Data Center Operations

"How to Evaluate a Building for Data Center Conversion" Earlier this week I shared how Chicago developers turned a $12 million office building into a $40 million data center in 15 months. Today, let's talk about what to look for. The Five Critical Factors: 1. Power Infrastructure This is the dealbreaker. Can you increase capacity to 30-50 megawatts? Existing transformers? Proximity to substations? The Chicago building had substantial electrical infrastructure from its trading floor days. Without power capacity, you don't have a deal. 2. Building Structure You need: Wide, column-free floors High ceilings for cooling Floor load capacity for server weight Cavernous layouts The Cboe building was designed for trading floors—which converts perfectly to data centers. 3. Existing Connectivity "This building is very heavily wired from its time as a trading platform," said buyer Daniel English. Look for heavy wiring, fiber proximity, and urban locations near connectivity hubs. 4. Cooling Potential CRE Daily reports liquid cooling is becoming standard as power densities jump from 120 kW per rack today to 600 kW by 2027. Can the building support liquid cooling systems and upgraded HVAC? 5. Urban Location Advantage English explained why urban conversions command premiums: "Just like Amazon last-mile delivery, data centers take less time to deliver when they're close." Low-latency applications—trading, streaming, gaming—pay premiums for urban proximity. The Best Candidates: Former trading floors, financial services buildings, telecom facilities, heavy industrial with power infrastructure. My Take: The Chicago flip proves it: The biggest returns aren't in greenfield development. They're in buying assets where someone else already solved the hard problems and the market hasn't caught up. What building in your market has these five factors? Because while everyone else sees obsolete real estate, you might be looking at a 233% return in 15 months. What are you seeing that others are missing? Sources: "Flip of former Cboe Global Markets headquarters in Chicago shows soaring data storage values" by Ryan Ori, CoStar News, October 23, 2025; "Data Centers Driving Growth In AI And Real Estate" CRE Daily, PrincipalAM research

Increasing both the capacity and number of data centers is fundamental to the growth of AI, but they have become a lightning rod for criticism from local residents and politicians alike as they are causing higher energy prices and using scarce water resources in a growing number of regions globally. On a recent episode of the Bloomberg Switched on Podcast, Tom Rowlands-Rees and Lloyd Arnold, BloombergNEF's Global Power Analyst and Data Center Analyst, respectively, discussed “What Really Determines Where Data Centers Get Built”. The decision about where to site data centers is becoming more complex, with decision makers having to factor in energy & water availability and cost, as well as land permitting. However, other criteria are becoming more important, including taxes, fiber connectivity, and existing ecosystems, which are impacting competitiveness, given that tech companies remain focused on sustainability and net zero initiatives. Key takeaways from the podcast include: · Power constraints are now the biggest bottleneck - Many regions face grid congestion, long interconnection queues, and rising competition for electricity from AI, electrification, and industrial loads. Access to reliable, low‑carbon power is becoming a decisive factor in site selection. · Regional competitiveness is diverging - Markets with streamlined permitting, strong renewable‑energy pipelines, and supportive policy frameworks are pulling ahead. Others are struggling with regulatory complexity, land scarcity, or slow infrastructure build‑out. · Construction timelines are lengthening - Supply‑chain pressures, skilled‑labor shortages, and stricter environmental reviews are extending development cycles. Speed to market is becoming a differentiator — and a challenge. · Geopolitics and resilience matter more than ever - Operators are diversifying locations to reduce exposure to geopolitical risk, extreme weather, and single‑grid dependency. Redundancy is becoming a strategic asset. · Permitting and land availability remain major hurdles in dense metros, pushing operators toward secondary markets. · AI workloads are reshaping design, driving higher rack densities[JB1] , new cooling strategies, and unprecedented energy demand forecasts. · Sustainability pressures are rising, with operators expected to prove real emissions reductions, not just offsets. Data center growth will continue, although some regions will be slower due to the challenges mentioned above. However, with so much capital being invested into the AI sector, we should expect that data center hyperscalers will be willing to overpay for the power and water needed to start the permitting and building process.      Listen on Apple Podcasts: https://lnkd.in/gSw5GwKM #ai; #datacenters; #hyperscalers; #renewableenergy; EcoTech Capital Cy Obert Jeffrey Lipton

