ESG Data Intelligence

ESG data is any information that measures a company’s or investment’s performance on environmental, social and governance factors — separately from, but increasingly alongside, its financial data.

Broadly, environmental (emissions, energy, water, waste), social (workforce, safety, supply chain labour practices) and governance (board composition, pay, anti-corruption policies) — each including both quantitative figures and qualitative disclosures.

It’s now core to regulatory reporting, investor due diligence, and supply chain requirements — and its quality is the single biggest reporting obstacle cited by 59% of companies reporting under CSRD or ISSB (PwC Global Sustainability Reporting Survey).

This depends on which frameworks apply and which topics your materiality assessment identifies as significant — there’s no single universal list, though most companies end up collecting emissions, energy, workforce and governance data as a baseline.

Through a mix of internal systems (utility bills, HR records, safety logs), supplier and value-chain requests, third-party data providers, and public disclosures — usually combined rather than relying on just one source.

The specific, measurable units used to track ESG performance — tonnes of CO2e, % of women in leadership, injury frequency rate — as distinct from the underlying data points and policies that don’t reduce to a single number.

Internal operations, suppliers and the wider value chain, third-party ESG databases and rating providers, and companies’ own public disclosures and filings.

The specific figures and disclosures collected under each metric — a facility’s electricity consumption for an energy metric, or a supplier’s certification status for a labour-practices metric, for example.

By tracking metrics against internal targets, prior-year performance, sector benchmarks, or externally set science-based or regulatory thresholds, depending on the topic.

For regulatory reporting, investor and lender due diligence, supply chain risk management, internal target-setting, and increasingly for public benchmarking and ratings.

A clear source, a consistent method for collecting and calculating it, and a traceable history from raw input to reported figure — reliability comes from the process behind the number, not just the number itself.

Through internal checks against source documents and prior periods, and, for external stakeholders, third-party assurance — limited or reasonable, the same tiers used in financial audit.

Frameworks that define which data points to disclose and how — CSRD and the ESRS, GRI, SASB, ISSB, SFDR and the EU Taxonomy are the main ones most companies encounter, each with its own scope and data requirements.

Third-party organisations that collect, score and sell ESG data on companies and investments — useful for benchmarking or due diligence on entities you don’t hold data on directly.

How complete, accurate, consistent and current a data set is — the dimensions that determine whether a figure can be trusted and compared over time.

Independent, third-party verification that reported ESG data is accurate — increasingly requested by regulators and investors, using the same limited/reasonable assurance tiers as financial audit.

Fragmented sources and inconsistent formats top the list. Around 70% of companies say they don’t have enough supplier data to calculate their supply chain emissions accurately (MIT Center for Transportation & Logistics), and just over half still rely on spreadsheets for at least part of their ESG data (EY/FERF survey).