Data quality indicators

Caroline Visentin

Chapter 38 in Elgar Encyclopedia of Life Cycle Sustainability Assessment, 2026, pp 129-132 from Edward Elgar Publishing

Abstract: Data quality indicators include completeness, accuracy, consistency, timeliness, relevance, and validity. Poor-quality data can lead to incorrect conclusions and flawed decision-making. Data quality indicators play a critical role in ensuring the accuracy and reliability of life cycle sustainability assessment results. Without these indicators, organizations risk making decisions based on inaccurate or incomplete information. The aim of this entry is to present the concepts of data quality indicators, describe the main indicators of data quality, and also demonstrate the relationship between data quality indicators and the analysis of life cycle sustainability assessment.

Keywords: Completeness; Data Quality Management; Life Cycle Sustainability Assessment (LCSA); Decision-Making; Data Reliability; Quality Metrics Framework (search for similar items in EconPapers)
Date: 2026
ISBN: 9781035309887
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