ESG data: A comprehensive guide to streamline ESG research and assessments

Over the past few years, environmental, social, and governance (ESG) data has moved from being a niche topic to a core consideration in the business and investment world.

A recent 2024 survey by Deloitte and The Fletcher School at Tufts University found that 83% of investors now include sustainability information in core investment decisions.

This has made robust transparent sustainability reporting more important than ever. It’s not just about ‘check the box’ regulatory compliance anymore—it’s about building trust with investors by identifying risks and opportunities, and clearly communicating how an organization will achieve its sustainability goals

But as demand grows and regulations shift, consultants, financial institutions, and regulators need to streamline their assessments and processes to ensure continued efficiency, accuracy, and alignment with evolving expectations.

What is ESG data?

ESG data is the information that exists in a company’s disclosures, which is organized into three key areas: environmental impact, social responsibility, and governance. In other words, it gathers fragmented information on a company’s sustainability initiatives and creates comparable, decision-useful information.

This information is categorized into one of two types:

Quantitative data refers to measurable metrics, such as carbon emissions, energy consumption, and workforce diversity numbers.
Qualitative data encompasses narrative-driven insights like corporate ethics, governance policies, and the overall company culture.

ESG data can come from both internal and external sources. Let’s break it down:

Internal ESG data sources

Operational records: Data about a company’s energy usage, water consumption, and waste generation. It’s tracked through utility bills or facility management systems.
Human resources systems: Information on workforce diversity, employee turnover, training programs, and labor practices. It’s typically stored in HR databases.
Governance documentation: Internal policies, executive compensation details, and ethics guidelines, often housed in corporate governance archives.
Sustainability reports: Quarterly or annual reports compiled by the company detailing ESG initiatives, progress, and performance metrics.
Risk management logs: Records of ESG-related risks, such as compliance breaches or environmental incidents. These are maintained by risk or legal departments.

External ESG data sources

Third-party data providers: Platforms like Bloomberg, Refinitiv, and MSCI offer ESG ratings and benchmarks based on public disclosures and proprietary data.
Regulatory filings: Mandatory submissions to bodies like the SEC or E.U. regulators, including climate risk disclosures or governance structures.
Industry benchmarks: Reports from trade associations or frameworks like the Global Reporting Initiative (GRI) that offer comparative ESG performance metrics across sectors.
Stakeholder feedback: Input from customers, suppliers, or communities, often gathered through surveys or public forums.
NGO and media reports: Independent assessments of a company’s ESG impact, such as carbon footprint analyses, published by environmental groups or journalists.

The importance of ESG data in assessing ESG risks and opportunities

The growing need for accurate and timely ESG data is driven by its role in assessing risks and opportunities. Regulators, investors, and stakeholders use this data to assess a company’s ability to manage environmental, social, and governance challenges while uncovering areas for growth.

Here’s how ESG data is commonly used:

Assessing reporting compliance: Ensures companies meet standards like the Corporate Sustainability Reporting Directive (CSRD) or GRI frameworks.
Engaging with stakeholders, portfolio companies, and suppliers: ESG data fosters better communication with key stakeholders, helping align sustainability goals.
Conducting underwriting and credit risk assessments: Banks and financial institutions increasingly use ESG data to assess the creditworthiness of businesses.
Measuring and tracking ESG performance: Organizations use ESG data to track how well their sustainability strategies are working and make adjustments as necessary.

Types of ESG data

Understanding the different types of ESG data is essential for anyone involved in assessing and reporting on sustainability performance. ESG data falls into four main categories: environmental, social, governance, and financial.

Environmental

This category focuses on a company’s impact on the planet. Some common examples include:

Carbon emissions: Hard numbers on the greenhouse gases a company emits, like CO2 and methane.
Energy consumption: Details about how much energy a company uses, where it comes from, and its efforts to reduce reliance on non-renewable energy sources.
Water usage: Tracking the water consumption of a business. It’s particularly important for industries like manufacturing or agriculture.
Waste management: Metrics that report how much waste a company generates and its efforts to reduce, recycle, or properly dispose of waste.
Climate risk assessments: Evaluations of how climate change may impact operations and supply chains.

Social data reflects how a company engages with its employees, customers, and the broader community. Key areas include:

Workforce diversity: Tracks the gender, racial, and ethnic diversity within the company, both at the executive level and throughout the workforce.
Labor practices: Cover workers’ rights, working conditions, and health and safety standards.
Community engagement: How a company contributes to and interacts with the communities where it operates.
Human rights policies: Companies are increasingly expected to uphold human rights within their operations and supply chains.
Employee well-being: Data related to health benefits, work-life balance, and overall employee satisfaction.

Governance

Governance data looks at how well a company is managed and whether it follows ethical practices. Typical metrics include:

Board diversity: The demographic makeup of the company’s board of directors.
Executive compensation: Analysis of how executive pay aligns with company performance, often scrutinized by investors.
Ethics policies: Rules and guidelines governing how a company handles issues like corruption, fraud, and conflicts of interest.
Regulatory compliance: Information on a company’s adherence to laws and regulations related to ESG.
Risk management practices: How a company identifies and manages ESG-related risks, like environmental or reputational threats.

