Data Quality Score
A data quality score is a rating assigned to emissions data that reflects its accuracy, specificity, and reliability. It helps organisations identify where estimates should be improved with better data and demonstrates to stakeholders the rigour of the GHG inventory.
What is Data Quality Score?
Data quality scoring is a systematic way to assess and communicate the reliability of the data underpinning a carbon footprint. Because GHG inventories rely on a mix of measured data, modelled estimates, and proxies — especially for Scope 3 — it is important to be transparent about where the data is strong and where it is uncertain.
The GHG Protocol Corporate Value Chain Standard provides a data quality assessment framework that evaluates each data point across several dimensions. The PACT (Partnership for Carbon Transparency) framework and the SBTi also reference data quality indicators. Common dimensions include: representativeness (does the data reflect the actual activity?), completeness (does it cover all relevant sources?), reliability (is the source trustworthy and verified?), temporal relevance (is the data from the correct time period?), and geographic relevance (does the factor match the location of the activity?).
A typical scoring system uses a 1–5 scale: 1 represents supplier-specific verified data (highest quality), 2 represents activity-based data with specific emission factors, 3 represents activity-based data with average emission factors, 4 represents spend-based estimates with sector-average factors, and 5 represents rough estimates or extrapolations (lowest quality). Each data point or category in the inventory is scored, and the overall inventory quality can be expressed as a weighted average.
Data quality scores serve multiple purposes. They help the organisation prioritise where to invest in better data collection (focus on the largest emissions sources with the worst scores). They satisfy reporting framework requirements — CDP asks about data quality methodology, and the CSRD requires disclosure of data limitations. And they provide stakeholders with appropriate context for interpreting the numbers — a Scope 3 figure based on supplier-specific data warrants more confidence than one based on spend estimates.
Improving data quality is an iterative process. Most organisations start with predominantly spend-based estimates (scores of 4–5) and progressively improve towards activity-based (3) and supplier-specific (1–2) data for their most material categories over successive reporting cycles.
Practical Examples
A company assigns data quality scores to each Scope 3 category: Category 6 (business travel) scores 2 (activity-based with specific flight data), Category 1 (purchased goods) scores 4 (spend-based), and Category 7 (commuting) scores 3 (employee survey data with average factors).
An organisation reports a weighted-average data quality score of 3.2 across its Scope 3 inventory, with an improvement target to reach 2.5 within three years by engaging key suppliers for primary data.
A CDP submission includes a data quality matrix showing each emission source, its score, and the planned improvement actions — demonstrating a commitment to progressively better measurement.
How Climatise Helps
Climatise automatically assigns data quality scores to each emission source based on the methodology and data type used. The platform provides a visual data quality dashboard that highlights areas for improvement and tracks your progress towards higher-quality measurement across successive reporting periods.
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