We're actively negotiating on several data center sites—200MW+ greenfield and brownfield projects, a site ready for new construction, and multiple edge portfolios. The due diligence has been a masterclass. Data center investing isn't real estate anymore. It's a hybrid of real estate, energy, and technology. And if you're a lifelong learner, this is one of the most fascinating spaces to be in right now. -The Basics Everyone Knows- You need land. You need power, fiber, and water. That's the easy part. What qualifies that land is where it gets complex. -Power- It's not just "do you have power?" It's: • What type of power—grid, renewable, on-site generation? • How many redundant sources are available? • What's the timeline to energize—months or years? • What's the cost per MW, and how does it escalate? • Can you secure a PPA that makes the economics work? -Fiber- • How many points of connectivity do you have? • How close is your nearest point of presence (POP)? • Are there dark fiber lines available to light up? • What's the latency to major cloud on-ramps? -Water- • What's the source—municipal, well, reclaimed? • Is it sustainable at scale? • Will local stakeholders oppose the usage? • What are the cooling alternatives if water becomes constrained? Each of these has layers underneath. And that's before you get into the technical due diligence. -Where It Gets Interesting- Once you understand the fundamentals, a whole new world opens up. Alternative power sources—nuclear, SMRs, behind-the-meter solar. Equipment sourcing and lead times for transformers and switchgear. Understanding the energy draw from AI workloads versus traditional compute. The difference between training clusters and inference at the edge. Then there's the structuring: PPAs, offtake agreements, utility negotiations, local stakeholder alignment, tax incentives, and creative financing that makes projects pencil. -Why This Matters- The operators who win in this space won't just be real estate people. They'll be the ones who understand energy markets, technology roadmaps, and how to structure deals that work for utilities, communities, and capital partners simultaneously. Data centers reward curiosity. The more you learn, the more creative you can get—and the better the opportunities you can unlock. We're deep in this right now. It's been one of the steepest learning curves we've taken on, and one of the most rewarding. Who else is navigating the complexity of data center development?

An engineer on my team asked me a question last week that made me stop and think.    "At what point does the industry run out of sites that make sense for large-scale datacenters?"    It is a better question than it sounds.    A viable gigawatt-scale datacenter site needs abundant power, affordable power, water access, fiber connectivity, reasonable land costs, favorable permitting, and a labor market deep enough to support construction and operations.    The number of locations where all of those factors converge is not unlimited. And the best sites are being claimed faster than new ones are being developed.    I am watching developers accept compromises on one or more of these factors because the ideal sites are no longer available. Building in locations with constrained water. Accepting power contracts with higher rates. Choosing sites where the labor market is thin.    Each compromise has a mechanical engineering consequence. Constrained water changes your cooling strategy. Higher power costs make efficiency more critical. Thin labor markets extend construction timelines.    The next wave of datacenter development will be harder than this one. Not because the technology is more complex. Because the easy sites are taken.    What compromises is your next site requiring? And have you modeled the mechanical engineering impact of each one? 

𝙋𝙤𝙬𝙚𝙧 𝙝𝙖𝙨 𝙧𝙚𝙥𝙡𝙖𝙘𝙚𝙙 𝙛𝙞𝙗𝙚𝙧 𝙖𝙣𝙙 𝙢𝙚𝙩𝙧𝙤 𝙥𝙧𝙤𝙭𝙞𝙢𝙞𝙩𝙮 𝙖𝙨 𝙩𝙝𝙚 #𝟭 𝙨𝙞𝙩𝙚 𝙨𝙚𝙡𝙚𝙘𝙩𝙞𝙤𝙣 𝙙𝙧𝙞𝙫𝙚𝙧. Most teams have not adjusted. For two decades, site selection started with fiber routes, latency, and incentives. Power was a check box. In 2026, power is the constraint. It dictates timeline, cost, and viability. Everything else is secondary. JLL is projecting average global build cost at $11.3M per MW for 2026, up from $7.7M in 2020. That delta is not general inflation. It is interconnection scarcity showing up in capex. What is actually happening in the field right now: • Sites with existing substations near retiring coal or industrial loads are commanding premiums. You are buying time, not just land. • Behind-the-meter natural gas is no longer a temporary bridge. It is the primary path when grid timelines exceed 3 to 5 years. • Operators willing to run hybrid power strategies are beating “wait-for-grid” models on speed to market. That gap is widening. If your deck still leads with fiber maps, you are optimizing the wrong variable. Lead with megawatts, queue position, and time-to-energize. Then design network, cooling, and tax strategy around that reality. Board-level translation: Power access is now the gating factor for revenue realization. Miss it and nothing else matters. What is the first question your team asks on a new site today: power or latency? #DataCenters #AIInfrastructure #SiteSelection #RoyaleStakes