Financial

Financial ESG data links a company’s ESG initiatives with its financial performance. It includes:

Sustainable investment disclosures: Information on investments made in green energy, sustainable products, or other eco-friendly initiatives.
ESG-linked financial performance: Insights into how sustainability efforts affect a company’s bottom line, such as through revenue growth or cost savings.
Economic impact of ESG initiatives: How a company’s ESG initiatives create long-term value—for example, by reducing operational costs or attracting socially responsible investors.

Common challenges in ESG data management

Managing ESG data presents a complex set of hurdles for consultants and businesses alike. From regulatory disparities to data silos, these challenges can undermine their ability to deliver cohesive, reliable ESG insights.

Lack of standardization in regulatory requirements

The variety of ESG frameworks (GRI, SASB, TCFD, and CSRD) creates a fragmented reporting environment. Each standard has its own set of metrics and requirements, making it hard to build a system that works across all standards.

The key issue here is limited interoperability—the ability for different systems to work together smoothly. While it’s essential for efficient ESG reporting, it remains elusive when requirements diverge.

For instance, the CSRD takes a broad, double-materiality approach. It mandates disclosures on environmental, social, and governance factors, considering the company’s impact and its influence on society. In contrast, the ISSB focuses on providing consistent, comparable, and reliable financial disclosures related to a company’s sustainability-related risks.

These divergent focuses mean that when consultants, regulators, and financial institutions tailor their reporting to meet one standard, they may inadvertently fall short on another, leading to incomplete or inconsistent disclosures.

Until we see greater harmonization, businesses will continue navigating a complex web of overlapping regulations.

Varying data sources

Gathering ESG data often means pulling from multiple departments and tools or systems within a company, as well as external sources like suppliers and stakeholders. Different departments might use different systems to track ESG-related data, making it challenging to get a clear, unified picture.

For instance, a company’s sustainability team might report on emissions in one way, while the operations team uses a completely different system. On top of that, external sources like suppliers might not provide standardized data, further complicating the process.

These inconsistencies can slow down assessments and erode confidence in the final reports. Consultants and regulators often spend a lot of time reconciling disparate datapoints, verifying their accuracy, and filling in any missing information.

Without a unified approach to data collection across all sources, assessors risk misrepresenting their client’s ESG performance, undermining stakeholder trust, and exposing them to regulatory scrutiny.

Ensuring data accuracy

As ESG data becomes more central to investment decisions and regulatory compliance, ensuring data accuracy is critical. Any errors or inconsistencies can lead to faulty strategies or legal challenges.

For financial institutions, regulators, and consultants, the challenge lies in verifying the integrity of ESG data, mitigating self-reported biases, and aligning it with the requirements of diverse reporting frameworks.

Verifying ESG data integrity begins with establishing robust validation processes. Self-reported data, while valuable, often carries inherent risks of bias or exaggeration, particularly when companies have a vested interest in presenting a favorable ESG profile.

For instance, a firm might underreport carbon emissions or overstate diversity initiatives to enhance its sustainability credentials. To counter this, ESG assessors must cross-reference internal data with external sources (such as third-party audits, satellite imagery for emissions tracking, or public records) to confirm accuracy.

No source of truth for ESG information

For many companies, ESG data exists in multiple systems—HR databases for diversity metrics, operational spreadsheets for emissions, and facility management logs for energy usage. Without a central data repository or integrated solution, pulling together all this information into a cohesive, reliable report can be a huge challenge.

This data often extends beyond internal systems to third-party platforms, supplier disclosures, and regulatory filings, further complicating integration. Piecing together these fragmented sources demands significant effort to align inconsistent formats and metrics.

Source-traceable data is crucial to address this challenge. Knowing precisely where information originates enables consultants to verify accuracy and ensure consistency. Traceability clarifies which disclosures house specific data points, enhancing trust and simplifying the reporting process.

How do companies collect ESG data?

Companies gather ESG data using a combination of internal tracking, surveys, third-party sources, and AI-powered analysis. These methods provide a comprehensive view of sustainability performance, regulatory compliance, and risk management:

Internal data collection: Businesses track ESG metrics through operational, HR, and governance systems. This includes energy usage from utility records, emissions from carbon accounting tools, workforce diversity from HR databases, and governance policies from corporate archives. Compliance teams also log ESG-related risks and incidents.
Surveys & questionnaires: Companies gather qualitative and quantitative insights from employees, suppliers, and stakeholders. Employee feedback informs workplace culture assessments, supplier input helps evaluate ESG compliance, and stakeholder perspectives provide a broader view of social impact.
External data sources: Third-party providers like MSCI offer ESG ratings and emissions benchmarks, while regulatory filings ensure transparency. Industry frameworks like CSRD help companies compare their performance against peers.
AI-powered ESG analysis: AI tools streamline ESG data processing by extracting insights from reports, disclosures, and unstructured sources like news and social media. They enhance reporting accuracy, assess compliance with global standards (e.g., IFRS S1 & S2, CSRD), and support financial institutions in credit risk and investment assessments.

