Why Squarely is Building the Foundation Before the Penthouse

It is a question we hear with increasing frequency from sustainability leads, CFOs, and strategy teams who are serious about where ESG reporting is heading: when will Squarely include financial materiality analysis — the ability to link material sustainability topics to financial risks, opportunities, scenarios, and business implications? 

It is a good question. And the honest answer is: we are building toward it deliberately, one infrastructure layer at a time. Not because the ambition is absent, but because we have seen what happens when that ambition outpaces the foundation it rests on. 

This post is about why we believe data structure has to come before data sophistication — and what we are doing, specifically, to get the foundation right. 

Doing things right before doing the right things 

There is a distinction worth making at the outset. Doing the right things means building toward financial materiality analysis, scenario modelling, ESG-linked strategic planning — the capabilities that transform ESG from a disclosure exercise into a management tool. That is clearly the direction the market is heading, and it is clearly where Squarely is heading too. 

Doing things right means ensuring that the data feeding those capabilities is structured, validated, traceable, and reliable before those capabilities are built on top of it. 

The order matters enormously. An organisation that builds a financial materiality module on top of poorly structured ESG data does not get decision-useful insights. It gets sophisticated-looking outputs built on shaky inputs — and when those outputs inform a board decision, a capital allocation, or a regulatory disclosure, the consequences of that shakiness become material. 

The ESG technology space has produced several cautionary examples of platforms that raced to build analytical layers before the underlying data architecture was ready. The result, consistently, is the same:  

• data migrations that consume months of organisational time,  
• retroactive cleaning exercises that undermine historical comparability, and  
• a loss of client trust that no feature release can fully recover. 

Squarely's position is simple: we would rather take the time to build the right foundation now than spend the next three years helping clients undo a foundation that was built too quickly. 

What the foundation actually looks like: the Data Blueprint 

When we talk about data structure at Squarely, we are not talking about storage architecture or database design in the technical sense. We are talking about something more fundamental: what does it mean to truly capture an ESG data point, rather than simply record a number? 

A reported figure — say, total energy consumption for a given year — looks simple on the surface. But behind that figure sits a set of questions that determine whether it is reliable enough to withstand external assurance, investor scrutiny, or regulatory review.  

• Who collected this figure?  
• From which sources?  
• Against what disclosure definition?  
• Was it reviewed before it entered the system?  
• Has it changed since the first submission, and if so, why? 

Without answers to these questions, a number is not data. It is a claim. 

What Squarely refers to internally as the Data Blueprint is our architectural response to this problem. Rather than capturing a reported figure in isolation, the platform is designed to capture the full context that makes that figure trustworthy and usable downstream. In practice, this means the platform is structured to hold: 

• Quantitative and qualitative information together. A figure without the narrative around it is incomplete. Why did energy consumption increase by 18% this year? Was it acquisitions, a new facility, a change in boundary scope, or an actual performance decline? The number alone cannot answer this. The Data Blueprint captures both dimensions simultaneously, so that the story behind the data travels with the data itself. 
• Supporting evidence and documentation. Every material data point should be traceable to a source document — a utility bill, an HR record, a third-party measurement report. The Data Blueprint structures this evidence as an integral part of the data submission, not an afterthought retrieved during audit preparation. 
• Explanations for changes, spikes, and anomalies. Year-on-year comparability is one of the most scrutinised aspects of ESG reporting. When a figure changes significantly between periods, the platform captures the explanation at the point of submission — not reconstructed months later when an assurance provider asks the question. 
• Ownership and accountability. Every data point in the platform has a named owner: a specific person, in a specific function, responsible for its accuracy and completeness. This is not an administrative detail. It is the mechanism that makes data governance real rather than nominal. 
• Approval workflows and audit trails. Before a data point is used in a report, it passes through a structured validation process. The platform records who approved what, when, and on what basis. This audit trail is not generated retrospectively — it is built into the collection workflow from the start. 

Why this enables what comes next 

Linking material sustainability topics to financial drivers, assumptions, time horizons, and scenario-based impacts — is strongly aligned with where Squarely is heading. It is not a feature we are deferring indefinitely. It is a capability we are building toward responsibly. 

The reason the Data Blueprint matters for that ambition is direct: financial materiality analysis is only as credible as the ESG data it draws from. If the underlying figures are estimates, unverified, or disconnected from their source documentation, then a scenario model built on those figures produces outputs that look precise but cannot be defended.  

When Squarely's data architecture is working as designed — when every figure has an owner, an evidence trail, an approval record, and a qualitative narrative — the data becomes a genuine asset. It can be aggregated with confidence. It can be compared across periods without caveats. It can feed analytical layers, financial models, and strategic planning processes because the people using those outputs know exactly what the inputs represent and how they were validated. 

That is the foundation we are building. And it is the only foundation on which the capabilities our clients are asking for can be built reliably. 

A platform built alongside the people using it 

One of Squarely's structural advantages in this process is that our product and technology teams are in-house, and the platform is developed in close collaboration with our sustainability consultants and clients. This means that the Data Blueprint is not a theoretical architecture designed in isolation — it is being shaped by how organisations are actually implementing sustainability reporting requirements on the ground, in the GCC and beyond. 

The feedback we receive from clients — including the kind of thoughtful, forward-looking input that asks us to think beyond disclosure toward financial decision-making — directly informs our product roadmap. 

We are approaching this one module and one infrastructure layer at a time, because we want the analytical capabilities our clients are asking for to be built on organisational data that is clean, validated, and genuinely reliable — not on data that looks complete but cannot withstand scrutiny when it matters most. 

The penthouse is coming. We are making sure the foundation is ready for it.