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From chaos to clarity

Why firms need a robust sustainability data governance framework

Authors: Kate Mckenzie, Senior Manager, Consulting

Developing effective sustainability strategies and delivering transparent reporting demands access to large volumes of reliable data, from a multitude of sources, often combined in new ways.

To succeed in having a data-backed sustainability strategy, organisations must be able to collect and manage their data efficiently and effectively.

Previously, we discussed the importance of using quality data and management information to drive your sustainability strategy. Data is central to a successful sustainability strategy, forming the foundation for informed decision-making and strategic development. However, it can be challenging to manage.

Why do organisations need to manage their data?

1. Impact, risk, and opportunity management

Organisations should integrate data-led information into managing their impacts, risks, and opportunities, and into business processes and decisions. However, for data to be truly valuable and integrated across the business, it must be accessible, understandable, and subject to further analysis by the decision-makers.

2. Inform internal models

Expectations are rising for organisations to conduct multiple climate scenario analyses and integrate their outcomes throughout the business. As in all modelling, a deep understanding of limitations, assumptions, and estimates is needed to make informed decisions.

3. Reporting and stakeholder engagement

Transparent communications regarding data quality and management are increasingly expected by regulators and other stakeholders. Stakeholders want to understand the quality of the data the firm uses to make strategic decisions, as this may affect how they understand and utilise the relevant information for their decision-making, such as investments or regulatory supervision.

For banks and insurers, the Prudential Regulation Authority (PRA) set out its expectations within the recent Consultation Paper - CP10/25. For large and listed firms, there are standards for data quality set under the UK Sustainability Reporting Standards (SRS) (currently in consultation). Read more about implementing CP10/25 and UK SRS here.

4. Assurance

There is an increasing requirement for assurance on sustainability-related information. In the UK, there is a consultation paper reviewing how and when this will be implemented, but we are seeing this trend globally. Implementing robust systems to manage and track data significantly streamlines the sustainability assurance process.

A high degree of confidence in data quality is critical to support each of these use cases. Organisations will also need to consider how to improve data quality for the most strategically material data.

What does data quality look like? A case study example

When calculating insurance-associated emissions, the Partnership for Carbon Accounting Financials (PCAF) sets an industry-leading framework which also provides guidance on data quality scoring. However, there are different levels of quality within each PCAF hierarchy score. For example, proxy emissions data for UK firms may be of better quality than those in countries which do not have a government-led and science-based data bank for sector-specific emissions data. Even reported data, with the highest data quality score, can vary in quality with the size and sophistication of the entity reporting, especially when it has not been subject to assurance. While PCAF provides a useful starting point, organisations need to create their own data quality scoring system to reflect their specific data sources, business models, and regulatory environments.

Score	Options	Emissions data source
1	Reported emissions	Verified actual GHG emissions data
2	Reported of physical activity-based emissions	Unverified actual GHG emissions data or primary energy consumption data
3	Reported of physical activity-based emissions	Physical activity-based for production output
4	Economic activity-based emissions	Reported emissions, or physical activity-based data for an entity, attributed to the specific project or asset
5	Economic activity-based emissions	Sector-average GHG emissions

PCAF data quality score table

What steps can you take?

1. Implement a data governance framework

Organisations should introduce a formal data governance framework to ensure accountability, consistency, and traceability of data. Responsibility should be clearly outlined within your sustainability operating model – there is a need for collaboration between sustainability teams, data teams, and business units to ensure data is fit-for-purpose and aligned with strategic goals.

2. Detailed metadata

Firms will need to track sustainability-related metadata– information about each data point, such as data source, quality, and sensitivity. This process can help automate the detection of potential issues and obtain new data points when they become publicly available, with LLMs playing a key role here. However, AI is not a one-size-fits-all solution; it is effective only if the tool meets specific needs and requirements while ensuring accuracy. Furthermore, there are trade-offs to consider, such as the AI-associated emissions and rapidly growing potential exposure to cyber threats.

3. Create a data plan

Organisations should develop a structured data improvement plan, aligned with their governance framework, to enhance the quality of sustainability data over time. Organisations should prioritise improving data quality for the most material sustainability data points, those that have the greatest strategic, reputational, or regulatory impact.

4. Culture and change management

Implementing the right culture is important to ensure that data managers collect and create data in the right way, following the data governance framework and ensuring all data points are complete. Furthermore, it is vital that organisations build a data-literate culture where employees understand the value of sustainability data and are trained to manage it responsibly.

5. Using existing structures

Most importantly, use the expertise and frameworks that you already have in-house – organisations will typically have data management processes in place. These should be adapted to work for your sustainability data.

Understanding data quality is a vital step in understanding sustainability-related information and using it throughout the business. However, maintaining metadata on data quality and data sources appropriately can be challenging and requires a robust data governance framework. Organisations must start this journey early and prepare for the emerging data management expectations from regulators and other stakeholders.

Our team of practical and experienced consultants continue to support our clients in setting their own agenda to address rapidly changing sustainability and climate-related requirements.

Please contact your usual Crowe contact for more information.