シラバス Syllabus

授業名 Competing on Business Analytics and Big Data
Course Title Competing on Business Analytics and Big Data
担当教員 Instructor Name Ricardo Lim
コード Couse Code EST234_G22V
授業形態 Class Type 講義 Regular course
授業形式 Class Format Live Virtual
単位 Credits 1
言語 Language EN
科目区分 Course Category 入門科目0系 / Pre
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

New Age, New Frontier of Asia should know about Big Data and Analytics. However one must get past the initial awe and intimidation, and learn about the basic foundation of data and its glorious possibilities, and also severe limitations. For the non-data scientific manager, this course help unravel the mysteries of big data and analytics into their fundamental functions.

授業の目的(意義) / Importance of this course

This is a full two-day course. We will try to demystify Data analytics: What is the analytics and big data frenzy all about? Each time we watch Netflix or a Tiktok, or merely walk around the mall with our smartphone (location: on), we are being big-data-dissected and “predicted,” i.e., led to what we need to buy or watch next, whether we like it or not. The business press has added to the frenzy with jargon like “Digitization,” “AI,” “algorithms,” etc. We are awed and blinded by the science.

到達目標 / Achievement Goal

In this course participants will learn about

1. foundations of data and analytics. We dive beneath the jargon into the how-tos and applications of analytics. We do practical in-class exercises on data analytics. We should no longer feel the intimidation.

2. Basic statistical concepts, and focus on two predictive tools, recommender systems and regression, the foundations for many big data applications. No worries, non-numeric people! We will simplify and get into the practical understanding. You will not be a data scientist from this course (do you really want to?), but you should be able to intelligently talk to, and even manage, data scientists.

3. appreciate how analytics and big data fit into business models. Where do these fit on platforms and two-sided models like AirBnb and Uber? More generally, how does digitization enable business models?

本授業の該当ラーニングゴール Learning Goals


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

1. Get beneath the jargon of Digital and Big data, and understand terms such as predictive vs prescriptive stats, AI, machine learning.
2. Understand the mechanics and interpretation of regression and recommender systems.
3. Link analytics and big data applications into business models.
4. 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

There is a portion of this course where we refresh students’ memories of math and statistics. Basic, not expert skills are needed for this portion. Then the course migrates into class discussions. I will grade participation in both class discussions and possibly learning team breakout rooms. How actively do you participate by sharing your ideas with others, collaborating with teams, and contributing insights to the group? The most rigorous students will read all assigned texts. Other students should at least browse alternative readings on the web (provided in each section) as background reading.

授業スケジュール Course Schedule


S01 Introduction. Being Digital. Analytics vs Science. Information Rules (network effects, lock in).
S02 Class will do short exercises on CLV, Recommender systems

Cases written by Prof. Ricky Lim

1. Customer Lifetime Value
2. Correlation and Regression (with accompanying Excel spreadsheet)
3. Collaborative Filtering


01 Data enabled business models
Read https://steveblank.com/2010/11/15/creating-startup-success-customer-development-business-model-design/

There is a 102-slide embedded slideshare deck. It is a good, actually, easy-to-view PPT on the business model canvas
See also: http://theleanstartup.com/ and https://steveblank.com/slides/

02 Disruptive AI-driven business model: Lemonade

Why the Lean Start-up Changes Everything R1305C-PDF-ENG
Lemonade: Delighting Insurance Customers with AI and Behavioural Economics – IN1673






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. I will assign a final term paper, 1000 words limit, to be submitted one week after completion of the course. 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

I executed this course in 2021. It got overall ratings of 4.58, with a high of 4.8 Here were some student comments:

“It is useful for students not so firm in statistics and data analysis.”
“The course materials were very clear and well organized, and the explanation of complex topics made it very understandable. The teacher was really encouraging us to speak out our ideas and built up on them which made the conversation very dynamic.”
“I really enjoyed the practical exercises with data and regression.”
“Sharing of clear explanations, clarifications of concept, real life examples, references for future interest”

There were some negative comments as well”

“The pace was too slow for me because I was already very familiar with most of the content.”

To answer the negative point, experience statisticians/analytics students may find the course too rudimentary. We designed the course with the non-data scientific manager in mind. This course is not necessarily for beginners, but for people who are intimidated about the details of analytics.

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