| 授業名 | Design Thinking for Big Data & AI |
|---|---|
| Course Title | Design Thinking for Big Data & AI |
| 担当教員 Instructor Name | Giulio Toscani |
| コード Couse Code | GLP216_G20N |
| 授業形態 Class Type | 講義 Regular course |
| 単位 Credits | 2 |
| 言語 Language | JP |
| 学位 Degree | MBA |
| 開講情報 Terms / Location | 2020 GSM Nagoya Fall |
授業の概要 Course Overview
Misson Statementとの関係性 / Connection to our Mission Statement
授業の目的（意義） / Importance of this course
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Objective: How Big Data and AI will give an edge over competitors. What are your data and what technologies will better connect to your customers and to learn what they expect from you by applying Disruptive innovation methodology.
Who will benefit: Aspiring and established leaders in any industry that do not just analyse data and use technology but understand them, expand them, and incorporate them into strategy of their organizations
Who will benefit: Aspiring and established leaders in any industry that do not just analyse data and use technology but understand them, expand them, and incorporate them into strategy of their organizations
到達目標 / Achievement Goal
本授業の該当ラーニングゴール Learning Goals
*本学の教育ミッションを具現化する形で設定されています。
LG1 Critical Thinking
LG4 Effective Communication
LG5 Executive Leadership (EMBA)
LG6 Innovative Leadership (MBA)
LG7 Global Perspective (GLP)
LG4 Effective Communication
LG5 Executive Leadership (EMBA)
LG6 Innovative Leadership (MBA)
LG7 Global Perspective (GLP)
受講後得られる具体的スキルや知識 Learning Outcomes
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Learning Outcomes
By the end of the course, you will be able to:
Make strategic, confident decisions based on solid data and AI technology
Learn a valuable business methodology that help you pinpoint weaknesses and discover new opportunities
Earn higher profits by better understanding the future of your business, your processes and your customers
By the end of the course, you will be able to:
Make strategic, confident decisions based on solid data and AI technology
Learn a valuable business methodology that help you pinpoint weaknesses and discover new opportunities
Earn higher profits by better understanding the future of your business, your processes and your customers
教育手法 Teaching Method
| 教育手法 Teaching Method | % of Course Time | |
|---|---|---|
| インプット型 Traditional | 20 % | |
| 参加者中心型 Participant-Centered Learning | ケースメソッド Case Method | 60 % |
| フィールドメソッド Field Method | 20 % | 合計 Total | 100 % |
事前学修と事後学修の内容、レポート、課題に対するフィードバック方法 Pre- and Post-Course Learning, Report, Feedback methods
[Teaching Methods]
Traditional - 20%
Cases Method - 60%
Workshop - 10%
Quizzes / Tests - 10%
Total - 100%
Course Approach, Reports, Feedback Methods
What do web search, speech recognition, face recognition, machine translation, autonomous driving, and automatic scheduling have in common? These are all complex real-world problems, and the goal of Big Data & Artificial Intelligence (AI) is to tackle these with rigorous mathematical tools. In this course, you will learn the business opportunities generated from these applications, practice implementing some of these systems and understand how the world and the business is changing. Specific topics include machine learning, natural language processing, chatbots and robots. The main goal of the course is to equip you with the tools to tackle new opportunities offered by Big Data/AI you might encounter in life, by applying Disruptive Innovation, a methodology based on Design Thinking.
Course Approach: Case and workshop methodology, with a final presentation and assignment delivery.
Report: Individual presentation befoe the case starts
Feedback: Written, on final assignment
Required amount of preparation: there will be 4 cases, a workshop and a final presentation, requiring 2 hours each, Minimum 10 hours.
No prerequisite Knowledge/Experience
Final Examination
Case ：All the cases studied
Assignment ：Team Presentation and class test
Deadline ：Team presentation, in the afternoon of the last day. Class-test: tt the end of the class on last day
Submission method：Submit in hard copy to the Professor
Traditional - 20%
Cases Method - 60%
Workshop - 10%
Quizzes / Tests - 10%
Total - 100%
Course Approach, Reports, Feedback Methods
What do web search, speech recognition, face recognition, machine translation, autonomous driving, and automatic scheduling have in common? These are all complex real-world problems, and the goal of Big Data & Artificial Intelligence (AI) is to tackle these with rigorous mathematical tools. In this course, you will learn the business opportunities generated from these applications, practice implementing some of these systems and understand how the world and the business is changing. Specific topics include machine learning, natural language processing, chatbots and robots. The main goal of the course is to equip you with the tools to tackle new opportunities offered by Big Data/AI you might encounter in life, by applying Disruptive Innovation, a methodology based on Design Thinking.
