Building Knowledge Sharing Into Operations, Not Just Strategy

The practical challenge is moving from "we should share knowledge" to "here's how we actually do it." A few approaches are proving effective.

Industry consortia like the Green Grid and Open Compute Project have created structured venues for sharing efficiency metrics and hardware specifications without exposing competitive differentiation. The Open Compute Project, founded by Facebook, has resulted in server designs that multiple operators now deploy—none of whom would have invested in open-sourcing that R&D individually. Collective action works when individual actors see the return.

Digital twin technology is accelerating this significantly. A digital twin of a data center—a real-time virtual model of the physical facility—can be used to simulate operational changes, test cooling modifications, and benchmark against industry standards without touching live infrastructure. When operators share anonymized digital twin data across a platform, the aggregate dataset becomes a learning resource far more powerful than any single organization's experience. Aveva, notably, is active in exactly this space—using operational data platforms to help industrial and infrastructure operators extract collective intelligence from distributed assets.

At the human level, structured secondments, joint working groups with utility partners, and partnerships with engineering schools are underused levers. The talent pipeline into data center engineering is narrow. Broadening it by pulling in people from energy, manufacturing, and civil infrastructure brings perspectives the industry is currently paying consultants to approximate.

Where This Leads

The data center industry is at an inflection point that is genuinely unusual in its intensity. The buildout required to support AI infrastructure over the next decade—some estimates put new global data center investment at over $1 trillion through 2030—is happening simultaneously with tightening sustainability mandates, grid capacity constraints, and water scarcity pressures in key development markets.

No single company or sector has all the answers. The operators who will navigate this best won't necessarily be the ones with the deepest internal R&D budgets. They'll be the ones who are best at finding, absorbing, and applying knowledge from wherever it lives—whether that's a semiconductor fab in Taiwan, a wind farm operator in Denmark, or a thermal storage researcher at a national laboratory.

Sustainable data centers aren't built in isolation. They're built by organizations willing to treat knowledge as an infrastructure asset—something to be invested in, maintained, and shared strategically. The technical solutions exist or are within reach. The bigger question is whether the industry's culture can move fast enough to use them.

That shift is already underway. The operators paying attention to it now will have a meaningful head start on everyone who waits for it to become obvious.

[INTERNAL LINK: data center optimization] [INTERNAL LINK: knowledge sharing in tech] [INTERNAL LINK: sustainable data centers]

**CALL TO ACTION:**

Ready to optimize your data center? Explore more insights and resources at InfraSale Marketplace.