Qualité de l'information et des documents numériques

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. 

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

Required and corequired courses

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 or after a previous MA 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. (link to the 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., CORBESIER I. et HAMITI G., Data Quality Tools : retours d'expérience et nouveautés. Bruxelles, Smals, Research Section, post de blog, 07/12/2021. Link to the article.

BOYDENS I., HAMITI G. and VAN EECKHOUT R., Un service au cœur de la qualité des données. Présentation d’un prototype d’ATMS. In Le Courrier des statistiques, Paris, Insee, june 2021, n°6, pp. 100-122. PDF file Link to the Journal and to the article

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

Additional information

Professor Lecturer' Web Page : https://isabelle-boydens.web.ulb.be/

Contacts

UV : http://uv.ulb.ac.be 
Teaching assistant : Guillaume Quintin  email : Guillaume.Quintin@ulb.be
Professor Lecturer : Isabelle Boydens email : Isabelle.Boydens@ulb.be

Campus

Solbosch

Method(s) of evaluation

• Practice work
• Oral examination
• Written report

Practice work

Oral examination

• Open question with short answer

Written report

The practical part consists of individual work and a short written report during the semester. The theoretical part will be tested in an oral exam lasting around 20 minutes and including open-ended questions.


Important :  With regard to the use of generative artificial intelligence tools, the terms and conditions of this course follow those set out for dissertations and TPMs in the Master‘dissertation guide’.

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