Most people are talking about data centers. Our team at Midwest CRE Advisors is actually working them. There are two ways I'm engaged in the Kansas City data center market right now and they couldn't be more different. 1️⃣ Greenfield Sites & the Power Problem Finding raw land for a data center in KC isn't the hard part. The hard part is the utility conversation. How close are you to a substation? What's the available load? What does the interconnection timeline look like with Evergy? Can the site support 20 MW, 50 MW, 75 MW+? Kansas City recently rezoned data centers as industrial facilities, which matters for site selection. But even with the right zoning, the wrong power situation kills a deal before it starts. I'm working with landowners right now who have sites that look ideal on paper, highway access, industrial zoning, right-sized acreage, and my job is to run the utility scenario before anyone wastes time or money. Evergy's pipeline is already over 15 GW in active agreements. The sites that can plug in fast are worth a premium. The ones that can't? You better know that before you buy. 2️⃣ Conversions & Modular Not every data center gets built from the ground up. I'm also engaged on the conversion side, existing industrial and flex buildings that have the bones to become edge or modular data center deployments. Floor load. Clear height. Power access. Fiber proximity. Cooling options. The modular data center market is projected to grow at 17.7% CAGR and a lot of that growth isn't in brand-new hyperscale campuses. It's in adaptive reuse and modular deployments that can be stood up faster, closer to the edge, at lower capital cost. Kansas City's central geography, affordable power rates, and available industrial inventory make it one of the better-positioned markets in the country for this type of deployment. If you have land, a building, or capital that belongs in this conversation, reach out. This market is moving fast and the window for early positioning is closing. 📍 Kansas City

Every billion-dollar data center begins with dirt. The land itself determines the limits of power, scale, and speed. Yet the best parcels today aren’t the cheapest they’re the ones wired for megawatts and milliseconds. A few things define who wins: • Power access: Sites within one mile of substations or transmission corridors. • Fiber connectivity: Low-latency paths to major network exchanges. • Policy alignment: Fast permitting, tax incentives, and community acceptance. Developers are now buying powered land years before construction starts. Others are co-locating next to generation hydro, gas, or nuclear to bypass grid congestion entirely. The model is shifting from single builds to multi-phase “AI corridors,” where power, fiber, and zoning are secured across entire regions. Microsoft’s 2024 Malaysia site captured this logic: a parcel beside a 500MW power plant, tied to new fiber routes, backed by state-level incentives. Data centers are no longer about square footage. They’re about control of electrons, connectivity, and permits. Whoever secures those first defines the next decade of digital infrastructure. Read the article below #datacenters

