High-quality ESG data provides a complete, reliable and consistent picture of a business environmental, social and governance performance.
Quality ESG data enables businesses to make informed decisions that align with long-term sustainability goals.
Naturally, reliable ESG data supports societal interests by promoting corporate accountability, shaping societal well-being, and fostering a more sustainable future. Moreover, access to credible ESG data aids investors in evaluating a company's ethical practices and risk exposure, influencing investment decisions.
However, obtaining and reporting on high-quality ESG data is no easy feat, for the following reasons:
These challenges are matched by increasing pressure from investors, regulators and clients, with the consequence that many companies find themselves submerged in increasingly demanding ESG data requests.
In the last few years, the European Union introduced some of the strictest rules around ESG worldwide, like the Corporate Sustainability Reporting Directive (CSRD) and the Sustainable Finance Disclosure Regulation (SFDR).
These regulations affect both companies and investors, and their impact is not limited to businesses based in the EU - companies in the US and Asia are also affected by European regulations, to the extent by which they intend to do business with EU-based undertakings.
And although the EU is effectively leading the charge in this effort to put pressure on businesses to provide more detailed information about their ESG standing, multiple organizations across the globe are following suit: the International Sustainability Standards Board (ISSB), the U.S. Securities and Exchange Commission (SEC), and several national and international regulatory bodies are all working to create more stringent rules around climate-related disclosures.
This trend around the global proliferation of sustainability disclosure frameworks and regulations does not seem to be slowing down any time soon - and some organizations might even be required to disclose ESG information according to multiple regulations and frameworks.
It's not only rules that are causing pressure. Customers demand increasingly higher environmental and social standards from companies.
Investors (and in particular impact investors) are also requesting more and more ESG information than just what's required by regulations - often to check whether a target investment is aligned with their unique impact thesis.
Another source of pressure for more reliable ESG disclosures comes from the workforce, and in particular from that part of the workforce that is constituted by individuals belonging to the Millennial and Gen-Z generations, and beyond. This is not a negligible source of concern, especially given that Millennials alone are on track to account for 75% of the global workforce in 2025.
It’s no mystery that these generations have an unprecedented consideration for ESG issues, implying that companies that fail to provide reliable and transparent accounts of their ESG performance might struggle to get access to the best talents, and experience high turnover.
While regulators are slowly getting better at defining a set of standard ESG metrics, like GHG emissions, and regulators are starting to work together to achieve more unification in their definitions, a lot in the ESG world is still attached to dangerously scattered definitions and methodological standards.
The lack of standardization gives rise to confusion among corporates and investors, which often feeds into a generally lower quality of the information reported - because firms are “mixing and matching” different sources, methodologies and guidelines.
Collecting ESG data might be the responsibility of one department - but de facto, it’s the task of many.
Depending on what issues are material to a given company, “ESG data” can come in many shapes and forms - and it can have a high level of granularity. As a consequence, in large organizations, the raw data is often scattered across multiple departments and business units, or even across supply chains.
Units and departments that have never interacted with one another before might need to work together to an extent never seen before - which can have a negative impact on data quality, especially in highly siloed organizations, or multinational supply chains (which could span countries with different processes and standards).
Another challenge to the quality of ESG data comes from the widespread adoption of estimates or proxies in reports and disclosures. Briefly put, we talk about “proxy data” to refer to data around ESG performance or KPIs of a given company that is generated by a third provider, on the base of publicly-available information.
One point worth mentioning is that, even though using estimates is a widespread practice in the industry, regulators are actually trying to limit the extent to which proxies can be used in disclosures. We have actually touched on this topic in one of our past articles.
Relying solely on proxies can negatively affect the quality of ESG data reported. What is more, different ESG data providers adopt different methodologies, which plays into the standardization issue outlined above.
As we have seen, ensuring the disclosure of high-quality ESG data can be tricky, especially if:
1) You work for a large organization or have to collect ESG data from multiple suppliers, portfolio companies or target investments.
2) You face high constraints in terms of the resources, such as time, human capital and budget that can be allocated to ESG.
3) You have to report against multiple regulations and ESG frameworks.
