In the Sustainable Development Report process, while identifying key themes serves as a strategic guideline for the entire report, data collection, compilation, and processing are the core foundation determining the report's quality. A report, no matter how professionally presented, will lose its value if the published data is inaccurate, inconsistent, or unverifiable.
In fact, this is also the most challenging aspect for many businesses. ESG (Environmental, Social, and Governance) data is often not centrally managed but scattered across various functional departments: energy consumption data is handled by the Engineering or Production department; labor and occupational health and safety information is in the Human Resources department; while financial data is managed by the Accounting department. The challenge lies in how to aggregate and standardize thousands of disparate data points, ensuring completeness, consistency, and accessibility, while also being ready to serve independent audit requirements as mandated by international reporting standards.
In the article below, GREEN IN will analyze the techniques for collecting and processing ESG data according to GRI standards, helping businesses build a solid data foundation for sustainable development reporting effectively and appropriately with available resources.
GRI 1 data quality principles
Before implementing data collection activities, the reporting department needs to establish and agree upon data governance principles based on the Reporting Principles outlined in GRI 1: Foundation. This serves as the basis for ensuring that published information is usable, meets transparency requirements, and qualifies for independent audits. The following three principles are key:
- Accuracy: Data needs to be measured at a level of detail appropriate to the nature of the business operations.
For example, when collecting electricity consumption data, businesses should not only record the total electricity cost, but also the actual electricity consumption (kWh) and, where possible, break it down by area or function, such as production and office areas. If estimation methods must be used, GRI allows their application provided that the method is reasonably sound, consistent, and clearly explained in the report.
- Consistency: To ensure comparability over time, the methods for collecting and calculating data must remain consistent across reporting periods.
For example, if in the previous year a business determined its wastewater volume based on meter readings, it should not switch to an estimation method based on water supply volume in the current reporting year, unless there is a reasonable justification and a full explanation for the change in methodology.
- Verifiability: This is a principle often overlooked but is crucial to the reliability of a report. All published data must be traceable, meaning it can be cross-referenced with original evidence. In practice, this requires that every number in a data summary (e.g., an Excel file or ESG data management system) be linked to supporting documentation such as invoices, minutes, internal records, or equivalent confirmation documents.
Technical data collection and processing procedures
Based on practical implementation experience, businesses should apply the following standardized 5-step process to establish an effective data governance mechanism, ensuring accuracy, consistency, and readiness for independent assurance operations:
Identify the data owner.
ESG data is often scattered across multiple functional departments within a business. Therefore, the first step is to build a data map to clearly identify the unit and individuals responsible for providing the source data for each group of metrics, specifically:
- Human Resources (HR) Department: Workforce size, training hours, turnover rate, gender and age distribution.
- Safety and Environment (HSE) Department: Workplace accidents, waste, greenhouse gas emissions, compliance with environmental and safety regulations.
- Engineering/Maintenance Department: Electricity, water, and fuel consumption (DO, FO, coal, LPG, etc.).
- Purchasing/Supply Chain Department: Supplier information, ESG rating, sustainable material certifications.
Clearly identifying data owners helps to reduce overlapping responsibilities and improve traceability.
Design a standardized data collection form.
One of the reasons for the inconsistency in ESG data is the collection of information through disjointed requests without a unified format. Businesses should design standardized forms and apply them consistently across all units.
Unit Collection and Conversion
This is a highly technical step, especially for businesses with multiple factories or operating locations. The initial data collected often exists in different units of measurement. The person responsible for data aggregation needs to convert all the data to a unified system of units.
Data compilation
In cases where a business has multiple branches or factories, the data aggregation process needs to be carried out carefully to avoid data duplication.
Review and Check for Reasonableness
Before including data in official reports, businesses need to perform a validation step to detect and address any discrepancies. A common and effective method is trend analysis, which includes:
- Compare the figures between consecutive reporting periods (this month versus last month).
- Compare the data by year.
When detecting fluctuations exceeding 5–10% (either increase or decrease), the responsible department should request the data owner to provide a clear explanation of the cause, such as production expansion, technological changes, operational issues, or errors in the recording process.
See more:
Comparing GRI and ESRS standards: Similarities and differences
GRI for Small and Medium-Sized Enterprises (SMEs): Benefits and Roadmap
Data classification in GRI: Quantitative and Qualitative
A GRI-based sustainability report is not simply a collection of measurement indicators, but needs to fully reflect how a business manages, makes decisions, and creates impact. Therefore, in the process of building the report, businesses need to collect and process the following two core data sets simultaneously:
Quantitative data: These are metrics that reflect the business's performance across key areas.
- Technical requirements: High accuracy, standardized units of measurement, and comparability across years.
Qualitative data: This includes information describing management methods, policies, strategies, and the operating context.
- Technical requirements: Must be substantiated by internal regulations. When collecting, departments should be asked to provide relevant evidence such as: policy decisions, meeting minutes, business procedures, training materials, or internal announcements.
Data management tools: Spreadsheets or specialized software?
Choosing an ESG data management tool largely depends on the scale of operations, the complexity of the data, and the company's budget. Below is a technical comparison to help businesses consider the most suitable solution.
Traditional spreadsheets (Excel / Google Sheets)
Advantage:
- Low cost, easy to implement.
- Flexible and familiar to most employees.
- Suitable for small and medium-sized enterprises (SMEs) or businesses just starting to implement GRI reporting.
Limit:
- Errors are easily made due to manual operations (data entry, formulas).
- Version control is difficult when multiple people are editing simultaneously.
- The level of security and the ability to retrieve change history are limited.
Specialized ESG management software
Advantage:
- Automate data collection processes through electronic forms or system integration.
- Built-in emission factors to automatically calculate CO₂e emissions.
- Centralized data storage on a cloud platform facilitates easy retrieval and auditing.
This software solution is suitable for large-scale businesses or those with complex ESG data volumes, helping to minimize the risk of errors and optimize report compilation time.
How to store records for auditing purposes.
If a business plans to implement independent third-party assurance (e.g., ESG auditing or consulting firms), establishing a robust record-keeping system is mandatory. Auditors will focus on assessing the ability to answer the core question: “What is the source of this data?”
To meet that requirement, businesses need to establish a clear audit trail through the following practices:
Organizing digitized archives
Prioritize storing records digitally rather than in paper form. The file system should be designed with a logical structure, for example:
Reporting Year → Subject (Energy / Water / Human Resources…) → Month → Original Evidence
Applying the "Hyperlink" principle
In the Master Data File, each significant data cell should be directly linked to the corresponding original invoice, record, or document in the archive folder. This approach allows auditors to quickly retrieve evidence and minimizes the need for additional documentation.
Storing calculation methods and assumptions
In addition to the data, businesses need to keep a separate file clearly describing the calculation method, assumptions, and conversion factors used.
This article has helped you understand how to collect data using GRI and the most accurate data processing techniques. We hope this information will be helpful in building a transparent database for your business. Don't forget to follow our upcoming articles at GREEN IN for more updated information!