Our industry is engaged in an important dialogue to improve the efficiency and resilience of real assets through transparency and industry collaboration. This article is a contribution to this larger conversation and does not necessarily reflect GRESB’s position.
In today’s commercial real estate landscape, energy efficiency and sustainability are top priorities. Yet, despite recent technological advancements, one major, persistent barrier remains: incomplete and inconsistent data coverage. Without a comprehensive and unified approach to energy data collection, real estate owners and operators struggle to optimize performance, meet regulatory requirements, and achieve sustainability goals.
This challenge is not just about missing numbers—it’s about unrealized savings, untapped efficiency, and the inability to leverage AI for automation and optimization. The good news? Real estate portfolios can bridge the data gap with a clear, strategic approach. Here’s how.
The data coverage challenge in real estate
Many buildings operate with limited visibility into their energy use. This can happen for several reasons:
- Siloed data sources: Utility bills, meters, submeters, IoT sensors, and BAS/BMS systems across buildings often don’t communicate or report into a consolidated platform, making it difficult to get a full picture of energy consumption.
- Lack of real-time data: Many buildings rely solely on utility bills, which provide only high-level data rather than granular, actionable insights.
- Inconsistent data collection: Some buildings track energy use and equipment performance in real-time, while others rely on monthly reports or manual data entry from property managers, leading to outdated and incomplete datasets that can hinder portfolio-wide energy improvement initiatives.
- Data blindspots: Certain asset types (including industrial and multifamily) are trickier from a data access standpoint, owing to lease structures, data ownership, and physical barriers to data collection. These challenges can hinder efforts to gain the complete picture.
- Limited tenant data: Without tenant-level submetering in multi-tenanted properties, it’s impossible to monitor and manage energy efficiency in leased spaces, leading to split incentive challenges and missed opportunities.
These gaps can hinder energy optimization efforts, AI-driven automation, and even GRESB scoring. Without complete data coverage, decision-making will always be reactive instead of proactive.
Why comprehensive data coverage matters
Comprehensive data coverage is essential for achieving sustainability goals, optimizing energy efficiency, and ensuring regulatory compliance. One of the most significant benefits is its impact on ESG reporting and compliance with frameworks such as GRESB. With investors and regulators demanding more transparency, having accurate, complete, audit-grade data can help drive better sustainability scores while supporting adherence to carbon reduction mandates. Without full coverage, real estate owners risk incomplete or misleading reporting, which can lead to reputational and financial consequences and an inability to properly measure, verify, and report progress towards corporate sustainability targets.
Beyond reporting, enhanced data coverage directly improves energy efficiency and cost savings. When buildings lack granular insights into energy consumption, inefficiencies often go undetected and opportunities to optimize remain hidden from view. A complete dataset—with energy consumption and demand data broken out into 15-minute increments or less—can allow owners and operators to identify where energy is wasted, diagnose equipment issues, optimize building schedules, and reduce peak demand charges. By layering on today’s advanced AI and automation tools, buildings can further adjust operations to improve energy performance, leading to measurable reductions in operating expenses and carbon emissions.
Another critical advantage of comprehensive data coverage is its role in predictive maintenance and fault detection. When data from submeters, sensors, and BAS is fully integrated, AI-driven analytics can detect anomalies and potential equipment failures before they escalate. This proactive approach minimizes downtime, extends the lifespan of critical systems, and reduces maintenance costs. Instead of reacting to issues after they occur, building operators can take early action to prevent inefficiencies and failures—heading off wasted costs, energy use, and equipment wear and tear.
Achieving full data coverage is not just about compliance or operational efficiency—it also positions real estate portfolios to future-proof their assets. As regulations and ESG disclosures alike shift towards rewarding or even mandating improvements in energy performance, and technological advancements make AI-powered building operations both feasible and accessible to the market, those with a foundation of robust, high-quality data will have a competitive edge. Real estate owners who invest in comprehensive data collection and analytics now will be better prepared to optimize energy use, meet evolving sustainability standards and regulatory requirements, and maximize asset value over the long term.
Building a unified energy data strategy
Achieving 100% portfolio data coverage requires a strategic, phased approach. Here’s a step-by-step roadmap that real estate owners and operators can follow to get started:
Step 1: Inventory existing data sources
Before making improvements, assess what data you already collect and where gaps exist.
- Are main meters integrated into a cloud-based platform (via smart metering or the installation of gateways)?
- Are you tracking whole-building energy use, or do you have submetering for different spaces?
- Are there parts of your portfolio where you rely solely on monthly utility bills?
- Are there parts of your portfolio where you have zero data coverage whatsoever?
- Are HVAC systems, lighting, and other key equipment loads being monitored separately?
- Are you monitoring utilities beyond electricity, like water, natural gas, steam, and/or fuel oil?
Answering these questions can help you identify your problem areas and prioritize investments in new meters, sensors, and integrations.
Step 2: Expand & standardize data collection
Once you’ve identified gaps, the next step is to ensure comprehensive and consistent data collection across your portfolio:
- Metering: Evaluate options for main meter data capture where you have meters that are not reporting into a centralized energy management system.
- Submetering: Evaluate installing submeters to track tenant-specific and equipment-specific energy use.
- BMS integration: Map BMS communication protocols to understand where BMS data can be integrated into an energy management system.
- IoT sensors: Deploy smart sensors for real-time tracking of occupancy and temperature and lighting automation.
- Data normalization: Standardize data formats across different buildings, making it easier to analyze portfolio-wide.
Step 3: Centralize data for a unified view
Siloed data leads to blind spots in energy performance. The solution is a single energy intelligence platform that consolidates all sources into one unified dashboard.
- Integrate all data sources into your energy management system of choice (from bills to meters and submeters to BMS).
- Evaluate API connectivity between related systems to avoid data fragmentation and drive better insights across your organization.
- Understand different stakeholder use cases for energy data and configure reports and dashboards to deliver critical insights to different user personas.
Step 4: Automate insights & actions
Once data coverage is in place, the final step is to layer on AI-driven insights and automation:
- AI-Powered fault detection: Automatically identify HVAC inefficiencies, equipment malfunctions, and energy anomalies that can prevent energy waste and extend equipment lifetime.
- Automated demand response: At applicable assets, leverage AI tooling and automation to adjust energy usage based on grid conditions, with an eye towards optimizing costs and carbon intensity, without manual intervention.
- Predictive analytics: Use historical data to forecast future energy consumption and demand and proactively optimize building performance.
Final thoughts
Achieving complete energy data coverage is no longer aspirational—it is a necessity for the future of sustainable and efficient real estate operations. By bridging data gaps, real estate owners and operators can unlock the full potential of AI-powered optimization, improve ESG performance, and drive long-term financial and environmental benefits.
With the increasing pressures of regulatory compliance, investor expectations, and sustainability commitments, organizations that prioritize comprehensive data coverage will be better positioned for success. The path forward requires strategic investment in technology, smart data collection, and the integration of AI-driven analytics. By taking these steps, real estate portfolios can transform fragmented data into actionable intelligence, creating buildings that are not only more energy-efficient but also more resilient, adaptable, and future-ready.
This article was written by Zoe Williams, Director of Marketing at Noda. Learn more about Noda here.