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Annual Review of Statistics and Its Application

Volume 4, 2017

The Evolution of Data Quality: Understanding the Transdisciplinary Origins of Data Quality Concepts and Approaches

Abstract

Data, and hence data quality, transcend all boundaries of science, commerce, engineering, medicine, public health, and policy. Data quality has historically been addressed by controlling the measurement processes, controlling the data collection processes, and through data ownership. For many data sources being leveraged into data science, this approach to data quality may be challenged. To understand that challenge, a historical and disciplinary perspective on data quality, highlighting the evolution and convergence of data concepts and applications, is presented.

Keyword(s): administrative data, decision theoretic framework, designed data, opportunity data, reproducibility, total survey error
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2017-03-07
2024-12-03
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The Evolution of Data Quality: Understanding the Transdisciplinary Origins of Data Quality Concepts and Approaches
Annual Review of Statistics and Its Application 4, 85 (2017); https://doi.org/10.1146/annurev-statistics-060116-054114
/content/journals/10.1146/annurev-statistics-060116-054114
/content/journals/10.1146/annurev-statistics-060116-054114
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