Business Intelligence
A.Y. 2026/2027
Learning objectives
The course aims to provide students with the theoretical knowledge and fundamental concepts required to understand the role of Information Systems within organizations and their contribution to supporting operational and decision-making processes, as well as the main data architectures and information systems supporting business operations and corporate governance.
The course also provides the conceptual tools and methodological knowledge needed to design Business Intelligence systems, with a focus on data extraction, integration, and analysis techniques, as well as the design and population of data warehouses and data marts.
The course is complemented by an integrated view of the evolution of Information Systems in the age of Artificial Intelligence (AI), from transaction processing systems to Business Intelligence systems and, ultimately, to modern AI-Augmented Information Systems, highlighting the role of data, knowledge, and AI in the digital transformation of organizations.
The course also provides the conceptual tools and methodological knowledge needed to design Business Intelligence systems, with a focus on data extraction, integration, and analysis techniques, as well as the design and population of data warehouses and data marts.
The course is complemented by an integrated view of the evolution of Information Systems in the age of Artificial Intelligence (AI), from transaction processing systems to Business Intelligence systems and, ultimately, to modern AI-Augmented Information Systems, highlighting the role of data, knowledge, and AI in the digital transformation of organizations.
Expected learning outcomes
Upon successful completion of the course, students will be able to:
- understand the role and architecture of enterprise Information Systems and their contribution to supporting organizational and decision-making processes;
- analyze and compare Business Intelligence solutions;
- describe the main technologies and methodologies for the design and management of Business Intelligence systems, including data integration and analysis techniques;
- analyze the evolution of Information Systems towards AI-Augmented architectures, evaluating the contribution of Artificial Intelligence to supporting business processes;
- understand the principles of Data Governance and their role in ensuring the quality, management, and effective use of enterprise data and knowledge.
- understand the role and architecture of enterprise Information Systems and their contribution to supporting organizational and decision-making processes;
- analyze and compare Business Intelligence solutions;
- describe the main technologies and methodologies for the design and management of Business Intelligence systems, including data integration and analysis techniques;
- analyze the evolution of Information Systems towards AI-Augmented architectures, evaluating the contribution of Artificial Intelligence to supporting business processes;
- understand the principles of Data Governance and their role in ensuring the quality, management, and effective use of enterprise data and knowledge.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
I - Enterprise Information Systems
Role, characteristics, and functions of enterprise information systems. Data organization and support for business processes. Enterprise information systems: ERP (Enterprise Resource Planning) systems, CRM (Customer Relationship Management) systems, SCM (Supply Chain Management) systems. The Information Systems function and professional roles. Information systems, ICT, and digital scenarios: design and development, cloud computing, main digital trends.
II - Business Intelligence
Decision-making processes and decision support. Managerial information needs: OLTP vs. OLAP. Architecture of a Business Intelligence system. Data warehouse and data mart. Architectures for data warehousing. ETL (Extraction, Transformation, and Load). Data integration. Conceptual and logical design of a datawarehouse. Datawarehouse population. Business reporting, OLAP, data mining.
III - Evolution of Information Systems in the age of Artificial Intelligence (AI)
Evolution of Information Systems towards AI-Augmented Information Systems. Integration of A into business processes. AI-Augmented Business Process Management. Evolution of Business Intelligence systems towards Augmented Analytics paradigms. Integrated analysis of structured and unstructured data and Retrieval-Augmented Generation (RAG) techniques for enterprise knowledge access. Data Governance: data and information quality, availability, and governance in AI-Augmented information systems; metadata management, information source cataloging, and enterprise knowledge management.
Role, characteristics, and functions of enterprise information systems. Data organization and support for business processes. Enterprise information systems: ERP (Enterprise Resource Planning) systems, CRM (Customer Relationship Management) systems, SCM (Supply Chain Management) systems. The Information Systems function and professional roles. Information systems, ICT, and digital scenarios: design and development, cloud computing, main digital trends.
II - Business Intelligence
Decision-making processes and decision support. Managerial information needs: OLTP vs. OLAP. Architecture of a Business Intelligence system. Data warehouse and data mart. Architectures for data warehousing. ETL (Extraction, Transformation, and Load). Data integration. Conceptual and logical design of a datawarehouse. Datawarehouse population. Business reporting, OLAP, data mining.
III - Evolution of Information Systems in the age of Artificial Intelligence (AI)
Evolution of Information Systems towards AI-Augmented Information Systems. Integration of A into business processes. AI-Augmented Business Process Management. Evolution of Business Intelligence systems towards Augmented Analytics paradigms. Integrated analysis of structured and unstructured data and Retrieval-Augmented Generation (RAG) techniques for enterprise knowledge access. Data Governance: data and information quality, availability, and governance in AI-Augmented information systems; metadata management, information source cataloging, and enterprise knowledge management.
Prerequisites for admission
Fundamental notions of relational databases and RDBMS are required (database design, data modeling, schema definition and query languages).
Teaching methods
The teaching consists of lectures, supported by the use of slides. For more technical/design topics, exercises are also performed, which are commented on in the classroom. Slides, which follow the lectures' and exercises contents, are available on the MyAriel website of the course. The course also includes in-depth discussions of business intelligence use cases and real-world scenarios in different domains, with a focus on the application of the concepts and architectures presented during the course.
Teaching Resources
Textbooks suggested:
- G. Bracchi, C. Francalanci, G. Motta. Sistemi Informativi d'impresa. McGraw-Hill - 2010 (Chapters 1,2,3,4,5,9)
- M. Golfarelli, S. Rizzi. Data Warehouse - Teoria e pratica della progettazione (2 ed.). Mc-Graw Hill, 2006 (Chapters 1,2,3,4,5 (until 5.2.10 included), 6 (until 6.1.8 included), 8, 9 (until 9.1.10 included)).
Supplementary course materials and lecture slides are available on the MyAriel platform.
- G. Bracchi, C. Francalanci, G. Motta. Sistemi Informativi d'impresa. McGraw-Hill - 2010 (Chapters 1,2,3,4,5,9)
- M. Golfarelli, S. Rizzi. Data Warehouse - Teoria e pratica della progettazione (2 ed.). Mc-Graw Hill, 2006 (Chapters 1,2,3,4,5 (until 5.2.10 included), 6 (until 6.1.8 included), 8, 9 (until 9.1.10 included)).
Supplementary course materials and lecture slides are available on the MyAriel platform.
Assessment methods and Criteria
The exam is written with a duration of 60 minutes, it covers all the topics of the program and it consists of questions and exercises. The final evaluation, expressed in thirtieths, takes into account the level of knowledge and mastery of the topics, considering clarity of exposition and accuracy of language, as well as the ability to solve design-type exercises.
Professor(s)