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No matter the business or industry, when it comes to reporting on ESG performance, companies struggle with one common challenge - data. It is the single most important driver of transparent and credible ESG reporting (the same for effective, robust ESG strategies, which one may argue are two sides of the same coin). Data grounds reporting in measurable, comparable, and objective terms. From baselining and target-setting to progress monitoring, a data-driven approach is essential for reporting and the wider ESG programme to which it belongs.
Companies are used to financial reporting processes but the approach for ESG data is quite a different ball game altogether. For one, unlike traditional financial reporting where ownership of financial information is often already centralised, ownership of non-financial data is fragmented across different functions with varying levels of ESG understanding. Information silos prevent timely and relevant data to reach report publishers.
Secondly, the terminologies and concepts behind ESG data are new to many, requiring training and awareness on ESG at the functional level for each business unit. For example, Economic Value Generated & Distributed is a Global Reporting Initiative (GRI) criteria which is related to but not the same as financial textbook definitions of revenue and expenditure. For ESG data collection, department-level buy-in is a hurdle for data collection as it is needed to internalise ESG commitments and integrate sustainability metrics and targets into daily operations.
Poor data management capabilities are undermined by uncertainty around disclosure standards. With so many reporting frameworks, evolving legislative requirements, and varying investor and market interests, companies are hard-pressed to pick a reporting standard that best fits this juggling act. Often, more than one framework is used or referenced in a company’s reporting approach. The lack of standardisation in ESG reporting can be confusing for many seeking guidance in establishing their own ESG data approaches, and even for report users. Until a clear and uniform standard is enforced, it’s a wild world where reporting frameworks are concerned.
There is significant overlap between ESG frameworks, but the good news is, while this has created confusion for companies, it also means that you can’t stray too far from shared concepts. Your underlying data management capabilities are transferable between frameworks and should be robust enough to respond to changes.
What happens when companies tack on ESG reporting without a systematic approach to data? A mad scramble to collect, coordinate, trace, and audit information from business units before the reporting deadline. To simplify the data collection process, many companies invest in software to manage ESG data on a 24/7 basis, with automated data collection so that come reporting season, report publishers won’t have to spend time disproportionately going through the process of soliciting information manually.
Data management software collects data according to reporting criteria, ensuring alignment with the chosen ESG framework. The use of such technologies to reliably organise and process data is especially useful when reporting companies mature and the amount of data becomes overwhelming, or when data sources span multiple entities and networks.
A robust software solution addresses many of the challenges of ESG data management, including reducing the risk of greenwashing, data manipulation or human error, and easing the auditing process later on with a clear view of the data source, thus enhancing accountability.