Business Intelligence


The main objective of this course is to take students to understand the capabilities of decision support processes enhanced by the use of Business Intelligence and Data Warehouse, namely Business Analytics and Performance Management.

General characterization





Responsible teacher

Miguel de Castro Simões Ferreira Neto


Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English


Data Warehousing


Turban E.; Sharda R.; Dursun D. and King D. (2011). Business Intelligence: a managerial approach. Second Edition. Prentice Hall, ISBN: 013610066X.; Howson, C. (2008). Successful business intelligence : secrets to making BI a killer app. McGraw Hill. ISBN 978-0-07-149851-7; Few, Stephen (2006). Information Dashboard Design: The Effective Visual Communication of Data. Sebastopol, CA: O’Reilly Media. ISBN 0596100167; Eckerson, Wayne W. (2006). Performance Dashboards: Measuring, Monitoring, and Managing Your Business. Hoboken, NJ: John Wiley & Sons. ISBN 0750661747; 0

Teaching method

This course will include lectures and labs.
In the lectures will consist of theoretical concepts, case studies and presentations from leading BI vendors.
The applied component of the course will include several computer labs where students will apply the concepts and theories presented in lectures leveraging the Microsoft Business Intelligence Platform (SQL Server, SQL Business Intelligence Development Studio).
In this context the students will have to develop a project.

Evaluation method

The assessment includes:
a) Project
b) Final Exam
Final grade calculated based on the following formula:
a) 50%
b) 50%

Subject matter

T.1 - Business Analytics and Data Visualization
1.1 Introduction to the field of Business Analytics ( BA )
1.2 Online Analytical Processing ( OLAP )
1.3 Reports and Queries
1.4 Multidimensionality
1.5 Advanced Business Analytics
1.6 Data Visualization
1.7 Geographic Information Systems
1.8 Business Intelligence real-time decision support and automated Competitive Intelligence
1.9 and Web Analytics : Web Intelligence and Web Analytics
1.10 Use , Benefits and Results of Business Analytics
T.2 - Data, Text and Web Mining
2.1 Concepts and Applications of Data Mining
2.2 Concepts and Applications of Text Mining
2.3 Concepts and Applications of Web Mining
T.3 - Business Performance Management
3.1 Introduction to Business Performance Management ( BPM )
3.2 Strategy
3.3 Planning
3.4 Monitoring
3.5 Acting and adjusting
3.6 Performance Measurement
3.7 BPM Methodologies
3.8 Architecture and applications of BPM 
3.9 Performance Dashboards
3.10 Business Activity Monitoring ( BAM )
T.4 - Information Dashboard Design
4.1 Introduction to the design of dashboards
4.2 Scope of Use of Dashboards

Lab 1 – Data Discovery
1.1 Exploring Public Repositories and Datasets
1.2 Exploring Public Data Services
1.3 Exploring Data Marketplaces
1.4 Exploring Social Data
1.5 Consuming Structured/Unstructered Data
1.6 Searching for Data

Lab 2 – Data Integration & Transformation
2.1 Data Loading Strategies
2.2 Data Quality
2.3 Data Cleansing 
2.4 Data Transformations
2.5 Data Refresh
Lab 3 – Building Analytics Models
3.1 OLAP / In-memory Models
3.2 Modelling Relationships
3.3 Modelling Measures
3.4 Modelling Hierarchies
3.5 Modeling KPIs
3.6 Modelling Time Intelligence
3.7 Data Level Security 
3.8 Model Ad-hoc Analysis

Lab 4 – Interactive Data Visualization
4.1 Best Practices & Examples
4.2 Ad-hoc Analysis 
4.3 Reporting & Dashboards
4.4 Information Sharing
4.5 Mobile Visualization
4.6 Alarms and Notifications
Lab 5 – Advanced Data Visualization
5.1 Storytelling
5.2 Real-time Dashboards
5.3 Geographic Information Dashboards
5.4 R & Python Visualizations
5.5 Natural Language Querying

Lab 6 – Predictive Analytics
6.1 Model Definition
6.2 Feature Preparation
6.3 Model Training & Scoring
6.4 Model Deployment
6.5 Model Consumption
6.6 Model ad-hoc exploration
6.7 Machine Learning with R
Lab 7 – Creating an End-to-End Solution
7.1 Data Modelling
7.2 Reporting and Dashboards
7.3 Predictive Analysis
7.4 Q&A


Programs where the course is taught: