Business Intelligence

No prerequisites are required.

The Business Intelligence Course allows students to become aware, learn to manage and develop the ability to propose solutions for Data Analytics, Data Visualization and, globally, Business Intelligence, especially for small and medium-sized companies. To this end, and in a first phase, students will have theoretical-practical classes where they learn to manipulate Self Service Business Intelligence tools (such as Tableau) and to understand how data should be presented and communicated in a way that is understandable by the public.

Few (2019) states that “we are not yet in the information age but in the data age” and, given the multiplicity of data available to facilitate the decision-making process, students will have
advantage if they are able to propose more efficient and effective solutions for the presentation of information in their companies. Later, they will get in touch with the reality of Data warehouses: what they are for, how they are created and case studies.
The Business Intelligence (BI) UC provides for the holding of some lectures with external guests on current research themes (it is essential that all Master UCs contribute to making the student feel able to conduct research) in the BI, Business Analytics area and application cases. among others.
The assessment methodology allows students to develop research skills and practices in specific areas of BI that the student perceives as being relevant to their training and / or to their company.

Given the growing amount of information from multiple sources, available to organizations it is imperative to systematize and structure this information in order to be used effectively and efficiently to support decision making. Thus, the objective of this course is to make known the potential of the business intelligence process and data warehouses to support and provide current information technologies and development methodologies of business intelligence solutions.

The Business Intelligence Curricular Unit allows students to acquire skills:

1) of a practical nature, namely, that students acquire knowledge, learn to manage, and develop the ability to propose Data Analytics, Data Visualization, and, globally, Business Intelligence solutions, especially for small and medium-sized companies.

2) of a scientific and research nature in business intelligence and analytics: given the nature of the study cycle, it is considered essential that students develop skills and knowledge of the main authors and the most recent studies and trends on the themes in this area.

Business intelligence (BI):
“A generic term to describe leveraging the organization’s internal and external information assets for making better business decisions.

in The Data Warehouse Toolkit: The complete guide to dimensional Modeling, 2nd edition, Ralph Kimbal,

Margy Ross, Ed. J. Wiley & sons, Inc, 2002, pp 393, 394

1. Introduction to Business Intelligence
2. Business Intelligence Components
3. Patterns and trends in Business Intelligence
4. Data warehouses:
4.1. Dimensional Data Modeling
4.2. Extract, Transform and Load (ETL) Process
4.3. Online Analytical Processing (OLAP) Tools
5. Introduction to Data Mining 6. Methodologies to implement Business Intelligence Projects

NAO

Fundamental references:

Stephen Few, 2020, Now you see it: an introduction to Visual Data Sensemaking, 2nd edition, Analytics Press

Stephen Few, 2019, The Data Loom: Weaving Understanding by Thinking Critically and Scientificallywith Data, 2019

M. Y. Santos e I. Ramos, 2017, Business Intelligence – da Informação ao Conhecimento – 3.a edição Atualizada, Editora FCA, ISBN: 978-972-722-880

E. Turban, R. Sharda, J. Aronson, D. King, 2016, Business Intelligence, Analytics, and Data Science: A Managerial Perspective, Pearson; 4th edition (December 12, 2016)

Artigos científicos

Aditional references:

Sherman, Rick (2014). Business Intelligence Guidebook: From Data Integration to Analytics, Morgan Kaufmann, 1st edition ISBN: 978-0124114616

C. P. Caldeira, Data Warehousing – Conceitos e Modelos, Edições Silabo, 2009.

Shmueli, G., Patel, N. R., Bruce, P. C. (2010). Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner, 2.nd ed, John Willey and Sons

Kimball, Ralph and Ross, Margy. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling (Second Edition). John Wiley & Sons, 2002.

R. Kimball, M. Ross, W. Thornthwaite, J. Mundy, B. Becker, The Data Warehouse Lifecycle Toolkit, 2nd Edition, Wiley, 2008.

Gangadharan, G., Swami, S. N. (2004). Business intelligence systems: design and implementation strategies, Information Technology Interfaces, 26th International Conference on (pp. 139-144). IEEE

Watson, Hugh J., Wixom, B. H. (2007). The Current State of Business Intelligence. IEEE Computer, 40(9), 96- 99 W. H. Inmon, Building the Data Warehouse (4th Ed edition), Hungry Minds Inc, 2005.