By Tim Bovy and Ian Hodges
In 2006, Professor Clive Humby coined the phrase: “Data is the New Oil.” He didn’t mean raw data, but that which has been refined, ensuring its quality, and thus increasing its value. Until now, for businesses the main focus has been on financial data. Environmental, social, and governance (ESG) reporting has changed that at the exact time that the European Financial Reporting Advisory Group (EFRAG) through the Corporate Sustainability Reporting Directive (CSRD) and the European Sustainability Reporting Standards (ESRS) are mandating the reliability, understandability, accuracy, and quality of non-financial information for organisations that must file reports according to its strict guidelines. The focus here is on non-financial information, which EFRAG has placed on equal footing with the data contained in an organisation’s financials.
EFRAG states that:
“The [ESRS] …… shall require that the information to be reported is understandable, relevant, representative, verifiable, comparable, and is represented in a faithful manner.’ (Article 19b(2) first paragraph)
“The information …… shall contain forward- looking and retrospective information, and qualitative and quantitative information.” (Article 19a(3) first paragraph)[1]
The social standards alone make these requirements for reporting non-financial information a formidable task.
The social standards alone make these requirements for reporting non-financial information a formidable task. Analysing the impact of an organisation’s Own Workforce (S1, Workers in the Value Chain (S2), Affected Communities (S3), and Consumers and End Users (S4) regarding, for example, human rights, from both an historical and forward-looking perspective means accessing information from a broad range of sources and expressing it in narrative texts. Non-financial information will need to be extracted from databases, word processing, spreadsheets, emails, texts, and any other documents that enable the organisation to render a “complete depiction”, which “includes all material aspects related to the reportable content, including appropriate descriptions and explanations. Users shall be able to make informed decisions by having access to all necessary information, which shall not omit relevant aspects, factors or topics within the defined reporting boundary.”[2] Such comprehensive reporting requires a structured approach.
People and Prompts
Although Artificial Intelligence (AI) will play an important role in gathering non-financial information for ESRS reporting, the first port of call should be to identify the people within the organisation who possess the knowledge and expertise to create the AI prompts necessary to interrogate information across the entire network, including businesses upstream and downstream that form an integral part of its value chain. Prompts should be carefully and thoughtfully crafted, since they will determine the quality and usability of the responses.
To detect and offset any AI misinformation, these experts within the organisation (and quite possibly external experts as well) will be instrumental in verifying the information that AI generates. One phrase that we hear quite often is ‘the human in the loop’. There is a considerable amount of thought going into how people with expert knowledge could insert themselves as moderators able to vet the information produced by AI and look for errors, inventions, or misplaced logic, resulting from AI’s well-known flaws, including hallucinations and catastrophic forgetting.
Using AI with Information Management Systems
As a 2024 survey has noted, “Organizations with mature information management strategies are 1.5x more likely to realize benefits from AI than those with less mature strategies.”[3] Information that is already organised and structured allows AI algorithms to more easily access and process it. Similarly, information that is soundly managed is more likely to be accurate, up-to-date, and comprehensive.
Structured and standardised data also means greater consistency in reporting, improved accuracy, and greater regulatory compliance.
With sound information management, AI can streamline data gathering, perform real-time monitoring, and more readily identify trends and patterns. Structured and standardised data also means greater consistency in reporting, improved accuracy, and greater regulatory compliance.
The most recent version of ChatGPT itself notes: “Combining Artificial Intelligence (AI) with Information Management Systems (IMS) unlocks significant value by enhancing how businesses collect, process, analyze, and leverage data. This integration optimizes decision-making, efficiency, and innovation.”[4]
A Blueprint for Ensuring Quality and Increasing Business Value
Organizations reporting under the ESRS that combine AI with IMS can substantially increase their value by satisfying the requirements for ensuring the usefulness and quality of their sustainability information, which, according to the EFRAG Guidelines, “must be relevant, and faithfully represent what it purports to represent, characteristics which are known as fundamental characteristics of quality. Usefulness is enhanced when information is comparable, verifiable and understandable.”[5]
As Forbes has observed, “measuring data quality should be the crux of how businesses succeed with their data,”[6] which is why dun & bradstreet, in its report regarding “The Big Payback on Data Quality,” notes that: “Data quality is a business issue, not an IT issue.”[7] EFRAG reinforces this view with its emphasis on “The Tone at the Top: All members of the highest governance body are ultimately accountable for the quality of the information included in sustainability reporting. In particular, through their oversight, they are expected to make sure that the processes that have led to the preparation of the information included in reports respect the principles of quality of information.”[8]
Usefulness is enhanced when information is comparable, verifiable and understandable.
There is a clear intent in these guidelines, and no doubt in those to follow, to make the reporting responsibilities of organisations concrete and achievable and, as such, the EFRAG Guidelines are an essential resource for reporting organisations. Not only because they offer practical interpretations of the regulations but also because they let us see into the mind of the regulator. This should be of particular interest to boards and senior management in reporting organisations. The guidelines make clear that a high standard of information quality is required; they offer ways to judge that quality; and they also suggest a level of effort that regulators and auditors might look for in assessing reports and, by inference, in assessing the sincerity of the reporting organisation.
Effort is crucial. Gathering the information necessary to meet the ESRS’ exacting reporting requirements is unabashedly burdensome, necessitating a total commitment from the entire organisation. Reporting organisations must examine information in much the same way a manufacturer would seek to apply quality control standards to its products. Provenance, relevance, accuracy and timeliness can be considered and balanced against the cost of producing and processing the information, whether qualitative or quantitative. Any gaps or inaccuracies must be addressed so that future reports build on earlier ones and the reporting becomes comprehensive in scope and detail.
The benefit to organisations is that the EFRAG Guidelines offer them a well-defined blueprint for ensuring the quality of their non-financial information. The stock of information held as an output of this work will deepen and broaden an organisation’s self-knowledge by highlighting its strengths and weaknesses and providing clarity to its impacts, risks, and opportunities, while simultaneously ensuring its reputation for data quality, increasing its business value, and attracting investors.
About the Authors
Tim Bovy has over 35 years of experience in designing and implementing various types of information and risk management systems for major law firms such as Clifford Chance; and for international accountancy firms such as Deloitte. He has also developed solutions for organisations such as BT, Imperial Tobacco, Rio Tinto, the Kuwaiti government, The Royal Household, and the US House of Representatives. Tim is an elected member of The Royal Institute of International Affairs, Chatham House, an Independent Think Tank based in Central London, and holds a BA degree, magna cum laude, from the University of Notre Dame, and MA and C.Phil degrees from the University of California, Davis.
Ian Hodges has worked in a variety of information management roles over a twenty-year career. He has designed and implemented records and information management systems at a national scale, developing parts of the digital archive at The National Archives (UK). At a corporate level he’s undertaken information management projects with The Royal Household and Her Majesty’s Treasury. Ian also has information rights expertise developing policies and procedures for Freedom of Information and Data Protection compliance and working as a Data Protection Officer. In addition to CISM, CIPP/E and CIPM certifications, Ian holds a BA degree from the University of Southern Queensland, a postgraduate diploma from Deakin University, Melbourne and an MA from Birkbeck, University of London.
References
- [1] EFRAG, “[Draft] European Sustainability Reporting Guidelines 2 Quality of information conceptual guidelines for standard-setting,” available at https://www.efrag.org/sites/default/files/sites/webpublishing/SiteAssets/Appendix%202.7%20-%20WP%20on%20draft%20ESRG%202.pdf
- [2]Please see footnote 1.
- [3] AI & Information Management Report: The Data Problem Stalling AI Success 2024, AvePoint, available at https://cdn.avepoint.com/pdfs/en/shifthappens/AI-IM-Whitepaper-v4.pdf
- [4] Response to the prompt: “Describe the benefits of combining AI with an information management system,” retrieved 17 January 2025.
- [5] Please see footnote 1.
- [6] Cristian Randieri,”The Importance Of Data Quality: Metrics That Drive Business Success”, Forbes, Oct 21, 2024, available at https://www.forbes.com/councils/forbestechcouncil/2024/10/21/the-importance-of-data-quality-metrics-that-drive-business-success/
- [7] dun & bradstreet, “The Big Payback on Data Quality,” available at https://www.dnb.co.uk/content/dam/english/business-trends/the_big_payback_on_quality_data.pdf
- [8] Please see footnote 1.
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