Data and the built environment

Despite a period of subdued growth, the UK built environment remains one of the economy’s largest and most complex sectors. Valued at over £560bn and employing roughly 12% of the national workforce, it underpins everything from housing and infrastructure to energy transition and public safety. Yet it is also a sector that has historically been slow to adopt new technologies and modern data practices. While the role of technology has been widely discussed, data itself is emerging as a distinct opportunity.

The scale of the industry means it generates vast volumes of information across design, construction, operation and regulation. At the same time, rising compliance requirements, stricter building safety regimes, pressure to decarbonise assets, and escalating labour and material costs are all increasing demand for better, more actionable data. Initiatives such as the “golden thread” of building information, mandatory energy performance disclosures, and growing ESG scrutiny are turning data from a nice-to-have into a necessity.

However, size of opportunity alone does not guarantee success, and investors need to be selective.

Adoption remains the first test. A large theoretically addressable market is meaningless without evidence that customers are changing behaviour. The most compelling data businesses show organic growth through new customer wins, not simply higher average revenue per user. Crucially, the data must solve a mission-critical problem more effectively than spreadsheets, consultants or internal workarounds.

Competition is intensifying, not only from other data providers but from AI-enabled tools. If generative models can deliver 80% of the insight at minimal cost, investors must ask whether the remaining 20% justifies a premium. In many cases, it will only do so if accuracy, auditability or regulatory assurance truly matter.

This places renewed emphasis on proprietary data. Exclusive access to hard-to-replicate datasets—such as long-term asset performance, real operational benchmarks or validated compliance records—remains a key differentiator, particularly as AI commoditises generic information.

Finally, the strongest propositions extend beyond raw data. Layering analytics, benchmarking, predictive insights or preventative maintenance workflows can embed data into day-to-day decision-making and materially increase switching costs.

Data in the built environment is a promising space, but enthusiasm should be reserved for businesses where the data is essential, defensible and capable of underpinning a broader, service-led offering.

To discuss any of these themes in more detail, please contact:

Matt McNally

mmcnally@armstrong-ts.com
+44 7894 736 523