Primary vs Secondary Data in ESG Reporting
Oct 10, 2025
Understand the critical differences between primary and secondary data in ESG reporting and how to effectively combine them for accurate disclosures.
Primary and secondary data are the backbone of ESG reporting. Here's the difference:
Primary data is collected directly by your organisation (e.g., energy meter readings, employee surveys). It's specific, accurate, and directly tied to your operations.
Secondary data comes from external sources (e.g., industry averages, government statistics). It's easier to access but less precise and may not reflect your unique circumstances.
Why does it matter? Regulators and stakeholders demand transparency in ESG disclosures. Primary data provides precision and traceability, but it's costly and resource-intensive. Secondary data is accessible and cost-effective but may lack accuracy and relevance.
The best approach? Combine both: Use primary data for direct operations (like Scope 1 and 2 emissions) and secondary data to estimate areas like supply chain impacts (Scope 3 emissions). This creates a balanced, reliable ESG report while managing costs and resources.
Quick Comparison
Factor | Primary Data | Secondary Data |
|---|---|---|
Source | Direct from operations | External sources (e.g., databases) |
Accuracy | High | Moderate |
Cost | High | Lower |
Timeliness | Real-time or near real-time | Historical |
Audit Trail | Clear and traceable | Limited |
Combining these data types ensures your ESG reporting is precise, thorough, and aligned with regulatory expectations.
Primary Data in ESG Reporting
Key Characteristics of Primary Data
Primary data in ESG reporting comes straight from a company’s operations. Unlike estimates or generalised industry figures, this data is directly collected from business activities, offering greater accuracy and standing up better in audits.
For example, installing smart meters to measure electricity usage across facilities provides real-time data, reflecting actual consumption patterns rather than relying on averages. The same approach applies to tracking water usage, waste generation, and energy efficiency metrics - offering a more detailed and precise picture.
Another important aspect is traceability. Primary data creates a clear audit trail, linking the original measurement to the final ESG disclosure. For instance, HR system records can verify workforce diversity data, ensuring transparency and accountability.
What also sets primary data apart is its specificity. Instead of relying on broad industry benchmarks, organisations can base their ESG reporting on data that aligns with their specific operations, locations, and business models. This tailored approach becomes especially useful when showing progress against precise ESG goals.
Use Cases for Primary Data in ESG
Primary data plays a central role in various ESG reporting scenarios. For example, it’s essential for calculating Scope 1 and Scope 2 emissions. Direct measurements, like fuel consumption in company vehicles, natural gas usage in production, or electricity consumption in facilities, form the backbone of accurate carbon footprint assessments.
In manufacturing, process-specific data is invaluable. Monitoring systems on production lines can track energy consumption per unit produced, waste generation rates, and water efficiency. This level of detail not only improves reporting but also highlights areas for operational improvements.
Social metrics also depend heavily on primary data. Employee satisfaction surveys, diversity statistics from HR systems, training completion rates, and health and safety reports provide a direct view of an organisation’s social performance.
Supply chains are another rich source of primary data. When suppliers share their emissions data, energy usage figures, or sustainability certifications, it enhances the precision of Scope 3 emissions reporting. Scope 3 emissions are often the most challenging to measure, making accurate supplier data a game-changer.
Water and waste management are also well-suited for primary data collection. Tools like flow meters, waste stream weighing systems, and recycling trackers provide concrete numbers, offering clear evidence of environmental efforts.
Benefits and Challenges of Primary Data
Primary data comes with clear advantages but also brings some hurdles. On the plus side, it offers unmatched accuracy. For instance, direct energy monitoring can highlight actual consumption patterns, which may differ significantly from industry averages. This precision allows for more accurate carbon footprint calculations and targeted efficiency measures.
It also boosts credibility. As investors and regulators demand higher-quality ESG data, robust primary data collection processes can build trust and confidence in an organisation’s disclosures.
That said, collecting primary data isn’t without its challenges. The costs can be high - installing monitoring equipment, training staff, and maintaining systems require considerable investment, which can be a heavier burden for smaller businesses.
Another issue is data fragmentation. Primary data often exists in different systems, such as energy management platforms, HR databases, or production monitoring tools. Bringing this data together can be time-consuming and complex. Without proper integration, creating a comprehensive ESG report becomes a significant challenge.
Additionally, managing primary data effectively requires dedicated resources, which some organisations may underestimate. Skilled personnel are essential for maintaining ongoing data collection programmes.
Modern solutions, like neoeco's financially-integrated sustainability management, help address these challenges by automating data collection and centralising information from multiple sources. This reduces manual effort while preserving the accuracy of primary data, simplifying ESG reporting processes.
Finally, some primary data metrics, such as biodiversity impacts or long-term social outcomes, require extended periods of measurement. This can lead to gaps in reporting timelines, which organisations must carefully manage to ensure consistent and meaningful disclosures.
Secondary Data in ESG Reporting
Sources and Applications of Secondary Data
Secondary data plays a key role in ESG reporting, especially when gathering primary data isn't feasible. It not only bridges data gaps but also helps standardise reporting practices.
