Methodologies for data quality assessment and improvement

By Carlo Batini, Cinzia Cappiello, Chiara Francalanci, Andrea Maurino. CSUR, 2009

1,348 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/1a8f5de43ab7b266b9edd921c8f181221dada881

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In Information Systems

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In Management Science and Operations Research

Citations in the corpus

Automating Large-Scale Data Quality Verification (2018, background): “The quality of data can refer to the extension of the data (i.e., data values), or to the intension of the data (i.e., the schema) [4].”
Quality assessment for Linked Data: A Survey (2015, background): “We would like to point out to the readers that a comprehensive survey done by Batini et al. [5] already exists which focuses on data quality measures for other structured data types.”
Open data quality measurement framework: Definition and application to Open Government Data (2016, methodology): “Batini et al. (2009) collected from the literature an exhaustive list of data quality methodologies, that are reported in Table 1.”
Data-centric Artificial Intelligence: A Survey (2023, neutral): “These metrics can be broadly categorized as either objective or subjective assessments [16, 170, 185, 244].”
Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda (2021, methodology): “Each study was independently assessed by at least two of the authors, using the following quality dimensions derived from Batini, Cappiello, Francalanci, and Maurino (2009) and (Bano & Zowghi, 2015): • Accuracy: the objectives of the study are clearly stated, and data collection methods are…”
Linked data quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO (2017, background): “[6] distinguish syntactic and semantic accuracy: Syntactic accuracy describes the formal compliance to syntactic rules without reviewing whether the value reflects the reality.”
A Survey on Data Quality: Classifying Poor Data (2015, background): “Authors in [44] survey an ample set of areas in data quality and also analyze work that defines data quality dimensions, despite not being the the main focus of the work.”
A Review of Data Quality Assessment Methods for Public Health Information Systems (2014, methodology): “A comprehensive data quality assessment method includes a set of guidelines and techniques that defines a rational process to assess data quality [37].”
Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook (2020, background): “…can be found in the literature to improve speciﬁc dimensions of data quality [93], the assessment and measurement of these dimensions have historically relied on self-report surveys and user questionnaires [94] due to their association with subjective and situational judgements for quantiﬁcation.”
Data quality: A survey of data quality dimensions (2012, methodology): “Despite availability of large variety of techniques for accessing and improving data quality such as business rules, record linkage and similarity measures, due to rise difficulty and multiplicity of using these system, data quality methodology has defined and provided [2].”