Introduction: ESG Reporting Is Only as Strong as the Data Behind It
As ESG reporting frameworks mature in India, the focus is gradually shifting away from the format of disclosures to the quality and reliability of underlying data. Over the past few years, companies have made significant progress in aligning with frameworks such as BRSR, developing policies, and initiating sustainability-related disclosures. However, beneath this visible layer of progress lies a less discussed but critical issue which is the ESG data gap.
This gap is not about the absence of intent. Most organisations recognise the importance of ESG and are actively engaging in reporting. The challenge lies in the systems, processes, and structures required to generate consistent, accurate and verifiable data.
As ESG disclosures begin to intersect with assurance requirements, investor scrutiny, and regulatory expectations, this gap is becoming increasingly visible. Companies are realising that while they can produce reports, they are not always able to demonstrate the integrity of the data within those reports.
Understanding the Nature of ESG Data
To understand why this gap exists, it is important to first recognise how ESG data differs from traditional business data.
Financial data is typically generated through structured systems such as ERP platforms, accounting software, and internal financial controls. There are established standards, defined formats, and clear ownership within finance teams. Over time, organisations have built strong systems to ensure that financial data is accurate, auditable, and consistent.
ESG data, on the other hand, is inherently operational and distributed. It originates from multiple functions across the organisation, each with its own processes and priorities.
Energy consumption data may be recorded at plant level through utility bills or meter readings. Water usage could be tracked manually in certain facilities. Safety incidents are documented by EHS teams, often in formats designed for internal compliance rather than external reporting. Employee-related data is maintained within HR systems, while supplier-related information is managed by procurement.
Each of these data points exists within its own context. Bringing them together into a unified ESG reporting framework requires integration, standardisation, and validation, which many organisations have not yet fully developed.
The Illusion of Data Availability
One of the most common assumptions in ESG reporting is that data is readily available within the organisation. While this may appear to be true at a surface level, the reality is more complex.
Data may exist, but it is often:
- incomplete in terms of coverage across locations
- inconsistent in format and frequency
- not aligned with reporting definitions
- lacking historical continuity
For example, a company may have access to electricity bills for all its facilities. However, if certain units track diesel consumption separately or maintain records in different formats, consolidating total energy consumption becomes a challenge. Similarly, employee data may be available, but differences in classification of contract workers, temporary staff, or part-time employees can lead to inconsistencies in reporting.
This creates a situation where companies believe they have the required data, but when they attempt to compile it into a structured ESG disclosure, gaps begin to emerge.
Fragmentation Across Departments
A key contributor to the ESG data gap is the fragmentation of data across departments.
Unlike financial reporting, where data flows through a centralised system, ESG data is generated independently by multiple functions. Each function is focused on its own operational priorities and may not be aligned with ESG reporting requirements.
For instance, an operations team may track energy usage for cost management purposes, while ESG reporting requires additional granularity such as source-wise consumption or intensity metrics. Similarly, HR systems may capture employee data for payroll and compliance, but ESG reporting requires classification based on gender, employment type, diversity and inclusion metrics.
In the absence of a centralised ESG data framework, organisations rely on manual coordination to collect and align this information. This not only increases the effort required but also introduces risks related to data consistency and accuracy.
The Role of Manual Processes and Their Limitations
Many organisations continue to rely on spreadsheets and manual templates for ESG data collection. While this approach may be sufficient in the early stages of reporting, it becomes increasingly inadequate as the complexity of disclosures grows.
Manual processes are inherently prone to errors. Data may be entered incorrectly, formulas may be inconsistent, and version control becomes difficult when multiple stakeholders are involved. In addition, manual consolidation of data from different sources can lead to duplication or omission of information.
Another limitation of manual systems is the lack of real-time visibility. ESG data is often compiled at the end of the reporting period, which means that organisations do not have continuous insight into their performance. This restricts their ability to identify trends, address issues proactively, or integrate ESG considerations into decision-making.
As reporting moves towards assurance, the limitations of manual processes become even more pronounced. Without a structured system, it is difficult to demonstrate how data has been collected, validated, and maintained over time.
Absence of Standardised Methodologies
A critical but often overlooked aspect of ESG data management is the need for standardised methodologies.
In many organisations, different units or departments may adopt their own approaches to measuring and reporting ESG indicators. This can result in variations in how metrics are calculated, interpreted, and presented.
For example, greenhouse gas emissions may be calculated using different emission factors across facilities. Water consumption may include or exclude certain sources depending on local practices. Waste may be categorised differently based on regional regulations or operational definitions.
These inconsistencies may not be immediately visible in aggregated data, but they become significant when disclosures are subject to detailed review or assurance. Without standardisation, it is difficult to ensure that reported figures are comparable across locations and over time.
The Documentation and Audit Trail Gap
Another major challenge in ESG data management is the lack of structured documentation and audit trails.
In financial reporting, every number can be traced back to supporting documents and system entries. There are clear records of transactions, approvals, and adjustments. This creates a transparent and verifiable data environment.
In ESG reporting, such audit trails are often missing.
Companies may compile final figures for various indicators, but the supporting documents may be scattered across different locations, stored in different formats, or not retained systematically. There may be no documented methodology explaining how calculations were performed, or no record of internal validation processes.
This creates a significant challenge during assurance, where auditors require not only the final numbers but also the ability to trace those numbers back to their source.
Why This Gap Is Becoming More Critical
The ESG data gap is not a new issue, but its implications are becoming more significant due to changes in the regulatory and business environment.
With the introduction of BRSR Core assurance, companies are required to demonstrate the reliability of their disclosures. Investors and lenders are increasingly using ESG data to assess risk and long-term performance. Global customers are seeking greater transparency from their suppliers. Rating agencies are incorporating ESG metrics into their evaluations.
In this context, the quality of ESG data directly influences credibility, access to capital, and business opportunities.
Organisations that are unable to provide consistent and verifiable data may face challenges in meeting these expectations, even if their underlying ESG performance is strong.
Moving Towards Structured ESG Data Systems
Addressing the ESG data gap requires a shift in approach. Companies need to move from viewing ESG data as a reporting requirement to treating it as a managed system within the organisation.
This involves several key elements.
First, establishing a centralised framework for ESG data management, where data from different departments is integrated into a consistent structure.
Second, defining clear ownership for each data point, ensuring that responsibilities for data collection, validation and reporting are clearly assigned.
Third, standardising methodologies across the organisation, so that all units follow the same approach for calculating and reporting ESG indicators.
Fourth, building documentation and audit trails that support the reliability of data and enable traceability during assurance.
Finally, leveraging technology where possible to automate data collection, reduce manual errors, and improve real-time visibility.
Conclusion: Data as the Foundation of ESG Credibility
As ESG reporting continues to evolve in India, the importance of data cannot be overstated. Policies, commitments, and disclosures are only as credible as the data that supports them.
The ESG data gap represents a critical challenge, but also an opportunity for organisations to strengthen their internal systems and processes. Companies that invest in building robust data frameworks will be better positioned to meet regulatory requirements, respond to stakeholder expectations, and integrate sustainability into their core operations.
In the long term, ESG will not be defined by the reports companies publish, but by the quality, consistency, and reliability of the data they can demonstrate.