For instance, UK government resources like DEFRA offer conversion factors to calculate carbon footprints. Similarly, industry databases such as Ecoinvent provide detailed life cycle assessment data, enabling businesses to evaluate the environmental impact of their products and processes. These tools are especially useful for manufacturers and other industries seeking to understand their ecological footprint.
Sector-specific benchmarks are another valuable resource, particularly for estimating Scope 3 emissions. Organisations like the Carbon Trust provide standardised data for frequently used business items, helping companies establish consistent baselines. Financial data providers, including MSCI and Sustainalytics, also compile ESG metrics, allowing businesses to compare their performance against industry peers.
When direct supplier data isn't available, supply chain reporting often leans on secondary data. Tools like the Understanding Scope 3 emissions resource can guide organisations in applying standard conversion factors to estimate indirect emissions reliably.
Advantages of Using Secondary Data
Using secondary data can save time and money by offering pre-compiled datasets, reducing the need for extensive primary data collection. This efficiency makes it a practical choice for many organisations.
Another benefit is its ability to provide comprehensive coverage across an entire value chain. Secondary data promotes consistency in reporting, which is useful for internal benchmarking and meeting regulatory requirements. By combining secondary data with primary sources, businesses can enhance transparency and improve their audit readiness. However, these advantages come with some limitations.
Limitations of Secondary Data
Despite its benefits, secondary data has its drawbacks, which can affect the accuracy of ESG reporting. For example, relying on industry averages may not reflect an organisation's unique performance, potentially misrepresenting its environmental impact.
Geographic differences also present a challenge. Broad averages, whether national or global, may fail to account for regional variations in energy sources or industrial practices. Additionally, secondary data often lags behind current developments, meaning it may not capture recent sustainability improvements.
Other limitations include the inability to detail specific operational nuances and the difficulty of creating a clear audit trail for external data sources. Secondary data also struggles to account for cutting-edge innovations or new sustainable technologies.
Platforms designed for ISSB reporting can help address these issues by blending secondary and primary data. While secondary data is a valuable resource, understanding its limitations is critical for ensuring accurate and transparent ESG reporting. Selecting the right platform can make a significant difference in maintaining data quality and credibility.
What is ESG Data and how to use it?
Primary vs Secondary Data Comparison
Choosing between primary and secondary data comes down to how the data is gathered. Primary data is collected directly from specific operations, offering detailed and tailored insights, while secondary data relies on general industry benchmarks and averages. Understanding these distinctions is key to improving ESG reporting practices.
Comparison Table
Factor | Primary Data | Secondary Data |
|---|---|---|
Data Source | Collected directly from actual operations | Derived from estimates and industry averages |
Measurement Accuracy | Highly precise, specific to the operation | Moderate accuracy, based on general benchmarks |
Timeliness | Real-time or near real-time collection | Often historical, reflecting past conditions |
Cost & Resources | High initial investment and ongoing maintenance | Lower cost, easily accessible |
Audit Trail | Clear and traceable from source to report | Limited traceability to original sources |
Geographic Specificity | Location-specific measurements | Broad averages that may not reflect local nuances |
Implementation Speed | Slower, requiring infrastructure | Quick, with immediate access |
Combining Primary and Secondary Data
For most organisations, a mix of primary and secondary data works best. Primary data is ideal for areas where precision is critical, while secondary data can cover gaps where direct collection is impractical or too expensive.
Take, for instance, a manufacturing company: it might gather primary data to track its direct energy use and waste output. Meanwhile, secondary data could be used to estimate emissions from its supply chain. This combination allows businesses to balance accuracy with cost-efficiency, ensuring a more comprehensive ESG report.
ESG Reporting Platform Requirements
ESG platforms need to handle both primary and secondary data while adhering to various reporting frameworks. This involves managing a mix of data sources, from real-time sensor outputs to industry benchmarks, in a way that ensures accuracy and coherence. The goal is to create a unified system that connects operational insights with industry standards, building on the earlier discussion of primary and secondary data.
Key Features of ESG Platforms
Data centralisation is a fundamental requirement for effective ESG platforms. Instead of juggling spreadsheets across departments, organisations need systems capable of pulling data from sources like accounting software, energy meters, HR systems, and external databases. By centralising this information, organisations ensure consistent and reliable reporting across all outputs.
AI-driven automation simplifies the handling of complex ESG data. For instance, automated tools can map financial transactions to sustainability impacts, reducing errors and saving time. Imagine a company purchasing electricity - an intelligent system can calculate the associated carbon emissions automatically, using the correct grid factors for the location.
Multi-framework compliance is another critical feature. Organisations often need to report under various standards such as ISSB (IFRS S1 & S2), CSRD, and GHGP. A strong platform supports these frameworks simultaneously, formatting data to meet each standard's unique requirements without duplicating effort.
For example, neoeco integrates sustainability factors directly into financial transactions, ensuring the same level of accuracy as traditional financial records. By embedding over 90 ESG impact factors into double-entry accounting, platforms like this bridge the gap between finance and sustainability teams.
