Types of Sustainability Data

Sustainability data is most commonly organised into three broad categories, mirroring the three pillars of ESG: environmental, social, and governance. Environmental data captures an organisation’s impact on the natural world, including greenhouse gas emissions, energy consumption, water use, waste generation, pollution, land use, and biodiversity impact. Social data captures the organisation’s impact on people, including workforce data, health and safety records, diversity and inclusion metrics, employee wellbeing, community investment, human rights, and supply chain labour standards. Governance data captures how the organisation is led and held accountable, including board composition, executive pay, anti-corruption measures, ethics, risk management, and compliance. Together, these three categories provide a comprehensive picture of how responsibly an organisation operates (Datarade, 2024).

Within these categories, sustainability data can be classified in several ways that determine how it is collected, analysed, and used. The first distinction is between quantitative and qualitative data. Quantitative data is numerical and measurable, tones of CO2 emitted, percentage of women on the board, number of health and safety incidents, litres of water consumed. Qualitative data is descriptive, stakeholder feedback, accounts of community engagement, descriptions of governance processes, narrative explanations of how a policy is applied. Both are essential, quantitative data shows what is happening, while qualitative data often explains why. The comparison between the two is explored in detail later in this learning cycle.

A second distinction is between primary and secondary data. Primary data is collected directly by the organisation for its own purposes, meter readings, employee surveys, internal audits, supplier questionnaires. Secondary data is collected by someone else and used by the organisation, published emissions factors, industry benchmarks, government statistics, third-party ESG ratings. Primary data is usually more accurate and specific to the organisation, but more costly to collect, secondary data is cheaper and faster to access but may be less precise or less current.

A third distinction concerns what the data actually measures. Activity data records what an organisation does, the number of training sessions delivered, the volume of materials purchased, the kilometres travelled. Outcome or impact data records the effect of those activities, the reduction in emissions achieved, the improvement in employee wellbeing, and the change in community health. Activity data is easier to collect but tells you little about whether the activity made a difference; impact data is harder to measure but is what genuinely matters for assessing CR&S performance. Related to this is the distinction between leading indicators, which signal future performance (such as the percentage of suppliers audited), and lagging indicators, which measure past results (such as the number of supply chain violations found).

A particularly important category of environmental data is greenhouse gas emissions data, which is conventionally divided into three scopes. Scope 1 covers direct emissions from sources the organisation owns or controls, such as company vehicles and on-site fuel combustion. Scope 2 covers indirect emissions from purchased energy, such as electricity. Scope 3 covers all other indirect emissions across the organisation’s value chain, including purchased goods and services, business travel, and the use of sold products. Scope 3 is typically the largest and most difficult category to measure, because it depends on data from suppliers, customers, and other third parties (Sawant et al., 2025). Understanding the three scopes is fundamental to environmental data, and apprentices will encounter them throughout their CR&S work.

Finally, sustainability data can be structured or unstructured. Structured data is organised in a defined format, spreadsheets, databases, standardised reporting templates. Unstructured data has no predefined format, emails, photographs, free-text survey responses, meeting notes, and social media content. A growing proportion of useful sustainability data is unstructured, which makes it harder to analyse systematically but often rich in insight.

Across all of these classifications, the central principle is the same: different types of data serve different purposes, and a credible CR&S practitioner understands what each type can and cannot tell them. The activities that follow in this learning cycle build on this foundation, exploring where data comes from, how it is collected and stored, how qualitative and quantitative data compare, and how data is used to report CR&S performance against objectives.