Data Ecosystems and Governance in Organizations


In this course we will understand the huge variety of data that we have access in our daily lives, the computer systems that manipulate and store them and how we can extract useful and quality information for the business, and to support decision making. Topics such as access to data, by whom and how, are extremely important for reasons of security and privacy, a policy of access to data is essential. The need for standards, policies, and procedures on manipulating data, in complement with compliance and adherence with data privacy and protection regulations, is crucial for risk minimization in organizations. The last objective of this course is the General Data Protection Regulation, its impact on organizations and the approach to a protection and data compliance program. 

General characterization





Responsible teacher

José Carlos Teixeira


Weekly - Available soon

Total - Available soon

Teaching language





DAMA, The Data Management Association. Guide to the Data Management Body of Knowledge , 2014.
KIMBALL, Ralph, Foreword by W. H. Inmon. The Data Warehouse Toolkit . Practical Techniques for Building
Dimensional Data Warehouses. Wiley 2013.
MARR, Bernard. Biga Data in Practice . How 45 successful companies used Big Data Analysis to deliver
extraordinary results. Wiley 2016.
RAMESH, Sharda, DELEN, Dursun, TURBAN, Delen. Business Intelligence, Analytics, and Data Science . A
Managerial Perspective. Pearson Education Limited, 2018.
SARSFIELD, Steve. The Data Governance Imperative . A business strategy for corporate data. IT Governance
Publishing 2011.
SOARES, Sunil. The IBM Data Governance Unified Process . Driving Business Value with IBM software and
Best Practices. MC Press Online, 2011.
VOIGT, Paul, BUSSCHE, Axel Von. The EU General Data Protection Regulation (GDPR). A Practical Guide.
Springer, 2017.
The Bibliography for this course is optional.
We can privide e-Books for a major part of it, at the request of the student.

Teaching method

Lectures will cover the fundamental topics of the course, including time for questions, invited guest speakers and case studie discussion and presentations. 


Evaluation method

Group Work - Case Study (each group with 5 students max), 25%.

Mid-Term Exam, 25%.

Final Exam, 50%.  

Subject matter

Data Ecosystem : Is a collection of infrastructure, analytics and applications used to capture and analyse data. Topics to Data Ecosystem be coveredbe covered includes Business Intelligence, Data Warehousing and Data Mining. Learn what Big Data is and how it is changing the world of analytics, Artificial Intelligence and Machine Learning definitions. 

Data Governance : Data Governance consists of the management of an organization's information assets and data availability, including rules, policies, procedures, functions, and responsibilities that guide the overall management of the company's data. It provides guidance that ensures that data as quality, is accurate, consistent, complete, available, and secure and encompasses processes, organization, and technology. 

Data Quality : Data Quality refers to the general utility of a data set due to its ability to be easily processed and analysed for other uses, namely in a data base, in a data warehouse or in an analytical system. 

General Data Protection Regulation : The General Data Protection Regulation (GDPR) is a legal framework that sets guidelines for the collection and processing of personal information from individuals who live in the European Union (EU). Topics to be covered are: conditions for Consent, Lawfulness of Treatment, Rights of the Data Subjects, Privacy by Design and by Default, Registration of Treatment Activities, Data Minimization, Data Profiling, Data Protection Impact Assessment, Controller and Processor, Breach Notifications. Required field. 




Programs where the course is taught: