Commentary on issues in data quality analysis in life cycle assessment
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For compliance with the ISO standard 14044, comparative life cycle assessments are required to address data quality for time-related coverage, geographic coverage, technology coverage, precision, completeness, representativeness, consistency, reproducibility, sources of the data and uncertainty of the information. As the community of practitioners and data developers grows, the purpose of this commentary is to initiate discussion of current issues and opportunities for improvement in data quality analysis.
Commonly applied data quality analysis methods are described as ranging from the collection of only qualitative information to the assignment of numeric scores. Common interpretations of data quality information are described as ranging from comparison in raw form to contribution and sensitivity analysis results, combination into an aggregate/multiaspect score, or use to infer data uncertainty. Method strengths and issues are described.
The strengths of current data quality analysis methods lie in the consideration of the data quality aspects specified by the ISO standards and in the differentiation of low and high data quality. Weaknesses, however, lie in unrepeatable scoring criteria, aggregation of data quality information in a way that is difficult to interpret or misinterpreted and the use of data quality information in the estimation of uncertainty with no basis for accuracy.
It is found that among commonly applied methods there exists a need for improved repeatability and interpretability. When combined with emerging efforts to provide reliable uncertainty data to support the use of data quality information with contribution and sensitivity analysis results and efforts that have improved consideration of completeness, the future of data quality analysis promises substantial contribution to the field.
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- Commentary on issues in data quality analysis in life cycle assessment
The International Journal of Life Cycle Assessment
Volume 17, Issue 4 , pp 499-503
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- Data quality
- Data quality analysis
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