Nothing slows down a modern data center faster than building it without an operational plan. Yet, across digital infrastructure today, the relentless race for AI capacity has made speed to market the single most dominant metric.

While rushing to construct greenfield sites and retrofit brownfield facilities gets power online fast, ignoring how a site will run over its 15-to-20-year lifespan creates severe long-term bottlenecks.

Speaking to DCD on managing AI infrastructure at scale, Supratim Mukhopadhyay, vice president of new markets for Asia Pacific at Octave – a software spin-off from Hexagon AB with roots tracing back over 50 years – warns that data center operators risk repeating the very same mistakes previously made by other capital-intensive industries.

“We’ve seen something similar in heavy industries like oil and gas, where there was a race to build refineries. If you aren’t thinking about the full lifecycle of that asset once constructed, it will cause issues for operators down the line,” Mukhopadhyay warns.

“It is critical that operators leverage the current influx of capital project investment to start planning how they will maintain and operate these facilities for the next 10, 15, or 20 years in an optimum way.”

Bridging the digital information gap

One of the greatest operational barriers currently facing APAC data center operators is the structural information gap between design, construction, and ongoing operations. Short-term delivery pressures often cause design and build phases to be treated as silos, leaving operations teams to inherit incomplete documentation or disparate spreadsheets upon handover.

To prevent these disconnects, operators need to adopt a reverse-engineering mindset during the early stages of capital projects. By starting with end-state operational requirements, engineering teams can specify exact contractor handover deliverables, defining how data and BIM (Building Information Modeling) models must be structured to support ongoing facilities management.

This structural alignment becomes especially critical when managing mixed regional portfolios across ANZ and SEA. Greenfield projects present an ideal opportunity to build with digital management in mind from day one, establishing organizational standards across design, construction, operations, and asset protection.

Conversely, brownfield sites often lack historical documentation. However, by establishing digital baseline standards on greenfield builds, operators can work backward to capture missing brownfield data after the fact, using modern enterprise tools to bring older sites up to operational parity.

Mukhopadhyay adds: “Start from the end and define your construction parameters, engineering requirements, and handover expectations based on what you will actually need to run the facility.

“Lifecycle intelligence is fundamentally about securing the right information at each stage of the asset lifecycle and retaining it seamlessly for long-term operations.”

Standardizing portfolio scaling

As operators scale their footprints across multiple regional markets – balancing primary hubs with fast-emerging Tier 2 markets – operational complexity grows exponentially. Managing data centers as isolated facilities creates fragmented workflows, clunky data collection, and immense difficulty when enforcing custom tenant SLAs across mixed-tenancy data halls.

Implementing enterprise-level asset management software allows operators to maintain portfolio-wide governance while managing individual facilities or specific client footprint levels independently. By specifying structured data requirements from general contractors upfront and leveraging AI tools to convert unstructured legacy information into standardized formats, operators can ingest new facilities into existing management software seamlessly.

“Scalability becomes far less of a challenge because bringing a newly acquired or constructed data center online simply means placing it onto an existing platform with pre-built standards. Trying to operate individual assets without those underlying standards leaves teams constantly struggling to locate basic operational data,” notes Mukhopadhyay.

A standardized platform also directly addresses another of APAC’s most acute operational hurdles: severe engineering workforce shortages. Complex software with long onboarding curves exacerbates talent bottlenecks. Standardized enterprise platforms allow operators to create structured training pipelines that can bring interns and junior technicians up to speed within three weeks, enabling rapid workforce progression without compromising site integrity.

Mukhopadhyay says: “If you spend three months just training new personnel, your overall speed to capacity and speed to value are severely impacted. Having a single platform that offers a standardized way of operating and supporting your workforce is critical.”

Shifting from reactive fixes to predictive optimization

Maintaining long-term operational resilience requires balancing strict standardization with operational agility. Operators do not need to lock themselves into rigid technology setups; rather, they can adopt a phased maturity matrix. In doing so, they need to move progressively from basic maintenance frameworks to advanced asset management practices as their operations stabilize.

While building management systems (BMS) and DCIM tools monitor immediate environmental conditions, true operational resilience relies on connecting real-time telemetry back into enterprise asset management platforms.

By integrating BMS sensors directly with asset software, operators can track asset health trends, such as subtle coolant temperature drifts or unusual pipe vibrations, automatically triggering work orders before minor anomalies turn into catastrophic outages.

“It is no longer just about preventing failures; it is about building an operational discipline where you systematically eradicate failures by moving to condition-based, predictive maintenance,” Mukhopadhyay emphasizes.

To execute this effectively without incurring excessive operational costs or over-servicing equipment, operators should avoid trying to monitor every component in the building. Instead, teams should partner with asset experts to identify business-critical infrastructure – specifically power, water, and cooling systems – and construct targeted health maps around those primary failure points.

Ultimately, long-term resilience across APAC’s fast-expanding data center markets relies on establishing mid-to-long-term operational roadmaps today. By treating lifecycle intelligence as an essential foundation rather than a post-construction afterthought, data center leaders can make informed capital decisions, streamline workforce efficiency, and deliver sustained speed to capacity.

To learn more about bridging the gap between data center construction and operational lifecycle management, watch the full interview with Octave’s Supratim Mukhopadhyay here.