“Canada does not need to fear data centres. Canada needs to understand them, engineer them properly, power them responsibly, cool them intelligently, and recognize that digital infrastructure is now as essential to the modern economy as roads, railways, substations, and water systems.” – MJ Martin
Introduction
Data centres have become one of the most misunderstood forms of modern infrastructure. In Canada, the public discussion around data centres and artificial intelligence has become increasingly emotional, with many claims based on incomplete information, exaggerated assumptions, or social media mythology. As someone who looks at these facilities from an engineering and operating perspective, the first point is simple: not all data centres are the same. They do not have the same power profile, water demand, staffing model, network design, cooling strategy, or community impact.
The term “data centre” is often used as if it describes one standard building. In reality, there are several distinct classes of facilities, each designed for a different technical and business purpose. The five most common types are edge data centres, enterprise data centres, colocation data centres, hyperscale data centres, and specialized AI data centres.
Edge Data Centres
Edge data centres are small, distributed facilities located close to users, sensors, industrial systems, telecom networks, or critical field operations. Their purpose is to reduce latency and process data closer to where it is created. In Canada, edge facilities are especially relevant for utilities, transportation corridors, remote communities, mining operations, ports, and telecom networks.
Their power requirements are modest compared with hyperscale or AI facilities, but reliability remains critical. They may be installed in small buildings, modular enclosures, telecom shelters, or industrial sites. Water requirements are usually low because many edge systems use direct expansion cooling, air cooling, or compact closed-loop cooling systems. Staffing is usually “lights out,” meaning there may be no permanent onsite personnel. Technicians visit only for maintenance, replacement, inspection, or emergency response. Network connectivity is essential, often involving fibre, wireless backhaul, carrier Ethernet, or redundant telecom paths.
Enterprise Data Centres
Enterprise data centres are owned and operated by a specific organization for its own internal use. Banks, utilities, universities, hospitals, insurers, and government agencies have historically used this model. These facilities support business applications, databases, records, cybersecurity systems, and operational workloads.
Power requirements vary widely, but enterprise sites are usually moderate in scale. They require high availability because they support internal business continuity. Cooling is often based on chilled water, computer room air conditioning, or in-row cooling. Water demand depends on the cooling design, but many Canadian enterprise facilities are moving toward more efficient closed-loop systems. Staffing may include onsite operators during business hours, with remote monitoring after hours. These sites are usually designed around serviceability, with access controls, spare capacity, backup generators, UPS systems, and maintenance contracts.
Collocation Data Centres
Collocation data centres are shared facilities where multiple customers lease space, power, cooling, and network access. A collocation provider supplies the building, security, power infrastructure, cooling systems, and carrier connectivity. Customers install and manage their own servers, storage, and network equipment.
This model is attractive in Canada because many organizations want professional-grade resilience without building their own facility. Power requirements are higher than typical enterprise sites because many tenants are concentrated in one facility. Water requirements depend on the cooling technology, but modern colocation providers are under pressure to reduce water consumption and improve energy efficiency. Space requirements are organized by cabinets, cages, suites, or dedicated halls. Staffing is usually a hybrid model. The facility operator maintains the building and infrastructure, while customer technicians or remote hands support customer equipment. Network connectivity is one of the strongest value propositions, since collocation sites often provide access to multiple carriers, cloud on-ramps, internet exchanges, and private networks.
Hyperscale Data Centres
Hyperscale data centres are massive facilities designed for cloud platforms, search engines, streaming services, social media, enterprise cloud computing, and large-scale digital services. These sites are built for scale, automation, and extreme operational efficiency. In Canada, hyperscale investment is influenced by access to reliable electricity, fibre routes, land, climate, tax policy, and proximity to major markets.
Power requirements are significant, often measured in tens or hundreds of megawatts. These sites require strong grid planning, substation capacity, transmission access, and long-term utility coordination. Water requirements vary. Some hyperscale sites use evaporative cooling, while others use air-side economization, closed-loop liquid systems, or advanced cooling designs that reduce water use. Canadian climate can be an advantage because cooler ambient temperatures can improve cooling efficiency for much of the year. Staffing is typically limited relative to the size of the facility because automation is extensive. These are often highly secure, highly standardized, lights-out or low-touch environments with strong remote monitoring, predictive maintenance, and disciplined service procedures.
