Data center real estate development requires massive upfront investment in infrastructure—a bet on future client demands that are notoriously difficult to forecast, especially with the unpredictable rise of AI, edge computing, and regulatory shifts.

When demand fails to materialize as planned, operators are left with underperforming assets that tie up capital and drag down ROI, while hesitance to build ahead can mean lost deals to faster competitors.

This balancing act places enormous pressure on both real estate and operational teams to 'get it right' far in advance, often without the necessary data granularity.

Legacy forecasting models depend on macro-level industry trends and client intent signals, rarely integrating real-time, granular, or alternative data to inform buildout decisions.

There’s often a siloed disconnect between real estate teams, technical planners, and downstream client pipeline visibility—creating a structural barrier to agile capacity planning.

Existing solutions rely on outdated market studies, client pre-leasing, and basic scenario planning, none of which offer real-time responsiveness or leverage interdisciplinary data sources for decisioning.

These approaches remain reactive and lag industry change.