The Real Estate Data Revolution

The commercial real estate industry is experiencing an unprecedented digital transformation. Nearly half of CRE leaders are building new tools in-house, over half are replacing outdated systems, and even more are upgrading their infrastructure to accommodate artificial intelligence. Yet despite this massive investment in technology, most organizations are missing a critical foundation: a well-structured data platform.

The Hard Truth About CRE's Digital Ambitions

Every organization in real estate has been talking about the promise of AI and smart data platforms—less wasted effort, more confidence in reporting, better decisions than ever before. But here's what industry leaders are discovering: many digital initiatives fail, not because the technology doesn't work, but because the data underneath is unstructured, inconsistent, or incomplete.

JLL’s recent global technology survey revealed a striking disconnect. While spending on data and AI is up across the board, only 40% of CRE firms have fully audited their data, and the same number say their business intelligence tools work as intended. Even more telling: there's a persistent 6-12% gap between what teams expect AI to accomplish and the results they actually achieve.

"Despite the high level of interest in AI, and then by default data, we see that majorities don't really have a strong grasp on it," explains Ibrahim Yate, Senior Research Analyst for Global Technology Advisory at JLL. "They haven't audited their data as much as they should. And this reflects the fact that most CRE professionals aren't experts in data; at least not the technical aspects behind data production and delivery".

From Scattered Systems to Strategic Intelligence

The challenge facing CRE organizations is all too familiar. “Let's be honest about what's often happening with real estate data,” says Andy Targell, Managing Director for Tech Strategy and Digital Transformation Advisory at JLL. “Lease records sit in one system, capital projects in another, energy ops data is elsewhere…. Data's in lots of different places. So when someone asks, ‘Which assets in my property should I look at for energy efficiency?’, or ‘How do I drive sustainability?’, getting that answer is difficult because data is disconnected across different systems.” 

A well-designed data platform solves this fundamental problem by creating structure, consistency, and connectivity across all data sources. The result: every insight, whether operational or strategic, comes from a common version of truth that enables accurate, efficient decision-making and sets the foundation for advanced analytics that train AI models.

The Architecture of Modern CRE Intelligence

Successful data platforms operate on three interconnected layers. The foundation begins with governance, where leaders establish how data is verified and used to support decisions. For instance, occupancy sensor data gets reviewed before implementing office policy changes or floor plan modifications. This governance creates trust in results and minimizes risk—especially crucial when deploying generative AI

The middle layer, the data factory, handles information flow by pulling together data from sources like integrated workplace management systems, property management databases, and enterprise resource planning software. The data hub applies business rules and sends clean information to secure cloud-based warehouses

The top layer delivers products that provide insights and analysis through dashboards, trend charts, and alerts highlighting key dates and opportunities. While building predictive models requires significantly more data—often ten times more than non-AI use cases—AI tools can uncover deeper patterns that transform portfolio strategy and operations.

Five Paths to Platform Success

Organizations have five distinct procurement approaches, each with clear trade-offs. Building in-house provides full control and customization but requires higher costs, longer timelines, and significant internal expertise. Hybrid approaches combine internal platforms with external modules, balancing control with speed. Anything-as-a-Service options offer resource savings and vendor expertise but limit flexibility

Partnering with consulting firms provides collaborative solutions with shared ownership, ideal when precise results matter more than speed. Managed services offer complete outsourcing for complex analytics but may create long-term dependencies and potential adoption challenges due to reduced internal ownership.

Real-World Impact: From Theory to Results

The business case for data platforms becomes clear through practical applications. Here are four quick examples:

• Portfolio optimization enables Fortune 500 leaders to standardize benchmarking rules, combining workplace management, sensor, and booking data to identify underused assets and high-cost locations.
• In life sciences, regulatory reporting drives investment in data quality and specific integrations that generate audit-compliant reports.
• Workplace management applications help global financial services firms address talent survey friction points by tracking room usage and satisfaction in near-real time.
• Sustainability reporting enables companies to meet Science Based Targets by integrating energy meters, building management systems, and utility feeds into unified carbon tracking systems.

Making the Financial Case

Winning financial support requires connecting data expertise and technology plans directly to business priorities. Successful business cases track investment against measurable progress, mapping milestones that demonstrate how platform improvements deliver returns over time. They also address the human element—one of the biggest risks to technology investments is adoption failure, making change management and user training essential components.

Your Strategic Next Steps

Senior leaders are betting big on smarter technology, but too many still make decisions with incomplete data. Moving from reactive gut decisions to fact-based, repeatable business wins requires treating data discipline as non-negotiable. Data platforms aren't just back-office technology—they're business engines that drive portfolio strategy, enhance experiences, and strengthen financial outcomes.

The path forward requires four critical steps: audit existing data sources and establish clear business rules, target specific business goals with quantifiable impact, evaluate procurement preferences deliberately, and develop business cases tied to bottom-line results. Most importantly, don't start your technology journey with AI tools or new platforms—start with the data infrastructure that makes everything else work.

In modern real estate, data is no longer a byproduct of operations. It is the product that drives smarter decision-making and competitive advantage.

To hear more from JLL’s experts like Ibrahim Yate and Andy Targell, watch JLL’s recent webinar on data platforms. Or if you’d like to speak to an expert you can contact JLL directly.