Data quality assessment

By L. Pipino, Yang W. Lee, Richard Y. Wang. CACM, 2002

865 citations recorded by the source corpus.

Subfields: Epidemiology, Management Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/7dbfe6c89dd7d39ecf5b9aedf192ade98716b778

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Information Systems

1. Carlo Batini
2. Cinzia Cappiello
3. Chiara Francalanci
4. Andrea Maurino

In Management Science and Operations Research

1. Hongwei Zhu
2. Yang W. Lee
3. Stuart E. Madnick
4. Carlo Batini
5. Cinzia Cappiello
6. Chiara Francalanci
7. Richard Y. Wang
8. Andrea Maurino

Citations in the corpus

• Methodologies for data quality assessment and improvement (2009, methodology): “The DQA methodology [Pipino et al. 2002] has been designed to provide the general principles guiding the de.nition of data quality metrics.”
• Overview and Framework for Data and Information Quality Research (2009, methodology): “Theinstrument operationalizes eachdimen­sion into four to .ve measurable items, and appropriate functional forms are appliedto theseitems to score eachdimension[Pipino et al.2002].”
• Data-Centric AI (2024, neutral): “– Data quality assurance (Pipino et al., 2002) that is commonly performed in dynamic environments where continuous monitoring and qualitative improvement are mandatory.”
• Data-centric Artificial Intelligence: A Survey (2023, neutral): “These metrics can be broadly categorized as either objective or subjective assessments [16, 170, 185, 244].”