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STIC-B510

Qualité de l'information et des documents numériques

academic year
2020-2021
The health crisis linked to the spread of Covid-19 constitutes grounds of force majeure constraining the University to adapt the assessment methods for some of the course units; Information on the new terms of these assessments will be provided to students through the usual information channels by Apris 26, 2021.

Course teacher(s)

Isabelle BOYDENS (Coordinator)

ECTS credits

5

Language(s) of instruction

french

Course content

  1. Historical overview, data quality definition ("fitness for use"), practical and epistemological stakes, cost-benefits challenges

  2. Hermeneutics applied to the quality of empirical databases, specification methods of data quality indicators and compromises

  3. Best practices to evaluate and improve data quality based on concrete case studies : organization, anomalies and transactions modeling and monitoring, “data & back tracking”, tools and techniques (data profiling, data standardization and data matching), management strategies, metadata systems and Master Data Management. 

Objectives (and/or specific learning outcomes)

To provide the methodological and practical knowledge to evaluate and improve information systems quality.

Prerequisites

Required courses

Teaching methods and learning activities

Combination of lectures with discussions with the students and practical exercises based on case studies; course taught in French

Contribution to the teaching profile

COMPREHENDING NEW KNOWLEDGE

  • Applying methods and techniques acquired during the BA to another field of study by demonstrating intellectual openness

  • Acquiring the methodological and practical knowledge necessary to design and manage an information system

DEEPENING SPECIALIZED KNOWLEDGE

  • Understanding and mastering specialized concepts in the field of Information Science

BEHAVING PROFESSIONALLY

  • Displaying analytical and synthetical thinking, contextualization skills, rigor and consistency

  • Demonstrating critical thinking and autonomy

References, bibliography, and recommended reading

BADE D., "It's about Time!: Temporal Aspects of Metadata Management in the Work of Isabelle Boydens". In Cataloging & Classification Quarterly, volume 49, n° 4, 2011, pp. 328-338. (lien vers l'article)

BATINI C. and SCANNAPIECO M., eds., Data and Information Quality. Dimensions, Principles and Techniques. New York, Springer, 2016.

BERTEN V. et BOYDENS I., Email Address Reliability, Deliverable, Section Recherches, Bruxelles, Smals, 2014.

BERTI EQUILLE L. éd., La qualité et la gouvernance des données au service de la performance des entreprises. Paris : Hermès, 2012.

BONTEMPS Y., BOYDENS I. et VAN DROMME D., Data Quality : tools. Deliverable, section recherches, Bruxelles, Smals, 2007.

BOYDENS I. HAMITI G. et VAN EECKHOUT R., Data Quality : “Anomalies & Transactions Management System” (ATMS), prototype & “work in progress”. Bruxelles, Smals, Research Section, post de blog, 08/12/2020.

BOYDENS I., "L'océan des données et le canal des normes". In CARRIEU-COSTA M.-J., BRYDEN A. et COUVEINHES P. éds, Les Annales des Mines, Série "Responsabilité et Environnement" (numéro thématique : "La normalisation : principes, histoire, évolutions et perspectives"), Paris, n° 67, juillet 2012, pp. 22-29.

BOYDENS I., "Strategic Issues Relating to Data Quality for E-government: Learning from an Approach Adopted in Belgium". In Assar S., Boughzala I. et Boydens I., éds., Practical Studies in E-Government : Best Practices from Around the World. New York : Springer, 2011, p. 113-130 (chapitre 7).

BOYDENS I. et VAN HOOLAND S., "Hermeneutics applied to the quality of empirical databases". In Journal of documentation, volume 67, issue 2, 2011, pp. 279-289.

BOYDENS I., Informatique, normes et temps. Bruxelles: Bruylant, 1999.

DE WILDE M. et VERBORGH, R., Using OpenRefine. Brimingham-Mumbai : Packt Publishing, 2013.

HAMITI G., Data Quality Tools : concepts and practical lessons from a vast operational environment. Cours-conférence, Université libre de Bruxelles, 13/03/2019.

LOSHIN D., The Practicioner's Guide to Data Quality Improvement. Elsevier, Morgan-Kaufmann OMG Press, 2011.

McCALLUM Q. E., Bad Data Handbook, Mapping the World of Data Problems. O’Reilly Media, 2012, 246 p.

MADNICK S. E., WANG R.-Y., YANG W.-L. et HONGWEI Z., "Overview and Framework for Data and Information Quality Research". In Journal of Data and Information Quality, Vol. 1, No. 1, 2009.

OLSON J., Data Quality: The Accuracy Dimension. Elsevier, The Morgan-Kaufmann Series in Database Management, 2002.

REDMAN T., Data Quality. The Field Guide. Boston: Digital Press, 2001.

REDMAN T., Getting in front on Data. Who Does What. Technics Publications, 2016.

RIVIERE P., Utiliser les déclarations administratives à des fins statistiques. In Le Courrier des statistiques, Paris, INSEE, décembre 2018, n°1, p. 14-23.

SHAZIA S. ed., Handbook of Data Quality. Research and Practice. Berlin, Springer, 2013.

Course notes

  • Université virtuelle

Other information

Additional information

Web page of the Professor, Isabelle Boydens, ULB (bibliographical and other useful links)

Contacts

UV : http://uv.ulb.ac.be 
Teaching assistant : Mathias Coeckelbergs  email : Mathias.Coeckelbergs@ulb.ac.be
Professor Lecturer : Isabelle Boydens email : iboydens@ulb.ac.be

Campus

Solbosch

Evaluation

Method(s) of evaluation

  • Other

The practical part consists of individual work during the term ; the theoretical part will be tested during a written examination

Mark calculation method (including weighting of intermediary marks)

Dristribution of the learning activities in the grading system : theoretical part (50%) - practical part (50%)

Language(s) of evaluation

  • french

Programmes