📊 Data Center Operations – KPI & SLA Framework we should follow below as per my Experience 🔹 Key Performance Indicators (KPIs) – Measuring Operations KPIs track how well the data center is performing. They ensure efficiency, cost optimization, and service quality. ✅ Examples of KPIs in Data Center Operations: 1. Uptime Percentage – % of IT load availability (e.g., 99.982% for Tier III). 2. Power Usage Effectiveness (PUE) – Ratio of total facility power to IT power (target: ≤1.5). 3. Cooling Efficiency (kW/ton or CFM per kW) – Optimized HVAC utilization. 4. Mean Time to Repair (MTTR) – Average time to fix an issue. 5. Response & Resolution Time – Time taken to respond and resolve incidents. 6. Incident Recurrence Rate – Frequency of repeated failures. 7. Energy Consumption per Rack – kWh consumption per IT rack. 8. Water Usage Effectiveness (WUE) – Water efficiency in cooling. 9. Occupancy/Space Utilization – White space utilization efficiency. 10. Maintenance Cost per kW – Cost efficiency of operations. 11. Safety Compliance Score – % of adherence to safety drills, PPE use, and audits. 12. Security Incident Rate – % of security breaches, unauthorized access attempts. 13. Training Hours per Employee – Continuous upskilling and safety preparedness. 14. Carbon Footprint Reduction – Sustainability and green energy adoption. 📌 Good Practice KPI: Customer Satisfaction Index (CSI/NPS) – Feedback from clients on reliability, communication, and support. 🔹 Service Level Agreements (SLAs) – Ensuring Accountability SLAs define the “what” – the commitments made to clients for service quality. ✅ Examples of SLA Commitments in Data Centers: 1. Guaranteed Uptime – e.g., 99.982% for Tier III, 99.995% for Tier IV. 2. Response Time – Max 15–30 min response for critical incidents. 3. Resolution Time – Critical issues resolved within defined hours (e.g., 4 hours). 4. Power Redundancy – N+1 or 2N availability for UPS/DG/transformer. 5. Cooling Availability – Continuous cooling with redundancy. 6. Service Availability – Uninterrupted facility operations. 7. Security Standards – 24x7 surveillance, biometric access, zero breach tolerance. 8. Safety Standards – Adherence to fire safety NOCs, EHS guidelines, evacuation drills. 9. Maintenance Standards – Defined SOPs, frequency of preventive maintenance, and compliance. 🔹 Additional Best Practices in Data Center Operations 1. Safety First:100% PPE compliance.Regular fire & evacuation drills.Zero accident target (TRIR). 2. Security & Access Control:Biometric + CCTV monitoring.Access based on role (least privilege).Incident logs for every security breach attempt. 3. Operational Excellence:Regular SOP/MOP/EOP drills.Root Cause Analysis (RCA) for all . Sustainability Practices:Renewable energy adoption.Hot/Cold aisle containment for cooling efficiency.Recycling & green building standards (LEED/IGBC). Formula: KPI + SLA + Safety + Security = World-Class Data Center Operations 🚀

I’ve watched too many databases choke on SLAs. So we decided to build a system where speed and scale finally coexist. If you have a fixed SLA of 50–100 milliseconds, most databases let you read only a little before the clock runs out. This leads to less data, less processing time, and weaker results. Aerospike flips that. We keep the index in memory and the data on fast SSDs, so every lookup is just one quick hop, even at massive scale. In the same SLA, you can read more data, faster, and give your algorithms more time to work. Fraud scores become sharper, risk analysis becomes richer, and recommendation engines become smarter. These predictive AI workloads have been running in production for over a decade on Aerospike. And in many cases, the competitive edge is measured in orders of magnitude. This is why companies like PayPal and many e-commerce leaders use Aerospike for fraud detection, recommendations, and risk analysis at global scale. In AI, the edge isn’t just speed. It’s giving your models the breathing room to be smarter… without breaking the SLA.

We were managing over 30 SAP systems across 3 continents with a team of just 7 engineers. The SLA was 99.9%. That sounds impressive—until you run the numbers. At 99.9%, you’re allowed just 43 minutes of downtime per month. Across 30+ systems, that means every second counts. And every mistake multiplies. In that kind of environment, manual recovery isn’t just inefficient—it’s unacceptable. There’s no time to wait for someone to notice a stuck job. No time to escalate. No time to log into four consoles to restart something by hand. We didn’t have a choice. We had to automate, not as a strategy, but as a survival mechanism. We wrote scripts. Built logic into workflows. Automated our monitoring. Codified our processes. That’s how we stayed ahead—not by scaling our team, but by scaling our capability. Those lessons from OZSOFT CONSULTING CORP. directly shaped what later became IT-Conductor. And today, when I talk to MSPs struggling with margin pressure, rising SLAs, and team burnout… I always come back to this: Automation isn’t optional when expectations are this high. Not because it's a trend. Because it's the only thing that gives you time back at scale.