Specialized AI Data Centres
Specialized AI data centres are the newest and most controversial category. They are designed for high-density computing using GPUs, AI accelerators, specialized networking, and large-scale model training or inference. These facilities are not simply larger versions of traditional data centres. Their rack densities are much higher, their cooling challenges are more intense, and their internal networking requirements are far more demanding.
Power requirements are substantial because AI processors consume far more electricity than conventional enterprise servers. Cooling requirements are also more complex. Many AI facilities are moving toward direct liquid cooling, rear-door heat exchangers, immersion systems, or hybrid cooling architectures. Water use depends heavily on the selected cooling system and the source of heat rejection. Space requirements are not always about building size alone. The key issue is power density per rack, floor loading, cable management, maintenance access, and mechanical plant capacity. Staffing models are specialized. These sites may operate lights out for routine computing but still require highly skilled electrical, mechanical, network, and hardware technicians for serviceability.
Availability, Reliability, and Serviceability
Across all five types, the professional data centre discipline is built around availability, reliability, and serviceability. Availability means the service remains operational. Reliability means the systems perform consistently over time. Serviceability means components can be maintained, replaced, upgraded, and repaired without unnecessary disruption.
The engineering fundamentals are serious. Power systems require utility feeds, transformers, switchgear, UPS equipment, generators, transfer systems, grounding, monitoring, and protection. Cooling systems require redundancy, airflow management, filtration, heat rejection, and controls. Networks require diverse paths, carrier redundancy, routing discipline, cybersecurity, and physical security. Staffing requires procedures, training, access control, incident response, and maintenance planning.
Summary
The public debate about data centres needs more precision. An edge data centre serving a Canadian utility is not the same as an enterprise facility in a hospital, a collocation centre in Toronto, a hyperscale cloud campus, or a specialized AI training facility. Each has a different purpose, footprint, power requirement, water profile, cooling method, staffing model, network dependency, and reliability target.
Canada should not evaluate data centres through fear or mythology. It should evaluate them through engineering, economics, grid capacity, environmental performance, and public value. Data centres are now part of critical infrastructure. The right question is not whether they should exist. The right question is how they should be designed, powered, cooled, regulated, and integrated responsibly into Canadian communities.
About the Author:
Michael Martin is the Vice President of Technology with Metercor Inc., a Smart Meter, IoT, and Smart City systems integrator based in Canada. He has more than 40 years of experience in systems design for applications that use broadband networks, optical fibre, wireless, and digital communications technologies. He is a business and technology consultant. He was a senior executive consultant for 15 years with IBM, where he worked in the GBS Global Center of Competency for Energy and Utilities and the GTS Global Center of Excellence for Energy and Utilities. He is a founding partner and President of MICAN Communications and before that was President of Comlink Systems Limited and Ensat Broadcast Services, Inc., both divisions of Cygnal Technologies Corporation (CYN: TSX).
Martin served on the Board of Directors for TeraGo Inc (TGO: TSX) and on the Board of Directors for Avante Logixx Inc. (XX: TSX.V). He has served as a Member, SCC ISO-IEC JTC 1/SC-41 – Internet of Things and related technologies, ISO – International Organization for Standardization, and as a member of the NIST SP 500-325 Fog Computing Conceptual Model, National Institute of Standards and Technology. He served on the Board of Governors of the University of Ontario Institute of Technology (UOIT) [now Ontario Tech University] and on the Board of Advisers of five different Colleges in Ontario – Centennial College, Humber College, George Brown College, Durham College, Ryerson Polytechnic University [now Toronto Metropolitan University]. For 16 years he served on the Board of the Society of Motion Picture and Television Engineers (SMPTE), Toronto Section.
He holds three master’s degrees – in business (MBA), communication (MA), and education (MEd). As well, he has three undergraduate diplomas and seven major certifications in business, computer programming, internetworking, project management, media, photography, and communication technology. He has completed over 80 next generation MOOC (Massive Open Online Courses) [aka Micro Learning] continuous education programs in a wide variety of topics, including: Economics, Python Programming, Internet of Things, Cloud, Artificial Intelligence and Cognitive systems, Blockchain, Agile, Power BI, Big Data, Design Thinking, Security, Indigenous Canada awareness, and more.
Martin in a volunteer, a photographer, a learner, a technologist, a philosophizer, and a romantic optimist.