Course Approach: Case and workshop methodology, with a final presentation and assignment delivery.
Report: Individual presentation befoe the case starts
Feedback: Written, on final assignment
Required amount of preparation: there will be 4 cases, a workshop and a final presentation, requiring 2 hours each, Minimum 10 hours.
No prerequisite Knowledge/Experience
Final Examination
Case ：All the cases studied
Assignment ：Team Presentation and class test
Deadline ：Team presentation, in the afternoon of the last day. Class-test: tt the end of the class on last day
Submission method：Submit in hard copy to the Professor
授業スケジュール Course Schedule
第1日（Day1）
AI and Big Data Now - Why This Time it’s DifferentArtificial intelligence (AI) and Big Data are rapidly emerging as the most important and transformative technology of our time. Recent advances, particularly in machine learning - a computer’s ability to improve its performance without human instruction - have led to a rapid proliferation of new applications that are changing the game for companies in almost all industries.
●使用するケース
Case 1 BytedanceCase 2 Choosy
第2日（Day2）
Consolidating Your AI/Big Data StrategyA growing proportion of human activities such as social interactions, entertainment, shopping, and gathering information are now mediated by digital devices and services. Such digitally mediated activities can be easily recorded, offering an unprecedented opportunity to study and measure intimate psycho-demographic traits using actual — rather than self-reported — behavior. Such Big Data assessment has a number of advantages: it does not require participants’ active involvement; it can be easily and inexpensively applied to large populations; and it is relatively immune to cheating or misrepresentation. The question is: What Data and Technology can push forward the company business?
●使用するケース
Case 3 Verisk: trailblazing in the big data jungleDisruptive Innovation Workshop
第3日（Day3）
The Future of business and workThe only thing predictable about the future of business and work is that there will be lots of change. One day you read that there is a looming labor shortage as the population ages, the next you read that mass unemployment is right around the corner due to the advent of robots and other artificial intelligence. In this session, we will look at trends in the market and the future of work and business. We will consider how demographic changes present business and labor market opportunities, as well as challenges.
●使用するケース
Case 4 Nestlé: developing a digital nutrition platform for japanFinal Presentation
Class Test
成績評価方法 Evaluation Criteria
*成績は下記該当項目を基に決定されます。
*クラス貢献度合計はコールドコールと授業内での挙手発言の合算値です。
Participants will be graded according to their voluntary participation during case discussion, professors session and workshop. Also cold call will be used for participants, to test their preparation. Final presentation and test will represent 50% of the weight.
*クラス貢献度合計はコールドコールと授業内での挙手発言の合算値です。
| 講師用内規準拠 Method of Assessment | Weights |
|---|---|
| コールドコール Cold Call | 10 % |
| 授業内での挙手発言 Class Contribution | 40 % |
| クラス貢献度合計 Class Contribution Total | 50 % |
| 予習レポート Preparation Report | 0 % |
| 小テスト Quizzes / Tests | 0 % |
| シミュレーション成績 Simulation | 0 % |
| ケース試験 Case Exam | 0 % |
| 最終レポート Final Report | 20 % |
| 期末試験 Final Exam | 30 % |
| 参加者による相互評価 Peer Assessment | 0 % |
| 合計 Total | 100 % |
評価の留意事項 Notes on Evaluation Criteria
Note on GradingParticipants will be graded according to their voluntary participation during case discussion, professors session and workshop. Also cold call will be used for participants, to test their preparation. Final presentation and test will represent 50% of the weight.
教科書 Textbook
- L. Williams「Disrupt: Think the unthinkable to spark transformation in your business」FT Press（2015）
参考文献・資料 Additional Readings and Resource
Recommended readings for the future
Textbook: Malone, T. W. (2018). Superminds: The surprising power of people and computers thinking together. Little, Brown.
Daugherty, P. R., & Wilson, H. J. (2018). Human+machine: reimagining work in the age of AI. Harvard Business Press.
