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05 January 2026 Digital twins will rewrite the design rules for the data center Sergey SamchukAVP, Technology
Discover the benefits of IT/OT convergence, plus our roadmap for achieving it in your own data center operations.
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Long story short Digital twins are more than a simulation tool. They’re a strategic asset that can fundamentally reshape how data centers are designed, tested, and optimized. Forward-thinking operators are already using them to de-risk critical decisions and accelerate innovation, with the vast majority of technology leaders in major corporations actively pursuing digital twin initiatives (McKinsey). However, only 42% of decision-makers in data centers are currently using digital twins, with many still perceiving them as internal tools. Why it matters In a sector where downtime can cost millions and infrastructure decisions have decade-long consequences, the ability to test, learn, and iterate in a risk-free digital environment isn’t just valuable, it’s transformative. The complete picture The data center industry is facing a perfect storm of complexity. Rapid growth in AI workloads, stringent sustainability requirements, and unprecedented infrastructure investment are all converging. Traditional approaches to planning and testing – spreadsheets, static models, and gut instinct – simply can’t keep up. Enter digital twins: precise virtual replicas of physical data centers that enable operators to simulate scenarios, test configurations, and optimize performance before committing resources to real-world deployment. Digital twins aren’t new. Manufacturing, aerospace, and energy sectors have been using them for years. But their application to data centers represents a significant leap forward in how the industry approaches design, operations, and resilience planning. By creating a comprehensive digital replica – from server racks to HVAC systems to power distribution – operators can run “what-if” scenarios that would be impossible or prohibitively expensive to test in production. The value proposition is clear: reduce risk, accelerate time-to-market, and optimize resource allocation. But the devil is in the details. Building an effective digital twin requires more than just 3D modeling software. It demands deep integration across IT and OT systems, sophisticated data analytics, and the right talent to interpret results and translate them into actionable insights. AI initiatives face similar hurdles. Just 25% of AI implementations have achieved expected ROI, likely hindered by poor system integration and ineffective strategies for data management. “The devil is in the details. Building an effective digital twin requires more than just 3D modeling software.” Breaking down barriers between IT and OT One of the most transformative capabilities of digital twins is their ability to expose hidden interdependencies between IT and OT systems. In traditional data center planning, these systems are often designed and managed in silos. IT teams focus on compute, storage, and networking. OT teams handle physical infrastructure – power, cooling, and building management. This separation creates blind spots that can lead to catastrophic failures under stress. “One of the most transformative capabilities of digital twins is their ability to expose hidden interdependencies between IT and OT systems.” Let’s look at an example. Imagine that a data center provider simulates a regional power grid failure. Primary power is cut, backup generators take over seamlessly, and failover scripts reroute workloads to a secondary facility. HVAC and building management systems (BMS), which are not integrated with IT systems, continue running in standard operating mode. The HVAC system, functioning separately to the simulated power outage and increased IT load, does not adjust cooling to match the spike in server activity. In a real-life scenario, this could result in server aisles nearing thermal shutdown. When systems are not “in dialogue” with one another, and the physical interdependencies between IT and OT systems are not well understood, operators end up with a false sense of readiness. Another issue is that the industry currently lacks the talent to deploy digital twin technologies. 58% of operators in the UK currently struggle to fill critical roles, whilst 40% state that retaining their core team is particularly difficult. This revolving door of talent can make upskilling a challenge and, as things stand, there aren’t enough experts who understand how to direct simulations. At Hitachi, we are tackling the skills gap by providing data center talent with global and portable certifications in a wide range of areas, from data science to artificial intelligence to cybersecurity.
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Case study Lumada’s Digital Innovation Platform Hitachi’s Lumada platform demonstrates how digital twins work in complex operational environments. An entire factory can be replicated using Hitachi IoT technology, with the Digital Innovation Platform collecting, refining, and storing vast amounts of data from production sites.
This same approach applies to data centers. Just as manufacturers test production line changes in their digital twin before implementing them on the factory floor, data center operators can simulate cooling adjustments, power reconfigurations, or workload migrations in their digital twin before touching production infrastructure. The ability to test failure scenarios such as power outages, cooling failures and equipment malfunctions enables operators to check if their disaster recovery procedures actually work and identify gaps before they become real-world crises. Another area where digital twins are already making a difference is in defining and delivering more precise Service Level Agreements (SLAs), especially as the industry shifts towards software-as-a-service models. For example, digital twin technologies can be utilized during co-creation processes with end customers, to guarantee that a solution designed by the service provider is in line with expectations. Digital twins have key operational and commercial functions within data centers, which should not be overlooked. As operators look to scale with AI-driven compute demand, these tools have a key role to play in helping deliver performance in line with this ambitious growth.
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Own the conversation Ask the big question: If we can simulate everything before we build it, why do data centers still fail? What’s the gap between theory and practice that even the most sophisticated digital twins can’t bridge? Disrupt your feed: The rush to deploy AI infrastructure is happening faster than our ability to model its long-term implications. Are we building digital twins complex enough to capture what we don’t yet understand? Drop this fact: According to McKinsey, the implementation of digital twins can accelerate the creation and setup of new projects, including AI-enabled features, by up to 60% and cut costs by around 15%.
