So what’s the difference?

Standardisation is rooted in uniformity and harmonisation in compatibility. Depending on the situation, either can offer advantages to unlocking the effective use of data. To unpack this further: 

Data standardisation is the process of bringing data into a uniform format to ensure consistency and comparability. There is a choice of bases on which standardisation may be applied. In a previous post, we identified 13, ranging from file formats to governance.

Data harmonisation is about making disparate data sets interoperable. It’s crucial when dealing with multiple datasets with varied standards as it brings these diverse data sources together into a coherent, usable whole. 

To illustrate the difference, let’s take the example of car. The way fuel for cars is refined and distributed is standardised: petrol from any supplier is expected to work in any ordinary petrol engine. By contrast, a car’s interior controls are harmonised: every car must have a way to steer, accelerate and brake but there is no single layout for how those controls are arranged.

Why harmonisation matters: lessons from TNFD

Applying this to our recent work supporting the Taskforce on Nature-related Financial Disclosures (TNFD), we can see why harmonisation is often essential. TNFD asked us to help develop their global data strategy and a set of principles for nature data. Early on, it became clear that nature data could not be reduced to a single standard because it spans water, soil, species, forests, and many other systems, each with its own metrics and methodologies. 

In a fragmented landscape like this, harmonisation serves as the connective tissue. It allows decision-makers to interpret nature-related risks, opportunities, and impacts through a more integrated view.

The benefits of harmonisation

Improved Decision-Making: Harmonised datasets offer a broader, richer, but still integrated view, enabling better-informed choices, particularly when decisions draw from multiple data sources.

Reduced Friction: Organisations can continue using the tools, formats, and definitions that work for them, while still contributing to an interoperable system.

Faster Collaboration: Harmonisation enables a shift from ‘agreeing on one way of doing things’ to ‘doing one thing well’, encouraging a focused, practical use-driven approach that drives alignment.

Why harmonisation fits IB1’s approach

These benefits are what makes harmonisation a natural fit for IB1’s use-case driven approach. In our Open Energy work, as we explore effective data-sharing use cases for the energy sector, we’re facilitating cross-sector collaboration with Distribution Network Operators (DNOs), regulators, and other stakeholders in the sector. Each has its own definitions, terminology, and internal standards. So how do they all agree on a common language? 

The answer is, they don’t, and they don’t need to. Expecting them to adopt one common language is unrealistic, time consuming and unnecessary. This would be a standardisation-first approach. Useful in some contexts, but often slow, costly, and difficult to achieve at scale. Instead, the approach is to pick a real-world use case and harmonise our approach across multiple stakeholders and data sets. Use cases give our working groups a practical focal point, allowing collaboration to form around specific needs. 

“We prefer to harmonise through utilisation and application rather than theorise and wait for a standard to be implemented”

Gavin Starks, CEO, IB1 at the Open Energy webinar. 

So when does standardisation have a part to play? 

Standardisation creates stability and comparability where consistent reporting is essential. For instance, this was the recommended approach in our Impact Investing report for COP28, where we advised organisations to require data-backed, standardised environmental reporting from their supply chains. This is crucial for decarbonisation and for accurate Scope 3 emissions reporting because stakeholders, consumers, investors and employees increasingly expect businesses to provide a full and trustworthy account of their value-chain emissions. 

Data standardisation, in this context, is the right way to go because it establishes a common baseline that ensures everyone is measuring and reporting emissions in the same way, enabling meaningful comparisons, credible disclosure, and targeted action.

Ultimately, harmonisation and standardisation both have roles to play. But, often in our work we encounter multi-stakeholder projects, with disparate data sets that require a harmonised solution. By grounding decisions in real use cases we’re able to find cross-sector solutions to real-world problems.