Rudder, Christian. Dataclysm: Love, Sex, Race, and Identity--What Our Online Lives Tell Us about Our Offline Selves. Crown, 2014.
Harari, Y. N. (21). Lessons for the 21st Century. Spiegel & Grau.
Textbook: Malone, T. W. (2018). Superminds: The surprising power of people and computers thinking together. Little, Brown.
Daugherty, P. R., & Wilson, H. J. (2018). Human+machine: reimagining work in the age of AI. Harvard Business Press.
Rudder, Christian. Dataclysm: Love, Sex, Race, and Identity--What Our Online Lives Tell Us about Our Offline Selves. Crown, 2014.
Harari, Y. N. (21). Lessons for the 21st Century. Spiegel & Grau.
授業調査に対するコメント Comment on Course Evaluation
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担当教員のプロフィール About the Instructor
An expert on the subjects of Leadership and Disruptive Innovation by Big Data and Artificial Intelligence, Prof. Toscani caters to the board of companies, academic and not-for-profit institutions, like CCL (Belgium), SDA Bocconi (Italy), Telefonica (Spain, Argentina, Brazil, Guatemala), Deppon Logistics (China), Navozyme (Singapore), Nike and Megafon (Russia), Parexcelence (India).
Prof. Toscani started his career as a patent attorney for chemical plants in Lugano, Switzerland. He also has an MBA, and a PhD in Management from the Royal Institute of Technology, Stockholm, and is involved in research projects on Machine Learning and CEO mindset with IESE Business School. He has also published in leading publications like Marketing Intelligence and Planning, Journal of Non-Profit and Public Sector Marketing etc.
In addition to holding the extensive management and teaching experience, Prof. Toscani also presided over the Global Management Programme at Universitas Telefonica, co-managed and taught with IESE Business School. He is currently the Academic Director and professor for the Programme in Artificial Intelligence for Business Strategy in ESADE Business School, Barcelona and Madrid, Spain.
His vast experience on Big Data/AI springs from his work on location-system behavioural analysis and time perception project at Telefonica. He has also participated in the programme on AI for business at the Massachusetts Institute of Technology, and is currently setting a start-up delivering Artificial Intelligence services.
He plays the side flute in a classical music orchestra, is a yoga teacher, a vipassana meditator and an ultra-trail runner with personal record of 120 km. Based out of Barcelona, he has worked, visited or lived in over 100 countries, most recently having cycled solo halfway across the Black Sea and Caucasus, published in his youtube channel.
（実務経験 Work experience）
Professor and Advisor
• ESADE Business and Law School, Barcelona, Spain. Adjunct Professor
Law School degree: Digital Technologies Impact
MIBA (Master in Business Analytics): Human + Machine strategy
Executive education: Remote teams; Digital Entrepreneurship; Leadership in AI.
• Pacifico Business School, Lima, Perú. Adjunct Professor
• University of Bath, Bath, UK. Visiting Professor. MBA Programme: Contemporary issues at the time of Big Data/ Artificial Intelligence
• NUCB Nagoya University of Commerce and Business, Nagoya, Japan. Visiting Professor. MBA programme: Disruption by Big Data|Artificial Intelligence
• Ranepa Business School, Moscow, Russia. Visiting Professor. Global MBA: Digital Entrepreneurship
• Politecnico, Milan, Italy. Visiting Professor. Master in Strategic Design: Design Thinking in AI
• Navozyme, Singapore. Advisory Board.
• Programme Director Universitas Telefónica. Barcelona, Spain.
Direction and Teaching of the Programmes for Telefonica Global Executives
Refereed Articles
- (2023) The effects of the COVID-19 pandemic for AI practitioners: the decrease in tacit knowledge sharing. Journal of Knowledge Management
- (2023) Cómo el trabajo en remoto está reduciendo el conocimiento implícito. Harvard Deusto Business Review
- (2022) Los mundos virtuales, un nuevo reto para la propiedad industrial de las marcas. Harvard Deusto Business Review
- (2022) The role of reciprocity and reputation in service relationships with Arts organisations. Journal of Services Marketing
- (2018) Arts Sponsorship Versus Sports Sponsorship: Which Is Better for Marketing Strategy?. Journal of Nonprofit & Public Sector Marketing