Life Cycle Assessment (LCA) integration enhances carbon accounting by using science-based methodologies. This ensures that primary operational data and secondary supply chain information align, maintaining consistency across data sources and offering a more comprehensive view of emissions.
Real-time reporting capabilities allow organisations to track ESG performance continuously. This is especially useful when working with primary data from IoT sensors or smart meters, enabling immediate insights that can drive operational improvements rather than waiting for periodic assessments.
These features collectively address the challenge of combining precise primary data with adaptable secondary benchmarks, ensuring thorough and accurate ESG reporting.
Beyond automation and centralisation, maintaining data quality and transparency is key to credible ESG reporting.
Ensuring Data Quality and Transparency
Supplier engagement plays a vital role in ensuring data quality, particularly when gathering primary data from the supply chain. Clear data collection protocols, along with training for suppliers on measurement methods, are essential. Regular audits of supplier data can catch inconsistencies early, maintaining the credibility of the overall report.
Regular validation processes should be built into the platform. Automated checks can flag anomalies, like unusually high energy consumption or missing data, while manual reviews focus on critical data points that significantly affect ESG scores.
Documentation of data sources is crucial for creating an audit trail. Every data point should include metadata detailing its source, collection method, and any transformations applied. This level of transparency is especially important when integrating primary and secondary data.
Version control and change tracking ensure that any updates to data or methodologies are properly recorded. Whether switching from secondary to primary data or updating emission factors, the platform should log what changed, when, and why.
Access controls and approval workflows safeguard data integrity. Only authorised personnel should be able to modify critical data points, which is particularly important for primary data collection. Errors, such as incorrect sensor calibrations, can propagate across the system if not managed carefully.
The best ESG platforms weave these quality assurance measures into their workflows, making it easier for teams to uphold high standards without adding unnecessary administrative tasks. When selecting an ESG platform, organisations should look for solutions that make managing data quality straightforward and intuitive, rather than treating it as an afterthought.
Conclusion
Choosing between primary and secondary data for ESG reporting involves finding the right balance based on your organisation's needs and reporting goals. Primary data delivers precise and transparent insights, especially for critical emission sources, while secondary data fills the gaps where direct measurement isn't practical or cost-effective.
This balance is crucial across all emissions scopes. For example, prioritise primary data for Scope 1 and key Scope 2 emissions, while blending it with secondary benchmarks for Scope 3 to create a comprehensive and practical approach.
Relying too heavily on one type of data can either strain resources or compromise credibility. To avoid these pitfalls, establish robust data quality checks and validation processes to ensure both accuracy and reliability.
Modern ESG platforms can help simplify this complexity. Tools like neoeco's financially-integrated sustainability platform combine primary and secondary data using AI automation and LCA methodologies, enabling compliance with multiple frameworks.
Looking ahead, automation will play a key role in ESG reporting. Platforms that seamlessly integrate real-time sensor data with industry benchmarks can help maintain data quality, enhance transparency, and meet ever-changing regulatory demands.
Ultimately, effective ESG reporting relies on tools that streamline data management, empowering organisations to focus on sustainability improvements while meeting stakeholder expectations.
FAQs
How can organisations balance primary and secondary data in ESG reporting to ensure accuracy while managing costs effectively?
Organisations can refine their ESG reporting by focusing on primary data for key areas where precision is paramount - think direct emissions or site-specific environmental metrics. For other areas where exactness is less critical, secondary data like industry averages or third-party datasets can offer a cost-effective alternative without significantly compromising accuracy.
Tools such as neoeco simplify this entire process by automating tasks like data collection, validation, and integration. This not only ensures ESG disclosures are audit-ready and aligned with global standards but also cuts down on the manual workload, making the process more efficient.
What challenges arise when relying only on secondary data for ESG reporting, and how can they be addressed?
Relying only on secondary data for ESG reporting can create problems like inconsistent or incomplete information. Since this data often comes from a mix of sources, it might not offer the level of detail needed for precise analysis. This can compromise data reliability, make it tougher to meet reporting standards, and increase the risk of issues like greenwashing.
To tackle these problems, organisations can put robust data validation processes in place, stick to standard ESG frameworks, and leverage advanced tools such as neoeco. With AI-driven automation and Life Cycle Assessment (LCA) methodologies, platforms like neoeco improve data accuracy, deliver real-time insights, and ensure alignment with global standards like ISSB and CSRD.
How do ESG platforms help integrate and manage primary and secondary data to ensure accurate reporting and compliance?
ESG platforms simplify the handling of primary data - like energy meter readings or employee survey results - and secondary data from external sources by automating how this information is collected and validated. This reduces the chance of human error and keeps datasets consistent.
Many of these platforms leverage AI-powered tools and methods, such as Life Cycle Assessment (LCA), to deliver precise, up-to-date insights. By linking systems like ERP platforms with ESG data repositories, they make data sharing seamless and improve readiness for audits. This approach helps organisations meet global standards like ISSB and CSRD, ensuring reporting is both reliable and efficient.