Master’s degree in Management and business strategy

Business Analytics

Course code
Name of lecturers
Alessandro Zardini, Ludovico Bullini Orlandi
Alessandro Zardini
Number of ECTS credits allocated
Academic sector
Language of instruction
secondo semestre (lauree magistrali) dal Mar 1, 2021 al Jun 1, 2021.

Lesson timetable

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Learning outcomes

This course aims to introduce students to the concept and techniques of Business Analytics models, but also the new role of the "Business Analyst" within the organization. Particularly in the first part of this course, it will be used the POPIT(TM) model. Therefore, the data analysis and the interpretation of the information must always be aligned with: human resources (skills), the organizational environment, processes, and technologies. Taking as a reference the KPIs (Key Process Indicators), deriving from the business processes previously defined through the BPMN language, they will be analyzed and evaluated in real-time to create reports or dashboards to support the decision-making process. The data in addition to being used for the decision-making process, for the operational part, will also be used to align the POPIT(TM) components with the corporate business model better. This analysis will be conducted through the use of Business Intelligence tools to better respond to market needs. In the second part of the course, starting from historical series and external destructured data (through Big Data) we will proceed with the realization of predictive models to support managers in their strategic and operational decisions.


The course aims to provide students with the fundamental concepts for reading, and to analyze enterprise data, to generate predictive models to support the decision-making process. The contents of the course can be summarized as follows:
1. Business Process Management, and the model POPITTM (People, Organization, Process, e IT).
2. Business Process Mapping, Analysis, and re-Engineering.
3. BPMN (Business Process Model Notation) for business integration.
4. DataWare House.
5. Business Intelligence tools.
6. Data analysis (querying, reporting, clustering, etc.).
7. Predictive Analysis (correlation, supervised regressions, etc.).
8. Introduction to Big Data.
9. People analytics.

Reference books
Author Title Publisher Year ISBN Note
Davenport T.H., Harris G.J. Competere con gli analytics. La nuova scienza per vincere nel business (Edizione 1) Franco Angeli edizioni 2019 9788891781086

Assessment methods and criteria

The final test is written. Mainly, it is composed of 3 open questions.

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