ECTS - University of Pisa
View syllabus
Strategic and competitive intelligence
FILIPPO CHIARELLO
Academic year2023/24
CourseDATA SCIENCE AND BUSINESS INFORMATICS
Code787II
Credits6
PeriodSemester 2
LanguageEnglish

ModulesAreaTypeHoursTeacher(s)
STRATEGIC AND COMPETITIVE INTELLIGENCEING-IND/35LEZIONI48
FILIPPO CHIARELLO unimap
IRENE SPADA unimap
Learning outcomes
Knowledge

By the end of the course:

  • Students will have acquired knowledge about the tools and methodologies to design and develop competitive intelligence projects 
Assessment criteria of knowledge

Ongoing assessment to monitor academic progress will be carried out in the form of laboratory activities and meetings between the lecturers and a group of students developing the project
Skills

By the end of the course:

  • Students will know how to use text mining & NLP, SNA, RStudio for intelligence purposes
  • Students will be able to conduct research and analysis of IP, scientific and market sources
  • Students will be able to present, in a written report the results of their activity carried out during the project work
Assessment criteria of skills
  • During the lab sessions, small projects will be carried out in order to understand how to use the tools
  • Practical activities will be carried out to search for sources through known databases 
  • Students will have to prepare and present a written report that documents the results of the project activity
Behaviors
  • Students will acquire and/or develop an awareness of environmental issues
  • Students will be able to manage the responsibility of managing a team project
  • Students will acquire accuracy and precision when collecting and analysing experimental data
Assessment criteria of behaviors
  • During the lab sessions, the accuracy and precision of the activities carried out will be evaluated
  • During group work, the methods of assigning responsibility, management and organisation during the project phases will be evaluated
Prerequisites

Fundamentals of:

  • financial & cost accounting
  • strategy
  • organization design
Programma (contenuti dell'insegnamento)
  • [1] SCI FUNDAMENTALS: VUCA, Ansoff Model, surprise in business, risk & uncertainty, applications of CI, CI cycle
  • [2] IP INTELLIGENCE BASICS: Patents, trademarks, copyrights, patent search engines, ecosystems & platforms
  • [3] DATA SCIENCE FOR SCI PROJECTS: (1) Basics: text analysis; (2) Advanced: NIR, topic modelling, network analysis and visualization
  • [4] DATA SCIENCE PROJECT DESIGN: Scoping, KITs and KIQs, metrics, management, result, visualization
  • [5] SCI APPLICATION LAB: How to extract intelligence from scientific papers; How to extract intelligence from IP; How to extract intelligence from HR and other sources (i.e. Wikipedia)
Syllabus
  • [1] SCI FUNDAMENTALS: VUCA, Ansoff Model, surprise in business, risk & uncertainty, applications of CI, CI cycle
  • [2] IP INTELLIGENCE BASICS: Patents, trademarks, copyrights, patent search engines, ecosystems & platforms
  • [3] DATA SCIENCE FOR SCI PROJECTS: (1) Basics: text analysis; (2) Advanced: NIR, topic modelling, network analysis and visualization
  • [4] SCI APPLICATION LAB: How to extract intelligence from scientific papers; How to extract intelligence from IP; How to extract intelligence from HR and other sources (i.e. Wikipedia), Data Viz for SCI
Bibliografia e materiale didattico

REFERENCE BOOKS:

  • For [I, II, IV] modules: McGonagle, John J., Vella, Carolyn M. (2012). Proactice Intelligence. The Successful Executive's Guide to Intelligence, Springer
  • For [III, V] modules: Ziegler, Cai-Nicolas (2012). Mining for Strategic Competitive Intelligence, Springer    +     Text Mining with R

For each lessson, a detailed bibliography is available on the Course Plan (in SCI Teams)
Bibliography

REFERENCE BOOKS:

  • For [I, II, IV] modules: McGonagle, John J., Vella, Carolyn M. (2012). Proactice Intelligence. The Successful Executive's Guide to Intelligence, Springer
  • For [III, V] modules: Ziegler, Cai-Nicolas (2012). Mining for Strategic Competitive Intelligence, Springer    +     Text Mining with R

For each lessson, a detailed bibliography is available on the Course Plan (in SCI Teams)
Assessment methods

Grading is based on:

  • TEAM PROJECT WORK
  • INDIVIDUAL DISCUSION

There is the 70-30 weighting rule: 70 for PROJECT WORK (deliverables + group discussion), 30 for INDIVIDUAL discussion.

Both project work AND individual discussion should be almost 18/30 to grade.

Team project work requires to deliver (1) a report; (2) a presentation to be discussed (3) the code.

Deliverables are valuated on the following criteria and points:

  • clarity: 5p.
  • completeness: 4p.
  • data viz & communication effectiveness: 5p.
  • originality: 4p.
  • methodological fit: 6p.
  • value for the insights: 6p.

 
Updated: 20/10/2023 19:15