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No matter the business or industry, when it comes to reporting ESG performance, companies struggle with one common challenge – data.
It is the single most important driver of transparent and credible ESG reporting. Data grounds reporting in measurable, comparable, and objective terms. From base lining and target-setting to progress monitoring, a data-driven approach is essential for reporting and the wider ESG programme to which it belongs.
Find out more about the ESG reporting challenges with data in this article.
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. But it is needed to internalise ESG commitments and integrate sustainability metrics and targets into daily operations.
Poor data management capabilities are made worse by the uncertainty surrounding disclosure standards. With multiple reporting frameworks, changing legislative requirements, and diverse investor and market interests, companies struggle to choose the most suitable reporting standard. Often, companies end up using or referencing more than one framework in their reporting approach.
The lack of standardization in ESG reporting can be perplexing for those looking for guidance in establishing their own ESG data approaches, including report users. Without a clear and consistent standard in place, the world of reporting frameworks remains chaotic.
However, there is some positive aspect to the situation. Despite the confusion caused by the overlap between ESG frameworks, it also means that there are shared concepts that you can't deviate too far from. Your underlying data management capabilities can be applied across frameworks and should be strong enough to adapt 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. That means report publishers won’t have to spend time 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:
