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Data Governance

Code: 44749
Credits: 9
2026/2027
Degree programme Type Course
Archival Studies and Information Governance OB 2

Contact lecturer

Name :
Ainhoa Pascualena Lasa
Email :
ainhoa.pascualena@uab.cat

Teaching staff

Maria del Pilar Campos Martinez

Group languages

You can consult this information at the end of the document.

Prerequisites

  1. It is recommended that the subject "Information Systems and Systems Architecture" has been previously taken
  2. It is recommended that the subject "Information Description and Retrieval" has been previously taken

Objectives

  1. Know the data life cycle and its management.
  2. Understand the context of data production.
  3. Apply archival principles to data management.
  4. Know and understand the main tools and systems for data management.
  5. Know data management systems and databases.
  6. Know data governance models, rules, and standards.
  7. Know and understand the basic systems for data use, exploitation, and visualization.

Learning outcomes

  • (CA21) Establish an organisation's quality data.
  • (CA22) Design the criteria and formats for managing the life cycle of an organisation's data.
  • (KA30) Describe the data life cycle.
  • (KA31) Identify types and formats of data.
  • (KA32) Recognise data governance systems: repositories, data architecture platforms, and database systems.
  • (SA23) Use the main data management instruments and systems.
  • (SA24) Apply data governance techniques in organisations.
  • (SA25) Apply archival principles to data management.

Contents

1.1. Data in organizations (introduction)


1.2. Where are data produced?


1.2.1. Ways of capturing and generating data (procedures, sensors, etc.)


1.2.2. Models for structuring data (master, referential, etc.)


1.2.3. Architectures for storage (types of databases)


1.2.4. OpenData websites exploration


1.3. How are data used?


1.3.1. Data preparation


1.3.1.1. Data formats to be cleaned


1.3.1.2. Data cleansing


1.3.1.3. Preparation for exploitation


1.3.2. Exploitation and use of data


1.3.2.1. Data visualization


1.3.2.2. Advanced statistical analytics, or based on ML and AI


1.3.2.3. Practical application of advanced analytics algorithms


1.4. Integrated data governance


1.4.1. Data identification and cataloging


1.4.2. Data lineage control


1.4.3. Virtualization of data access


1.4.4. Legal and security aspects


1.4.5. Links with archival science


Learning activities and methodology

Title Hours ECTS Learning outcomes
Type: Autonomous
Final test: test of general knowledge of the subject. 10 0.4 CA21, CA22, KA30, KA31, KA32, SA23, SA24, SA25
Reading materials and working by themselves 75 3 CA21, CA22, KA30, KA31, KA32, SA23, SA24, SA25
Type: Guided
Classroom participation and debate forums 15 0.6 CA21, CA22, KA30, KA31, KA32, SA23, SA24, SA25
Theoretical sessions 45 1.8 CA21, CA22, KA30, KA31, KA32, SA23, SA24, SA25
Type: Supervised
Exercise 1: Challenge definition (problem, questions) and data set identification (exploration, justification) 20 0.8 CA21, KA31, KA32
Exercise 2: Data preparation: data quality analysis, transformations performed, entities, relationships and data map 30 1.2 CA21, CA22, SA23, SA24
Exercise 3: Dashboard: Definition of indicators (KPIs), data visualization and interpretation 30 1.2 CA21, SA23, SA24

A detailed schedule outlining the content of each session will be presented on the first day of the course and will be available on the course’s Virtual Campus, where students will find the various teaching materials deemed appropriate by the instructors and necessary information for effective course monitoring. Should the teaching modality change for reasons of force majeure according to the competent authorities, the teaching staff will inform students of any modifications to the course schedule and teaching methodologies.


The autonomous learning activities will be reading materials and preparing for the final general knowledge test of the course.

The directed activities will be theoretical lecture sessions. They will also include questions or debates in class and a forum with open participation by students.

The supervised activities will be 3 practical exercises to be done at home with the explanations received in class.


Note: The course content will be sensitive to issues related to gender perspective and the use of inclusive language.


Annotation: within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

Assessment

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Classroom participation and debate forums 10% of the final grade 0 0 CA21, CA22, KA30, KA31, KA32, SA23, SA24, SA25
Exercise 1: Challenge definition (problem, questions) and data set identification (exploration, justification) 20% of the final grade 0 0 CA21, KA31, KA32
Exercise 2: Data preparation: data quality analysis, transformations performed, entities, relationships and data map 20% of the final grade 0 0 CA21, CA22, SA23, SA24
Exercise 3: Dashboard: Definition of indicators (KPIs), data visualization and interpretation 20% of the final grade 0 0 CA21, SA23, SA24
Final test: test of general knowledge of the subject. 30% of the final grade 0 0 CA21, CA22, KA30, KA31, KA32, SA23, SA24, SA25

Exercises 1, 2, and 3 will account for 20% of the final grade, the final exam for 30%, and participation in class, discussion forums, and exercises throughout the course for 10%.


For this subject, the use of Artificial Intelligence (AI) technologies is allowed as part of the work, provided that the final result reflects a significant personal contribution from the student. The student must clearly identify which parts have been generated with this technology, specify the tools used and include a critical reflection on how these have influenced the process and the final result of the activity. The lack of transparency of the use of AI in this assessable activity will be considered a lack of academic honesty and may lead to a partial or total penalty in the grade of the activity, or greater sanctions in serious cases.


This course does not provide for a single-assessment system.

Bibliography

Earley, Susan, & Henderson, Déborah (Ed.). (2017). DAMA-DMBOK: Data management body of knowledge (2nd edition). Data Management Association.


Ghavami, Peter (2020). Big data management: Data governance principles for big data analytics (1a ed.). De Gruyter.


Khatri, Vijay, & Brown, Carol V. (2010). Designing data governance. Communications of the ACM, 53(1).


Laurent, Anne, Laurent, Dominique, & Madera, Cédrine (Ed.). (2019). Data lakes. ISTE Ltd / John Wiley and Sons Inc.


Lemieux, Victoria L., Gormly, Brianna, & Rowledge, Lyse (2014). Meeting Big Data challenges with visual analytics: The role of records management. Records Management Journal, 24(2).


Reina, Luís (2023). Noves arquitectures de dades. Lligall; Revista catalana d’arxivística, 46.


Serra Serra, Jordi (2024). El gobierno “archivístico” del dato. Tábula, 27.


Torreblanca, Sònia (2023). La governança de dades com a interacció: Un concepte analític per a les administracions públiques. Lligall; Revista catalana d’arxivística, 46.


Resources published at Localia.cat (Diputació de Barcelona)

Software

  • Microsoft Excel
  • Microsoft Power BI (desktop version)
  • Microsoft Sharepoint
  • Microsoft Copilot

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE

Type of teaching Group Language Semester Shift
(TEm) Theory (master) 1 Catalan first semester afternoon