Provided by:
UNIPI
From:
M.Sc. in Data Science and BI
Sede:
Polo Fibonacci, University of Pisa
Lecturers:
Antonella MARTINI
Semester:
1
Hours:
48
Educational Goals:
CI programs have goals such as proactively detecting business opportunities or threats, eliminating or reducing blind- spots, risks and/or surprises; and reducing reaction time to competitor and marketplace changes. The end product of any worthwhile CI activity is what practitioners term Ôactionable intelligenceÕ Ð i.e. intelligence that management can act upon; perspective. It is more than analysing competitors: it is a process for gathering information, converting it into intelligence (about products, customers, competitors, and any aspect of the environment) and then using it in decision making. In this sense, big data brings big change to CI. The course is very interactive and includes also in-class seminars with experts on emerging topics, defined each year (i.e. patent analysis, due diligence, social network analysis for business). It provides many tools and techniques; HBS cases are used. Students will apply these tools in groups when analysing a preselected case company. They are expected to present early stage versions of their CI reports and, in the final workshop, they will present the results of their CI analysis, which is then discussed in plenary.
By the end of the course students will have acquired knowledge about the tools and methodologies to design and develop competitive intelligence projects
Prerequisites:
Fundamentals of financial & cost accounting, strategy, organization design.
Programme:
[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)
