The ESG Data Chain: Automating the Reporting Process

Reporting on environmental, social, and governance (ESG) topics is undergoing a fundamental change. It is no longer a separate exercise for the communications department. The requirements are becoming more specific, the data is becoming auditable, and the processes are moving to the centre of the finance department. A recent study shows a rapid global convergence of ESG reporting standards (Delgado Sanchez 2024). For finance teams in Swiss SMEs, this creates a new, urgent task. They need to build and automate the ESG reporting process to integrate it into their existing workflows. The chain from data collection to the final report must have the same reliability as financial reporting itself.

Global Standards are Converging — What This Means for Swiss SMEs

A 2024 study analysed disclosure practices in 22 jurisdictions. It finds that ESG reporting is reaching global saturation. The average disclosure rate across all countries studied increased from 89.2% in 2019 to 97.1% in 2024 (Delgado Sanchez 2024). At the same time, the standard deviation of rates between countries decreased markedly. This demonstrates a strong convergence of practices and expectations.

For a Swiss SME, these figures are of direct relevance. Even if your company is not directly subject to a legal reporting obligation, it is part of a global supply chain. Large customers, banks, and investors are increasingly demanding standardised and audited ESG data. These requirements are systematically passed on to their partners and suppliers. Yesterday's voluntary disclosure thus becomes tomorrow's contractual obligation. The quality of this non-financial data is becoming a decisive factor in business relationships.

The Challenge: Data Outside the ERP System

Financial data is centralised in the ERP (Enterprise Resource Planning) system. The processes for its collection, processing, and control are established and highly standardised. The finance team can rely on the integrity of this data. With ESG data, the starting point is different. It often originates decentrally and outside of established financial systems.

CO2 emissions are calculated from consumption data for energy, fuel, and business travel. Information on employee turnover or training expenditure comes from the HR system. Information on sustainability in the supply chain comes from the suppliers themselves, often in the form of individual questionnaires or certificates. This information is often found in Excel spreadsheets, emails, or PDF documents. It is not standardised and is rarely subject to the same strict controls as accounting data. This manual and fragmented data landscape poses significant risks to data quality. The chain from source to report is susceptible to errors in many places, and the process is neither transparent nor efficient.

Building a Robust Data Chain: From Collection to Validation

To create auditable ESG reports, finance teams need a structured process. This process must resemble the financial closing process in its logic and discipline. It can be divided into clear phases.

It begins with the materiality assessment. This defines which ESG topics and key figures are actually relevant for the company's own business model and its stakeholders. This is followed by the data collection phase. Here, clear responsibilities and formats must be defined. Who supplies what data, by when, and in what form? Uniform templates and clear instructions are a first, important step towards standardisation.

The next step is validation. The collected data must be checked for plausibility. Comparisons with previous periods, internal benchmarks, or industry-specific key figures help to identify outliers and errors. A four-eyes principle is also essential here. Finally, the validated data is consolidated and prepared for the various reporting formats. This could be a dedicated sustainability report, a customer questionnaire, or an integrated part of the annual report.

Automation as the Key to Data Quality and Efficiency

A manual process for ESG reporting is time-consuming and error-prone. As the level of detail and frequency of requests increase, it quickly becomes uneconomical and ties up valuable resources in the finance team. Automation is the logical step to ensure efficiency, consistency, and quality. It creates a stable bridge between decentralised data sources and central reporting.

Automated workflows can retrieve data from various systems and files. They can systematically apply predefined validation rules and report deviations for review. They generate consistent reports at the touch of a button and ensure that the same source data is used for all analyses. This affects precisely the Excel layer that lies between the ERP export and the final report in many companies. The digitalisation of these processes has proven positive effects on the quality of accounting information, as it increases transparency and reduces information asymmetries (Accounting, Finance and Computational Intelligence 2024).

A robust, automated process also deters opportunistic behaviour and strengthens internal control (Accounting, Finance and Computational Intelligence 2024). The systematic treatment of non-financial data with the same diligence as financial data is an important step. Modern audit systems increasingly combine text analysis and anomaly detection algorithms to improve the reliability of corporate reports (Journal of Management and Business Solutions 2024). A clean ESG process is part of this development. Furthermore, studies show a positive impact of sustainable business practices on financial performance (Jurnal Literasi Akuntansi 2025). Good ESG reporting is therefore not just an obligation, but can also make a measurable value contribution.

What You Can Do This Month

Building a comprehensive ESG reporting system is a project. However, you can start this month with concrete, manageable steps to lay the groundwork.

• Form a small, cross-functional working group. Involve colleagues from finance, operations, human resources, and procurement to ensure a holistic view.
• Conduct an inventory. What requests regarding ESG topics are you already receiving from customers, banks, or other partners? What data points are you already collecting, even if only informally in Excel lists?
• Select a single, but important, key figure. A good example is the CO2 emissions generated by electricity consumption at your main site (Scope 2). This key figure is relevant, and the data source is usually clear.
• Trace the path of this key figure back to its source. Document every single step: from the energy supplier's electricity bill to manual entry into a spreadsheet, to the calculation in kilowatt-hours and conversion into CO2 equivalents. Note who does what and where errors or uncertainties can arise.

This simple exercise creates a common understanding of the complexity of the task. It reveals the weak points in the existing data chain. It forms the basis for designing a robust, traceable, and ultimately automated process.

Excel is a registered trademark of the Microsoft Corporation. SAP is a registered trademark of SAP SE.