Business Intelligence

No prerequisites are required.

The Business Intelligence Curricular Unit allows students to gain 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. 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 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, in their companies, to propose more efficient and effective solutions for the presentation of information. Later, they will come into contact with the reality of Data warehouses: what they are for, how they are created and case studies.
The Business Intelligence (BI) UC foresees the holding of some Lectures with external guests on current research topics (it is essential that all Master CUs contribute so that the student feels able to carry out research) in the area of BI, Business Analytics and application cases. among others.

The teaching methodology is essentially “problem-based learning” and “project-based learning”, encouraging students to propose solutions to problems that are exposed in class and to develop a project throughout the semester. Even with regard to the writing of a scientific article, it is based on the project methodology since the student will have to deliver several phases of his work and receive feedback on each one in order to move on to the next one. The objective is to promote research and the publication of research articles with students,
The teaching methodology allows students to develop research and practical skills 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 available to organizations from multiple sources, it becomes imperative to systematize and structure this information in order to be used effectively and efficiently to support decision-making. Thus, the objectives of this curricular unit are:

– make known the potential of Business Intelligence processes and supporting Data Warehouses
– know the current information technologies and methodologies for developing Business Intelligence solutions.
– learn about the main trends in data visualization tools
– Know how to use a data visualization tool and implement the entire data analysis circuit, with a critical sense of the solutions to be used.

The Business Intelligence Curricular Unit allows students to acquire skills:
1) 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 more recent studies and trends on the themes of this area.

2) of a practical nature, namely that students become aware, 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.

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
1.1 Main concepts
1.2 Main trends in BI

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.