シラバス Syllabus

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 GLP228_G22N
授業形態 Class Type 講義 Regular course
授業形式 Class Format On Campus
単位 Credits 2
言語 Language EN
科目区分 Course Category 基礎科目100系 / Basic
学位 Degree MBA
開講情報 Terms / Location 2022 GSM Nagoya Fall

授業の概要 Course Overview

The mission of this course is to create knowledge within Big Data & AI, underlying the ethical elements that these technologies are implying, that boosts innovation within business and society. Being the professor Italian and teaching in 4 continents, this course will also give students the ability to bridge the gap between New Asia and the rest of the world.
This course is instrumental because it will prepare leaders to harness the power of disruptive innovation before their competitors do. Participants will gain a deeper understanding of what disruptive innovation is and will learn how to spot potential threats and opportunities in their own business.
By studying this course, students will be able to:
Understand the forces of disruption that could impact their business and see how to spot them early
Uncover a new approach for customer centricity to spot and shape new opportunities by applying the jobs to be done framework
Learn, through practical examples, how to leverage new business models and potential application for their companies
Get hands-on experience using the disruptive innovation toolkit

本授業の該当ラーニングゴール Learning Goals


LG1 Critical Thinking
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

By studying this course, students will be able to:
Make strategic and confident decisions using the best data and AI technology
Learn a valuable business methodology that helps you pinpoint weaknesses and discover new opportunities
Earn higher profits by better understanding the future of the business, processes and customers

SDGsとの関連性 Relevance to Sustainable Development Goals

Goal 4 質の高い教育をみんなに(Quality Education)

教育手法 Teaching Method

教育手法 Teaching Method % of Course Time
インプット型 Traditional 50 %
参加者中心型 Participant-Centered Learning ケースメソッド Case Method 30 %
フィールドメソッド Field Method 20 %
合計 Total 100 %

学習方法、レポート、課題に対するフィードバック方法 Course Approach, Report, 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 the intelligence cycle, disruptive innovation and, of course, the basics of machine learning, 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 report delivery.
Report: Individual case exam
Feedback: Written, on final report
Required amount of preparation: there will be 9 cases and a final individual report. The cases require at least 2 hours each, Minimum 18 hours.The final report is approximately 10 hours
No prerequisite Knowledge/Experience

授業スケジュール Course Schedule


AI and Big Data Now - Why This Time it’s Different
Artificial 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.

• Case 1 Bytedance
• Case 2 Mcdonald’s can a behemoth lead in the era of artificial intelligence?
• Case 3 Sensetime group limited: business model and expansion


Consolidating Your AI/Big Data Strategy
A 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?

• Case 4 GE Digital: Racing to Lead Industry 4.0
• Case 5 Nedbank group: leadership and adaptive space for digital innovation
• Case 6 The future of the fashion industry in a post-covid-19 world
• Disruptive Innovation Workshop


The Future of business and work
The 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.

● Case 7 Merck: Covid-19 Vaccines
● Case 8 Kathy Fish at Procter & Gamble: Navigating Industry Disruption by Disrupting from Within
• Case 9 Choosy
• Individual Final exam





成績評価方法 Evaluation Criteria

講師用内規準拠 Method of Assessment Weights
コールドコール Cold Call 0 %
授業内での挙手発言 Class Contribution 40 %
クラス貢献度合計 Class Contribution Total 40 %
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 20 %
最終レポート Final Report 40 %
期末試験 Final Exam 0 %
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

Participants will be graded according to their voluntary participation during case discussion and professor session. Also cold call will be, eventually, used for participants who do not participate, to test their preparation. Final report and cases preparation represent overall 60% of the weight.

A student’s final grade in this course will be based on the following weighting:

40% Class Participation
60% Individual final project and cases

Assessment reflects the quality of a student’s active participation in class discussions. Much of a manager’s success depends on communication; therefore, effective oral communication will constitute the student’s grade. Written work should be clear, logical, grammatically correct, spell-checked, persuasive, supported by examples, and backed up by citations for any data, ideas or other content used. It should represent the student’s best effort. To do well on the writing reports, you will need to incorporate and apply the course readings.

A note on Class participation:
Grading class participation is necessarily subjective. However, I try to make it as “objective as possible”. Some of the criteria for evaluating effective class participation include:

1 Is the participant prepared? Do comments show evidence of analysis of the case? Do comments add to our understanding of the situation? Does the participant go beyond simple repetition of case facts without analysis and conclusions? Do comments show an understanding of theories, concepts, and analytical devices presented in class lectures or reading materials?
2 Is the participant a good listener? Are the points made relevant to the discussion? Are they linked to the comments of others? Is the participant willing to interact with other class members?
3 Is the participant an effective communicator? Are concepts presented in a concise and convincing way?

