If CRE functions still run on fragmented, disconnected systems, what has to be in place before the data they hold can produce intelligence?
The Data Foundation, hosted by Darren Hill of NextSense, examined how teams build the data infrastructure that intelligence requires, how organisations connect information across siloed systems, and who should own it.
Tribe Host: Darren Hill
Co-Hosts: Elizabeth Linden (Lloyds Banking Group), Bex Moorhouse (WPP), Fiona Happiness (GSMA), Lewis Barker (ServiceNow), Surasen Naidu (Standard Chartered) and Alex Grossick (Deutsche Bank)
Scribes: Grace Olorunshola and Ismah Khan
Who Owns Workplace Data?
Workplace data remains deeply fragmented across CRE, HR, finance, IT, security and external service providers. Some organisations run centralised data teams or centres of excellence, but scattered systems still make a single comprehensive view difficult.
A useful distinction emerged between owning data and owning its intelligence. The goal is complete visibility and access to time-sensitive insights across property, safety and HR functions, and this does not require storing every raw dataset internally.
Clear ownership becomes critical during M&A activity, when newly acquired assets are not yet integrated into existing management systems. Some felt that each dataset, and the intelligence drawn from it, needs an unambiguous owner and clear accountability, wherever the data resides.
Data Quality, Consistency and Risk-Based Governance
Collecting more data does not automatically produce better decisions. Inconsistent terminology and metrics, such as varying calculations of occupancy rates, can undermine otherwise useful analytics. One example was business units calculating desk and occupancy ratios on conflicting bases, which means common definitions are needed before tenure or location performance can be compared.
Rather than attempting to clean all historical legacy data, many supported a risk-based approach to governance. High-consequence datasets such as lease commitments, financial liabilities and employee health and safety need strict validation, cut-off points and audit checks. One example was applying exactly that to a lease portfolio. Lower-risk datasets, including usage data, can tolerate minor imperfections without stalling progress.
Many were in agreement that core definitions and taxonomies should be standardised across business units, with controls proportionate to the operational and financial impact of the decisions they support.
Turning Data into Strategic Narrative
Distributing raw numbers or generic dashboards is not the same as delivering actionable intelligence. Occupancy shows presence, but it does not reveal whether a location is cost-effective, productive or strategically suited to the business.
Many in the Tribe felt that data analysts need to partner with real estate experts to understand the “why” behind the numbers. It was also observed that CRE teams need to tailor the message to the audience, translating spatial metrics into financial terms for finance leaders and into experience and culture impacts for HR.
One of the Tribe members felt that having the data is not enough: teams also need people with the soft skills to tell a compelling story, turn insight into business value and build stakeholder trust in what the data shows. The Tribe largely agreed that reporting should start from the core business question and turn complex data into a clear strategic narrative.
AI and the Evolution of Real Estate Expertise
Many of the Tribe described AI as significantly accelerating transaction processing, anomaly detection, reporting and predictive asset management, but believed it cannot replace domain knowledge or human judgement. Real estate remains a relationship-driven, client-facing industry: AI analytics can establish a deal or identify an opportunity, but human intervention remains pivotal in closing it.
Standard corporate approval cycles struggle to keep pace with AI development, which makes continuous upskilling and clear usage guardrails necessary. One of the Tribe members added that technology in CRE can no longer be treated as an add-on to someone’s job. Teams need technical expertise, in-house or through third parties, to shape and unlock the value AI can create.
One example showed the risk of skipping validation: early-career staff accepted AI-generated space recommendations without checking them in context. Many of the Tribe therefore believed outputs need human-in-the-loop review and enough domain expertise to challenge the machine’s assumptions.
Biggest Opportunity: Augmenting Productivity and Building Incentivised Supplier Partnerships
Repetitive manual reporting can be automated, and AI can be used for rapid trend analysis and forecasting. Pairing technical data specialists with real estate experts lets organisations validate AI outputs, challenge baseline assumptions and ensure that predictive insights lead to sound strategic decisions.
The second opportunity is to move away from transactional vendor relationships by securing open API access and contractual data portability rights. It also lies in aligning commercial incentives so that service providers are rewarded for shared savings, efficiency gains and better workplace outcomes rather than for maintaining billable manual processes.
The roadmap that emerged from the discussion starts with explicit data owners and an integrated foundation that connects real estate, HR, finance, IT and security platforms through APIs. It then applies AI to specific business problems, with trained teams and subject-matter experts still making the final decisions.
Biggest Challenge: Preserving Professional Judgement and Avoiding Supplier Lock-In
It was noted that current AI adoption in real estate sits at around 12%, and that rapid integration brings risks. Over-reliance on AI agents can create echo chambers, surface biased outputs or weaken the critical reasoning of early-career professionals. Securing approval for data governance roles remains difficult, even though governance is the prerequisite for trustworthy AI outputs.
Supplier relationships add a second problem. Third-party facilities management and property providers frequently restrict API access or make it hard to export organisational data. If automation reduces a vendor’s billable hours or revenue, that vendor has little incentive to support the client’s efficiency. One organisation lost historical operational data when it moved away from an FM supplier.
The Tribe largely agreed that contracts need to guarantee open API access, data ownership, historical data retention and explicit export rights, whatever the state of the supplier relationship.
OCCUPEERS took place on 13 October 2026 at Fulham Pier, London.
Find out more about the event at space-plus.org/occupeers