Bad Data Breaks Good Intentions

Big investment decisions hinge on sustainability data. But if that data isn’t traceable, accurate, and complete, it quickly becomes a liability instead of a strategic asset. 

That’s why so many of the conversations we have with clients come back to one thing: trust. If the data doesn’t feel credible, they won’t use it. And if their investors or stakeholders spot inconsistencies, the entire value of a report (and the relationship) can be at risk. 

Trusted data isn’t about promising perfection. It’s about building transparency into every part of the data lifecycle. 

If You Don’t Trust the Data, You Shouldn’t Use It 

When it comes to sustainability performance, data is only useful if people believe it. “Garbage in, garbage out” applies just as much to ESG as it does to financials. Real asset investors are increasingly aware that decisions – and disclosures – must be built on trustworthy sustainability data. If the numbers behind a portfolio’s carbon footprint or energy savings are flawed, the consequences can range from misallocated capital to damaged credibility. In the current landscape, where investors, regulators, and the public are scrutinising sustainability claims, ensuring data integrity has become a critical priority. 

Think about the major decisions made with sustainability information: an investment committee approves a retrofit budget based on projected energy savings, or an asset manager proclaims great sustainability achievements in an annual report. These actions rely on the assumption that the underlying data is accurate and reliable. Poor-quality data can lead to misrepresentation and negative impacts on performance and reputation. 

For example, if a property’s energy usage was logged incorrectly and emissions are understated, a fund might appear to be meeting targets when it’s not – leading to a false sense of achievement, or even accusations of greenwashing, when the error comes to light. 

Investors today are treating sustainability data with the same seriousness as financial data; questionable data submission isn’t acceptable for financial metrics, and the same goes for sustainability metrics. This is also reflected in benchmarks and regulations, like SFDR; if sustainability disclosures are found to be inaccurate or misleading, there could be consequences, from reputational damage to regulatory scrutiny. 

This external pressure means having auditable, verifiable sustainability data isn’t just best practice, it’s quickly becoming a compliance issue. 

What’s Getting in the Way of Clean Sustainability Data? 

Achieving trusted data in a real asset portfolio is not without its challenges. Sustainability data often comes from multiple sources – utility bills, tenant surveys, facility management systems, third-party benchmarks – and it’s handled by different people.  

This introduces plenty of room for human error or inconsistent methodologies. For example, one property manager might report energy in kilowatt-hours while another reports in gigajoules, leading to confusion until the units are aligned. In other cases, data might be missing for some assets, forcing reliance on estimates.  

And with many investment teams still using basic tools like spreadsheets to manage this data, maintaining a single source of truth becomes difficult, and version control becomes a nightmare. There’s often no audit trail to see who changed what. If someone questions a utility reading, you need to be able to trace it back to where the data originated, when it was added or edited, and by whom – otherwise, you’re undermining confidence in the data. 

Companies relying on manual processes often have to do multiple rounds of double-checking to clean up inconsistencies, because they just can’t trust the data they worked so hard to collect in the first place. But the pace of sustainability reporting leaves no buffer for rework. GRESB season, for example, is part of an intense annual cycle for many real asset investors, and the deadlines don’t wait while you ask your team “are these the final numbers?”, again.  

Without strong data controls, it’s easy for small mistakes to slip through, and the margin for error is shrinking as industry standards tighten. 

Turning Data Chaos into Something You Can Stand Behind 

Building trusted sustainability data requires a combination of robust systems and vigilant processes. Our approach is to embed quality assurance at every step of the data journey.  

It starts with having a central platform to house all sustainability information – by consolidating data across assets and funds, we eliminate the inconsistencies that occurs when information lives in silos. Automated validation rules check incoming data. If a monthly water consumption figure is wildly out of line with an asset’s history or peer benchmarks, the system flags it for review rather than letting it slip through. These “sense-checks” catch common errors (like an extra zero or a misplaced decimal) early on. 

But most importantly, we maintain an audit trail. Every change or update in the dataset is logged. If a number is adjusted (say a utility reading is corrected after a billing error) we have a record of who made the change and why. This builds trust internally: anyone can drill down and see the lineage of a data point. When an investor or auditor asks, “where did this figure come from?”, we can demonstrate the path from raw data to reported metric transparently. 

On top of technology, we apply human expertise. Our sustainability consultants perform periodic data audits and sense-checks, especially ahead of critical reporting events like GRESB or year-end disclosures. With years of experience in the bag, they can often spot anomalies that automated checks might miss – for instance, an improbably high energy intensity value that suggests someone entered an incorrect floor area.  

By combining state-of-the-art technology with hands-on human expertise, we can catch and correct these issues proactively, and help clients avoid the pitfall of publishing data that doesn’t add up.

Trusted Sustainability Data Is a Design Choice 

Building trust in sustainability data doesn’t happen overnight. For many clients, it’s a journey to move from 95% manually collected data to 95% automated, quality-assured data. That shift requires more than software. It takes a plan: understanding where the data sits, how reliable it is, and what gaps need to be filled. We help clients map their path – from partial coverage and spreadsheet chaos to a system that supports confident reporting and strategic decisions. 

With our Managed Data Service, we help asset managers establish clear data governance processes through a data strategy: defining how data should be collected, who is responsible for each input, and how often it’s updated.  

Because reliable sustainability data isn’t achieved by accident – it requires intentional system design and diligent execution. But the payoff is huge. When real asset investors and their stakeholders trust the numbers, they can focus on strategy and performance improvement rather than second-guessing the foundation.  

Contact us if you want to learn more about our data services.