<abstract xmlns="http://www.w3.org/1999/xhtml"><sec><h3>Purpose</h3><p>This paper relates the definition of data quality procedures for knowledge organizations such as Higher Education Institutions. The main purpose is to present the flexible approach developed for monitoring the data quality of the European Tertiary Education Register (ETER) database, illustrating its functioning and highlighting the main challenges that still have to be faced in this domain.</p></sec><sec><h3>Design/methodology/approach</h3><p>The proposed data quality methodology is based on two kinds of checks, one to assess the consistency of cross-sectional data and the other to evaluate the stability of multiannual data. This methodology has an operational and empirical orientation. This means that the proposed checks do not assume any theoretical distribution for the determination of the threshold parameters that identify potential outliers, inconsistencies, and errors in the data.</p></sec><sec><h3>Findings</h3><p>We show that the proposed cross-sectional checks and multiannual checks are helpful to identify outliers, extreme observations and to detect ontological inconsistencies not described in the available meta-data. For this reason, they may be a useful complement to integrate the processing of the available information.</p></sec><sec><h3>Research limitations</h3><p>The coverage of the study is limited to European Higher Education Institutions. The cross-sectional and multiannual checks are not yet completely integrated.</p></sec><sec><h3>Practical implications</h3><p>The consideration of the quality of the available data and information is important to enhance data quality-aware empirical investigations, highlighting problems, and areas where to invest for improving the coverage and interoperability of data in future data collection initiatives.</p></sec><sec><h3>Originality/value</h3><p>The data-driven quality checks proposed in this paper may be useful as a reference for building and monitoring the data quality of new databases or of existing databases available for other countries or systems characterized by high heterogeneity and complexity of the units of analysis without relying on pre-specified theoretical distributions.</p></sec></abstract>

PurposeThis paper relates the definition of data quality procedures for knowledge organizations such as Higher Education Institutions. The main purpose is to present the flexible approach developed for monitoring the data quality of the European Tertiary Education Register (ETER) database, illustrating its functioning and highlighting the main challenges that still have to be faced in this domain.Design/methodology/approachThe proposed data quality methodology is based on two kinds of checks, one to assess the consistency of cross-sectional data and the other to evaluate the stability of multiannual data. This methodology has an operational and empirical orientation. This means that the proposed checks do not assume any theoretical distribution for the determination of the threshold parameters that identify potential outliers, inconsistencies, and errors in the data.FindingsWe show that the proposed cross-sectional checks and multiannual checks are helpful to identify outliers, extreme observations and to detect ontological inconsistencies not described in the available meta-data. For this reason, they may be a useful complement to integrate the processing of the available information.Research limitationsThe coverage of the study is limited to European Higher Education Institutions. The cross-sectional and multiannual checks are not yet completely integrated.Practical implicationsThe consideration of the quality of the available data and information is important to enhance data quality-aware empirical investigations, highlighting problems, and areas where to invest for improving the coverage and interoperability of data in future data collection initiatives.Originality/valueThe data-driven quality checks proposed in this paper may be useful as a reference for building and monitoring the data quality of new databases or of existing databases available for other countries or systems characterized by high heterogeneity and complexity of the units of analysis without relying on pre-specified theoretical distributions.

A Tailor-made Data Quality Approach for Higher Educational Data

Institute for Economic and Innovation Research

Journal of Data and Information Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"A Tailor-made Data Quality Approach for Higher Educational Data"}

PurposeThis paper relates the definition of data quality procedures for knowledge organizations such as Higher Education Institutions. The main purpose is to...

A Tailor-made Data Quality Approach for Higher Educational Data

Article Category: Research Paper

Online veröffentlicht: 09. Juli 2020

Seitenbereich: 129 - 160

Eingereicht: 08. Feb. 2020

Akzeptiert: 27. Mai 2020

DOI: https://doi.org/10.2478/jdis-2020-0029

Schlüsselwörter
Knowledge organization, Development of data and information services, Cross-sectional and multiannual quality checks, Higher education institutions, Information quality

© 2020 Cinzia Daraio et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Tailor-made Data Quality Approach for Higher Educational Data

Article Category: Research Paper

Online veröffentlicht: 09. Juli 2020

Seitenbereich: 129 - 160

Eingereicht: 08. Feb. 2020

Akzeptiert: 27. Mai 2020

DOI: https://doi.org/10.2478/jdis-2020-0029

SchlüsselwörterKnowledge organization, Development of data and information services, Cross-sectional and multiannual quality checks, Higher education institutions, Information quality

© 2020 Cinzia Daraio et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Knowledge organization, Development of data and information services, Cross-sectional and multiannual quality checks, Higher education institutions, Information quality