Data Quality vs Data Uncertainty
Credible and effective Greenhouse gas reporting requires confidence in the data on which the reporting is based, the two key concepts here are Data Quality and Data Uncertainty.
Data quality and data uncertainty are two terms in greenhouse gas (GHG) reporting that are a source of confusion. ISO 14064-1 requires the reporter to assess uncertainty (Clause 8.3) but is silent on data quality. The GHG Protocol Corporate Standard dedicates a whole chapter to managing inventory quality, including uncertainty. Many reporters struggle to complete quantitative uncertainty assessments and compromise with a qualitative (descriptive) assessment.
Managing inventory quality is critical, as the data needs to be accurate so that the intended users of the information can make decisions with confidence that the reported information is reliable and credible. Documenting and reporting the measures taken to ensure the accuracy of the information reported is key to promoting credibility and enhancing transparency.
In GHG reporting, data quality and data uncertainty play different roles in ensuring the accuracy and reliability of the reported information.
Data Quality
Data quality refers to the overall accuracy, completeness, and reliability of data being reported. Data quality usually covers five dimensions:
Accuracy: How close the data is to the actual value.
Completeness: Whether all necessary data has been collected and reported without any missing information or data gaps.
Consistency: The uniformity of the data over time and across historic reporting periods.
Relevance: How well the data meets the specific needs and objectives of the GHG reporting requirements.
Timeliness: How up-to-date the data is compared to the reporting period.
Ensuring high data quality can involve strict data collection methods, calibration of measurement instruments, and adherence to reporting guidelines.
Data Uncertainty
Data uncertainty deals with the level of confidence regarding the data's accuracy and reliability. It reflects the potential variability and errors in the data due to various factors:
Measurement Error: Variability introduced by the limitations or imperfections in the measurement instruments or methods.
Estimation Error: Uncertainty arising from using estimates or models rather than direct measurements.
Data Gaps: Uncertainty due to missing data or incomplete information.
Assumptions: Uncertainty related to the assumptions made during data collection or calculation processes.
The assessment of data uncertainty involves quantifying the potential errors or variability in the data and understanding how these factors impact the overall reliability of the GHG report. This might involve statistical analysis, sensitivity testing, or scenario analysis to gauge the degree of confidence in the reported figures. However, ISO 14064-1 and the GHG Protocol Corporate Standard allow qualitative uncertainty assessment.
Key Differences
Focus:
Data quality is about the inherent attributes of the data (accuracy, completeness, etc.), while data uncertainty is about the level of confidence surrounding the data.
Assessment Approach:
Data quality is assessed through checks and validations to ensure data meets required standards. Data uncertainty is assessed through analysis of potential errors, variability, and confidence intervals.
Impact on Reporting:
High data quality means the data is reliable and meets reporting requirements. Low data uncertainty means there is high confidence in the data's accuracy, even if the data quality might be good but not perfect.
In summary, data quality ensures that the data is as good as possible, while data uncertainty assesses how much confidence can be placed in that data. Both are crucial for credible and effective GHG reporting.
Feel free to contact us at info@mchugh-shaw.co.nz to discuss your assurance requirements. We have over 15 years of experience and complete ISO 14064-1, GHG Protocol, ISO 14067, Airport Carbon Accreditation, Eco Choice Aotearoa, Product Stewardship and Aotearoa New Zealand Climate Standard assurance.
Last updated August 2024