Class Participation 40%

This course covers a significant amount of content and much of the learning comes from in-class exercises and discussion. Therefore, students are expected to attend all class sessions, complete all assigned readings and come prepared and ready to participate. Attendance will be taken and participation will be evaluated at each class session. Participation in all on-campus sessions in their entirety is mandatory, and students may not be late or leave early for any of these sessions. Failure to be in attendance for the entirety of the session will result in removal from the class.

Individual final project and cases 60%
The Individual final project is meant to be a culmination of all the learnings in the class, that is reinforced by the cases; to do well on this report it is imperative to draw upon the readings and discussions in presenting your analysis.

Please kindly explain in your report the solution you propose for the problem and deeper need you have identified.
• Write a word document (with no upper or lower word-count limits) where you demonstrate what you have learnt in class, by answering in details, giving at least an example for each questions, all the points below.

1. What is the problem? (Distinguish problem from pain points)
2. Who has the problem? (Define your user by age, gender, social status, what type or product or service is purchasing/receiving, the level of satisfaction, the pain points, the frequency of use…)
3. What is your proposed solution? (state clearly in a sentence and repeat it at the end of the report A [label] that allows [users] to [benefit] by [method])
4. How did you reach this solution? (Explain in detail your disruptive innovation process across the 4 dimension of expansion, reductio…)
5. What is the value of solving that problem with your solution? (Show why this solution has value, why it is useful to that user, based on the need/deeper need you identified by observing/thinking of the user)
6. What is the technology you propose for your solution? Why? (Define what technical solution you have found, by searching what is available in the market)
7. What are the human skills required to solve the problem? Why? (Define what human skills and/or training are necessary, by searching what could be the technology limitations that need to be compensated by the human)
8. What is your most important learning for this course? (What is different now, compared to before you started the course)

Please take into account that I reward critical thinking and specifically
• RELEVANCE is the solution addressing the right problem?
• COHERENCE how well does the solution fit?
• EFFECTIVENESS is the solution achieving its objectives?
• EFFICIENCY how well are resources being used?
• IMPACT what difference does the solution make?
• SUSTAINABILITY will the benefits last?

So, jot down what questions you have asked yourself, to reach the conclusions.
Do your best to show that you have been critical, so what you propose makes sense from a business, technology and ethical/legal point of view.

Try not to just propose a solution that is neither impossible to produce, advertise and monetise, nor propose to use technology as a magic wand.

Think instead of a problem first, without considering technology at this stage.

Think of a possible ecosystem, a possible alliance, a different business model or a new model for revenues (the analogic vs. digital toothbrush example), the data you need to have and why these data.

Then think how technology could help you, by looking at what is available on the market.

I do not expect you to propose a detailed technical solution, but, yes a business sound solution using a technology that may have been used by someone else and you could use too.

Remember to list briefly how you are going to integrate the solution, to overcome skepticism from investors and solution users. Just show that you understand the final goal of using these technologies to solve your problem. And of course, explain what are the business and ethical implications.

The assessment criteria are:

Coherence: the smooth and logical flow of writing

Consistency: the uniformity of style and content

Originality: the ability to think independently and creatively

Accuracy: explaining in detail what is your solution about

使用ケース一覧 List of Cases


教科書 Textbook

  • Luke Williams 「Disrupt: Think the unthinkable to spark transformation in your business. 」FT Pearson FT Press; 2nd edition(2015)978-0133995909

参考文献・資料 Additional Readings and Resource

Davenport, T. H., & Kirby, J. (2016). Only humans need apply: Winners and losers in the age of smart machines. New York, NY: Harper Business.
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.
Harari, Y. N. (2019). Lessons for the 21st Century. Spiegel & Grau.
Crawford K. Atlas of AI: The Real Worlds of Artificial Intelligence (2021). Yale University Press.

授業調査に対するコメント Comment on Course Evaluation

Previous comments on this course have addressed a further need to provide examples and cases, that have been implemented into this current one.

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

  • (2018) Arts Sponsorship Versus Sports Sponsorship: Which Is Better for Marketing Strategy?. Journal of Nonprofit & Public Sector Marketing
  • (2018) Sponsees: the silent side of sponsorship research. Marketing Intelligence & Planning 0263-4503