Best practices for effective ESG data collection and reporting

To tackle ESG data challenges, consultants, regulators, and financial institutions can adopt proven strategies that enhance efficiency and reliability. By combining technology, stakeholder collaboration, and standardized processes, these best practices help organizations create more impactful ESG outcomes.

1. Centralize ESG data sources

Bringing ESG disclosures into one central platform can work wonders for reducing data fragmentation and speeding up reporting.

Rather than dealing with scattered information from operations, HR, and external providers, companies can funnel all their ESG-related data into one accessible hub. This makes it easier to manage, minimizing inconsistencies and saving time when aligning datasets.

AI-driven powered solutions like Manifest Climate enhance this process by extracting actionable insights from complex documents such as sustainability reports or regulatory filings. Our tool can automate data organization, identify trends, and provide a clearer picture of ESG performance, delivering accurate, consistent, and dependable ESG information that companies can trust for decision-making.

2. Ensure data accuracy and comparability

Reliable ESG assessments depend on accurate, comparable data across different standards and stakeholders. Expert-validated AI models play a vital role here, aligning information from various frameworks into a cohesive format.

These AI models eliminate inconsistencies—whether it’s differing emissions calculations or diversity reporting methods—giving businesses and consultants the confidence to benchmark performance.

Plus, source-traceable data practices add transparency by documenting where each data point comes from, whether it’s from internal systems, suppliers, or third-party providers. This traceability not only builds trust but also simplifies audits, ensuring ESG reports hold up under scrutiny from regulators and investors.

3. Involve and inform key ESG data stakeholders

Getting internal and external stakeholders involved is essential for accurate and well-rounded ESG reporting.

Internally, executives provide strategic oversight, ensuring ESG priorities align with business goals, while compliance teams verify adherence to regulations like the CSRD or California’s SB 253 and SB 261 mandates.

Sustainability officers contribute granular insights from operational data, bridging gaps between policy and practice. Externally, investors want transparency to assess potential risks and opportunities, driving the need for consistent, reliable data.

By engaging these groups early and keeping them in the loop with regular updates or collaborative platforms, consultants and regulators can resolve discrepancies and produce ESG reports that present a unified, credible narrative.

4. Strengthen ESG data engagement and awareness

Raising ESG awareness across the organization is key to embedding sustainability in the heart of business operations.

Training programs tailored for employees at all levels (covering ESG basics, data collection methods, and regulatory impacts) empower staff to contribute accurate data and align with company goals. Regular updates on ESG performance, shared via dashboards or reports, keep teams informed about progress, spotlighting successes and areas for improvement.

By fostering a culture of sustainability and accountability, businesses can turn ESG from a compliance task into a shared mission. With clearly defined roles, incentives tied to meeting ESG goals, and open conversations about challenges, companies boost both data quality and employee engagement.

5. Automate ESG data analysis with AI

AI-powered solutions are transforming how businesses handle ESG data analysis, making everything more efficient. These tools automate the extraction and processing of data from reports, filings, and unstructured sources, slashing the time spent on ESG research and tedious tasks like data entry or reconciliation.

Beyond just saving time, AI ensures companies stay compliant with ESG regulations by automatically mapping metrics to the required standards and flagging any gaps in real time.

Even more valuable, AI can spot risks and opportunities related to climate and ESG issues by analyzing patterns in the data and highlighting vulnerabilities (like supply chain emissions) or potential wins (such as investments in green technologies).

This kind of automation allows companies to act faster, stay compliant, and leverage ESG insights for strategic advantage.

6. Enhance ESG stakeholder confidence

AI-generated insights help businesses go beyond basic compliance, opening up opportunities for greater sustainability while also building stronger trust with stakeholders.

AI analyzes vast datasets to identify actionable prospects, such as energy efficiency gains or ethical supply chain enhancements, that drive long-term value. It also mitigates greenwashing risks by validating claims with transparent, data-backed evidence, ensuring that what companies promise aligns with actual performance.

This precision boosts stakeholder confidence. Investors trust the data behind risk-adjusted returns, regulators see a credible commitment to compliance, and communities can rely on authentic, positive impact.

Moving past box-ticking, AI empowers businesses to showcase genuine sustainability commitment, fostering credibility and deeper stakeholder alignment.

Accelerate ESG research and analysis with Manifest Climate

ESG data is the backbone of modern sustainability strategies, enabling organizations to navigate regulatory complexity, assess risks, and uncover opportunities. From gathering relevant ESG information to tackling data analysis challenges, the journey demands precision, collaboration, and innovative tools.

Manifest Climate supercharges this process with AI-powered solutions that centralize data and ensure accuracy, empowering consultants to streamline assessments and reporting. Our platform extracts, analyzes, and benchmarks ESG and climate data, providing comparable, source-traceable information for data-backed investment decisions.

To learn more about how Manifest Climate can support your ESG data assessment and reporting needs, book a demo today!