The Big Five challenges in reporting reliable sustainability data

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More and more companies are encountering the same obstacles when it comes to reporting reliable sustainability data. Especially with the development of more and more regulations, such as the EU taxonomy or earlier the EU Non-Financial Reporting Directive (NFRD). And not to forget the explosively increasing information needs for ESG (Environmental, Social & Governance) data from the world of investors and investors. Often, we hear:

 “We cannot set ambitious sustainability targets as we do not have reliable data.”
“We failed to get assurance on our non-financials.”
“We struggle with collecting all data internally.”
“I don’t remember where the data come from.”

Do you also recognize one of these problems? We see this in many companies. In five steps, we explain how you can solve the most common challenges in non-financial data collection and ensure a higher quality of your data. This also ensures that the management is provided with information that actually enables them to steer the organization’s sustainability performance.

1. No one feels responsible for the sustainability data
The data request always comes as a surprise to most colleagues involved; your data supplier for energy consumption cannot explain whether the reduction target is still being achieved, and your colleague from the supply chain is always late in delivering data. The coordination of the data collection process seems like a mammoth project, and you feel like a lone fighter within the organization. Internal commitment is what you need!

Support from the organization is crucial to collect and monitor non-financial data within the set deadlines. Since setting up processes to collect, monitor, and report data takes time and resources, support from management is essential. Therefore, it only makes sense in case the management supports this, and they actually use the non-financial information that follows from these processes for management decisions. In addition, involve relevant colleagues as early as possible in the process to align the timeline, required data, the format of the data and responsibilities. This is important because data suppliers that supply the data are not always responsible for the performance shown in the figures. A simple example: One colleague provides energy consumption figures every quarter, but is not “in charge” of all programs and activities that contribute to energy reduction. HR has a mobility program, for example, and the Operations business unit is simultaneously greening energy for processes, the implementation of which is done by the Purchasing department, which purchases green energy.

Responsibilities must be clearly defined, with a possible separation between the data supplier and the KPI owner. The colleague in the example does not want to have to worry about being accountable for something beyond his or her responsibility. However, he or she must be able to explain why production location X suddenly no longer reports gas consumption.

2. There is no clear definition of the KPI
Does energy consumption only include production facilities or also offices? Are the HR figures based on headcounts or FTEs, and are trainees actually included in the KPI? Or do you work within a large organization with several countries, business units or production locations where, for example, absenteeism in country A is reported according to a different definition than country B? If a KPI is not clearly defined, it leaves room for interpretation, resulting in data that are not comparable. As a result, correct management of these KPIs is almost impossible.

Clearly defining KPIs is therefore key. What exactly do you measure with the indicator, and what specific data is required for this? What is in scope, what is out of scope? These questions must be answered before you start data collection and reporting. It may then be helpful to apply internationally recognized reporting guidelines, such as the Standards of the Global Reporting Initiative (GRI), SASB, or the Greenhouse Gas Protocol. These guidelines offer standard definitions for indicators, which means that non-financial data from different companies are comparable.


3. Data is reported in a different way or from different systems each time
One quarter, the HR figures are taken from the HR system, the other quarter from the financial system. Facility A reports gas consumption based on invoices, Facility B based on its own measurements. And is information collected manually, or requested by telephone or e-mail? You get it: inconsistent data collection processes do not really give you the feeling of having reliable data.

To ensure continuous and reliable data quality, it is necessary to standardize the data collection process and automate it as much as possible. Reliable data collection management systems form the basis for this. Whether it concerns manual data collection or the use of specific software, the question of the source of data must be answered based on a predefined and documented process.

Setting up a reporting manual can help increase the accuracy and efficiency of the data collection process. A reporting manual provides guidance for individuals involved in data collection by recording definitions, process descriptions, data units, systems used and required calculations at KPI level.

4. Much data needs to be corrected afterwards
Are the data for Q1 not correct on closer inspection? A restatement in your sustainability report? Errors happen, but too many errors indicate that there are too few internal controls built in to ensure data reliability.

Internal controls can reduce the risk of incorrect, inconsistent, and inaccurate data. Different levels are possible: from a simple “four-eyes principle” to error messages in systems when exceeding predefined norm values. There are several manual and automated checks that help to test the quality of the collected data. Internal audit can also play a role in this: from giving advice on strategy and reporting to a more limited assurance role. If the Internal Audit plays a leading role, it can internally review the management of sustainability, evaluate the associated non-financial reporting, and audit the processes and data. This saves the organization an intensive and expensive external assurance process.

5. Where does the data come from?
Some of the energy consumption was estimated last year; what were the assumptions for that? Was waste reported based on own weightings or based on the report from the waste collector? If these questions came to mind, your organization will likely struggle to report sustainability data consistently.

Documentation of process descriptions, responsibilities, KPI definitions, calculations and steps to validate data are essential. It ensures that non-financial data are reproducible and collected consistently. Relevant documents such as invoices, certificates, or meeting minutes are ideally stored systematically. This not only simplifies the work for the accountant but also reduces internal efforts. In addition, if colleagues get sick or leave the organization, the data can continue to be consistently collected by other colleagues.


Authors: Anja Cichowlas & Julian Markus.

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*Would you like to learn more about the assurance landscape and how to effectively prepare for external assurance? Sign up for our webinar ‘The Sustainability Validation Landscape & Assurance Readiness’, on the 11th of June!

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