シラバス Syllabus

授業名 Leading in the Age of Data
Course Title Leading in the Age of Data
担当教員 Instructor Name Ricardo Lim
コード Couse Code CLD207_G22V
授業形態 Class Type 講義 Regular course
授業形式 Class Format Live Virtual
単位 Credits 2
言語 Language EN
科目区分 Course Category 発展科目300系 / Advanced & Specialized
学位 Degree Exed
開講情報 Terms / Location 2022 GSM ONLINE Fall

授業の概要 Course Overview

Misson Statementとの関係性 / Connection to our Mission Statement

The new frontier of Asia will require looking at a flood of data from global customers. Competition and globalization now demand that organizations use data to make better decision, predict customer behavior, and store knowledge.

授業の目的(意義) / Importance of this course

For the past ten years top consulting firms like McKinsey and KPMG have espoused “Digitalization” (or digitization). It seems everything now is “digital.”—Digital leadership and culture. Digital responses to COVID. Digital strategies. What is digitalization about?

Digitalization is a fancy term for data. It is composed of big data, data science, and data analytics. It has jargon such as recommender, AI, machine learning. These tools are mystical and opaque to most, even intimidating. We will spend the first part of the course on demystifying the jargon. We will do a light treatment of these concepts, in order to understand the scientific “engines” of digitalization. We study hard-nosed big data crunching, such as customer lifetime value of Telcos and of customer mining of Harrah’s in Las Vegas to some basic predictive analytics tools.

Data is also part of strategy. In the second part, we discuss how digital initiatives impel business models. Where do these fit on platforms and two-sided models like AirBnb and Uber? Also, given the new technologies, how do we capture data to fuel the data engines?

In the last part we cover data for non-profits and doing social good for the base of the pyramid. We also study the social elements of information, as well as the limits to analytics and big data: of fake news, of information overload and bias, and information fatigue.

到達目標 / Achievement Goal

By the end of the course, participants should::

1. Understand how data fit into modern business models
2. Know the basic elements of information, and the limits to analytics and big data
3. Discern false or low-quality information

本授業の該当ラーニングゴール Learning Goals


LG1 Critical Thinking
LG3 Ethical Decision Making
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 the end of the course, participants will be able to:

1. Do a broad survey of the digitalization movement in the 2000s.
2. Survey the Understand terms such as predictive vs prescriptive stats, AI, machine learning to be able to communicate with and manage data scientists and analysts.
3. Understand the mechanics of big data and interpretation of predictive tools, and translate results into practical business insights.
4. Explore various business models and how digitalization enables
5. Understand the social aspects of information.
6. Understand the limits and pitfalls of information.

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 25 %
参加者中心型 Participant-Centered Learning ケースメソッド Case Method 50 %
フィールドメソッド Field Method 25 %
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

The course is about class discussions. I will grade your participation in both class discussions and learning team breakout rooms. How actively do you participate by sharing your ideas with others, collaborating with teams, and contributing insights to the group? This is 70% of your grade.

I will be assigning a take home paper to be submitted about a week after class end. This will count for about 30% of the grade.

授業スケジュール Course Schedule


S01 - Introduction. Being Digital. Analytics vs Science. Information Rules (network effects, lock in).

S02 - 03 Worry-free STATS refresher

Business Analytics at a Glance BEP 498
Cases from Ricky Lim:

Correlation and regression
Collaborative Filtering


Stats Exercise
Case discussion on Telenor.

Case from Ricky Lim : Logistic Regression (an intro to Machine learning)
Telenor: Revolutionizing Retail Banking in Serbia IN1328


S01 - AI driven busness models Lemonade discussion
S02 Hubspot CRM discussion

Lemonade: Delighting Insurance Customers with AI and Behavioural Economics IN1673
HubSpot and Motion AI: Chatbot-Enabled CRM 9-518-067


S01 data for social good

: M-KOPA Solar: Using Digital Disruption to Connect the World's Poor LBS 188




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 70 %
クラス貢献度合計 Class Contribution Total 70 %
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 30 %
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

I will reward in-class contributions during case discussions. These include starting the case, summarizing others’ viewpoints, creating debate with others, concluding. I like good questions and how you clarify difficult concepts. I like active group participation, but participation must be positive, building, and collaborative—not attacking or criticizing.

Instead of a final exam, will assign a final take-home paper, 750 word limit, to be submitted one week after class end. The topic will be assigned in class.

使用ケース一覧 List of Cases


教科書 Textbook

  • 配布資料

参考文献・資料 Additional Readings and Resource


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

This was executed for the first time in November 2021. Please refer to NUCB administration for evaluation details.

担当教員のプロフィール About the Instructor 

Ricardo A. Lim, Ph.D. is a professor at the NUCB Business School and visiting professor at Ritsumeikan APU, Beppu, Japan. He was a former Dean of AIM, former President of the Association of Asia Pacific Business Schools (a consortium of 80 Asian B-schools), founding member of the Global Network to Advance Management at Yale Business School, and Asia-Pacirfic Advisory Council of AACSB. He teaches information systems, statistics, analytics, and design thinking x lean x agile concepts. He has published in the MIS Quarterly and the Journal of Management Information Systems, and serves as Associate Editor for the International Journal of Business and Economics, Taiwan. He currently consults for education and financial services sectors. Before joining academe he was a senior consultant for the Computer Sciences Corporation in Boston and Siemens Computing in Manila. He has a Ph.D. from the U. of Southern California, an MBA from the U. of Virginia, and a B.Com. from McGill University.

Refereed Articles

  • (2023) Determinants of Conspicuous Consumption in Smartphones. Asia Pacific Journal of Information Systems 33(3): 2288-5404
  • (2023) A Study of Satisfaction and Loyalty for Continuance Intention of Mobile Wallet in India. International Journal of E-Adoption (IJEA) 15(1): 1937-9633
  • (2021) Developing and Testing a Smartphone Dependency Scale Assessing Addiction Risk. International Journal of Risk and Contingency Management 10(4): 2160-9624
  • (2021) Business Model Innovation: A Study of Empowering Leadership. Creativity and Innovation Management 1467-8691
  • (2021) The Effect of Reciprocity on Mobile Wallet Intention: A Study of Filipino Consumers. International Journal of Asian Business and Information Management 12(2): 1947-9638