However, aiming for robust ESG disclosures is not something that can be compromised upon. Disclosing flawed, incomplete and unreliable ESG data can lead to greenwashing, and reflect negatively on the reputation of your organization.
Unfortunately, the lack of standardization is not something that you can personally control - however it can be comforting to know that something is moving in the right direction, as demonstrated by the recent release of the new ISSB (International Sustainability Standards Board) standards, which are creating a common vocabulary around ESG disclosures in the financial sector and are part of a renewed effort towards consolidation.
However, what you can focus on is ensuring you hold high standards of transparency when it comes to your ESG data.
Always make sure to clearly outline the methodology used for data collection and reporting to enhance credibility and ensure consistency, and don't forget to keep a pulse on the latest developments and trends in the ESG landscape.
If you plan on expanding the scope of ESG data collection beyond mandatory requirements, choose recognized frameworks for your voluntary disclosures (such as the ISSB or the GRI), and always double-check and flag inconsistencies and discrepancies across the methodologies adopted in your reports.
As we have seen, creating solid disclosures starts from building robust upstream data collection processes - which often entails involving multiple stakeholders spanning various departments, business units, and corporate hierarchy levels.
This is why it’s crucial to promote collaboration and engagement across different departments, and boost ESG knowledge and awareness among employees involved in data collection and reporting.
Engaging multiple stakeholders is a journey, and it simply won’t happen overnight.
The first step is ensuring everyone has a basic grasp of ESG concepts, methodologies, and reporting standards. You can achieve this by administering company-wide surveys to assess the pre-existing level of ESG knowledge and awareness among employees. Once you review your results, you can then schedule ad hoc upskilling sessions and presentations targeting potential gaps. In the long term, this can morph into a full-blown ESG upskilling program, depending on the resources at your disposal.
It goes without saying: robust data collection processes and verification mechanisms are key to ensure the accuracy of reported ESG data. Regularly audit and verify the data to maintain its reliability and trustworthiness.
Whenever possible, seek external validation and assurance from independent auditors or third-party experts to verify the accuracy and credibility of the data. External validation can also enhance confidence in reported data among stakeholders, so make sure to share the results of the auditing process across the organization.
Nothing in ESG is “set and forget”. Standards are continuously evolving, and companies’ processes are constantly adapting to integrate a higher consideration of ESG standards.
This is why it’s crucial to establish a robust feedback mechanism aimed at continuously reviewing and improving ESG data quality. Regularly assess reporting practices, address gaps, and adapt to evolving reporting standards and stakeholder expectations.
If followed thoroughly, all of the best practices outlined above can help increase the quality of your disclosed ESG data - however, resource constraints are always around the corner.
The harsh truth is, many ESG teams simply do not have the resources and internal capacity to address the problem of ESG data quality holistically: they are too busy dealing with a deluge of reporting regulations, standards, as well as with the nitty-gritty hurdles associated with data collection - which is part of the reason why the usage of proxies is so widespread.
This is why you should invest into advanced technologies like artificial intelligence (AI) and data analytics tools to streamline ESG data collection, analysis, and reporting. Integrating these technologies can create substantial efficiencies and enable you to focus more on strategic decision-making, choice of voluntary frameworks, stakeholder engagement, and so on.
AI solutions like Briink can support you across all of the following steps of your ESG workflow:
You can start immediately by trying the free version of our AI tools for ESG teams.
By painting a clear picture of how well a business handles environmental, social, and governance issues, ESG data helps businesses make smart choices for a sustainable future and keeps them accountable to society.
But getting and sharing high-quality ESG data isn't simple. This is due to the lack of standardization in ESG methodologies, the necessity of cross-departmental coordination, and time and resources constraints.
In this article, we went through some best practices ESG and sustainability leaders should adopt to work towards more reliable, complete and trustworthy ESG data, and we also touched upon the role that AI can play in enabling these teams to play a more strategic role within their organization - which can have wide impacts on the quality of the data produced.
If you want to learn more about this topic, and about the emerging role of AI in supporting your effort towards better ESG data quality, we have actually recently hosted a webinar on it - you can watch the full recording here.
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