The Challenges of Manual ESG Reporting

When it comes to reporting their carbon footprint, one significant obstacle that businesses must overcome is ensuring that each and every one of their emissions—including those generated by their operations, supply chain, and product consumption—are considered. However, doing so requires a significant amount of additional research that some businesses do not have the resources or the capacity to do.

Furthermore, there is no “one size fits all” solution. Instead, it is up to each company to figure out which aspects of its business are most relevant to the many stakeholders it has in this area, and then choose the method that will be most effective in communicating them.

In reporting ESG data, companies need statement accuracy that can be measured and independently verified. If these are lacking, the accuracy of the reporting may be called into question. Human error is another risk while entering ESG data, particularly when inputs are submitted manually.

Processing times are lengthened, and additional inefficiencies are introduced as a direct result of errors that occur during manual data input or rekeying. Inaccurate data collection results in extra difficulties and inefficiencies farther down the line.

According to research released by Ernst & Young, most public firms (55%) still save their ESG data in spreadsheets created in Excel. The same survey also indicated that larger firms that collect data from various sources concur that using spreadsheets in the modern business environment is impractical. Spreadsheets have several limitations, including the inability to communicate effectively, manual data input mistakes, broken linkages across workbooks, and a lack of data security.

Furthermore, the spreadsheets used in the management of ESG data are often generated by a single individual, and they include formulae and functions others may not understand. Consequently, it is difficult to present a comprehensive and integrated picture of ESG benefits and effects since the data relating to these topics is often stored in spreadsheets or manually entered into silos dispersed across the company.

Spreadsheets may foster a sense of familiarity, but their use is likely to result in poor data administration. When using multi-tier mapping, it’s likely companies will need more human resources and work hours to produce them. In addition, maintaining data transparency becomes a laborious and unmanageable task.

Another problem for businesses is that they are unsure which indicators to employ. Metrics are always subject to change, since they are continually gathering new information on environmental effects. Because reporting standards are constantly evolving as well, it may be challenging for businesses to maintain relevant data management and reporting at the same time.

Moreover, numerous businesses still struggle to collect and organize vast amounts of high-quality quantitative and qualitative data. This data is often decentralized, multidimensional, and extensive. It originates from several departments and sources, in multiple forms, and often comes in at different times.

To successfully conduct any sustainability effort, it’s essential to have consistent processing and administration of the appropriate data. In today’s business world, the absence of standardized procedures for handling information of this kind is one of the most important and pressing hurdles. For example, it there are not enough mechanisms for collection, verification, and presentation; it may be difficult for stakeholders and prospective investors to compare ESG funds and evaluate how well they perform in terms of sustainability.

How to engage frontline staff in data collection is likely to be another obstacle. Effective ESG reporting requires more than a top-down approach. It is essential to include all workers at all organizational levels daily.

However, many firms struggle to engage frontline employees in data collecting. Most of the time, there are questions about what sorts of information to gather from frontline employees and concerns about how well they collect the data and provide it to managers.