International Campus - HEC Montréal
The objective of the course is to stimulate the students’ interest and understanding of the digital transformation of firms. Firms are contemplating an incredible pace of change in the technologies they use. This is totally new. In the past decade, firms have seen the acceleration of cloud computing, the discoveries of new machine learning techniques and access to massive amounts of data as well as new data (unstructured data).
This course will be offered from June 7, 2021 to July 3, 2021.
This industrial revolution is a double edge-sword: it is an unprecedented opportunity for firms to benefit from these incredible inventions and innovations. It is also an unprecedented set of risks: (1) competitors can be more agile and take the lead, (2) startups can flourish within my industry but can also come from another industry with a general-purpose technology, and (3) the lack of agility in my firm may impede the digital transformation of my business model.
The objective of this course is to introduce the major themes of digital transformation, using different methods from data science. By using a coding approach, students will literally feel the new power they have access to as managers. For example, how can data science be used to take a fresh look at global innovation? The use of APIs and data packets on innovation of multinational companies and on patents around the world will help answer this question. How can data science be used to analyze the global financial industry? How can data science be used to analyze the international business environment? How can data science be used to analyze global risks, such as the COVID-19 pandemic? What does it mean for the digital transformation of firms?
This course is about the inner dynamics of the digital transformation. It is not about a simple to-do list of recipes on how to do the digital transformation, it is about the understanding of the forces at stake in this AI-driven industrial revolution. Beyond relying on concepts and theoretical frameworks, this course will expose students to the inner mechanics of the digital transformation: algorithms. In other words, they will get their hands dirty. This is the best way to rely grasp the intricacies and the deep challenges of the digital transformation. Students will thus understand that the digital transformation is in fact a true digital revolution. It does require not only a move from one state of nature to a new one, but it also requires a total paradigm shift in the business model.
Throughout this course, students will learn and use the R language. Packages of algorithms from reproducible research will also be used in R, such as TensorFlow. The learning of the R language and the various tools will be supported and reinforced by access to additional resources (data, courses, APIs, packets, etc.) made available on the professor’s data science platform.
In the end, students will be exposed to new methods made possible by recent advances in artificial intelligence analysis models, easy access to data and the necessary computing power. This course will look at examples of firms that have implemented transformative projects from various industries. We will cover only a few industries (finance, retail, etc.). This course is structured around three pillars:
During this course, a number guest lecturers who have overseen digital transformation projects will intervene.
A taste for and openness to learning code, especially the functional language R
At the end of the course, the student will be able to:
The course is designed to achieve the following learning goals:
The main pedagogical approach is that of reverse pedagogy based on the analysis of case studies and small coding projects. The content of the sessions will be known in advance and students should arrive prepared in class. Case studies will be used. An online multi-player simulation will also be used. During each session, after a masterly presentation aimed at deepening the concepts and methods seen before the session, will follow an application on a major theme of digital transformation. For these topics, students will have access, for example, to databases on global patents, on all scientific articles on coronaviruses, on the most innovative multinational companies in the world, on the annual reports of the largest multinational companies, on Twitter conversations, etc. By using either structured or unstructured data, and combining this data with the appropriate models, students will be exposed to the new possibilities that are available to them.
The course combines two complementary teaching methods:
Much of the course is also based on seminars and talks from guests from Montreal, Boston, New York, Washington, etc. The quality of these meetings depends on adequate student preparation and an open dialogue between students and the professor/lecturer. These are essential prerequisites to understand the reality of business in the host county and to build an in-depth knowledge of international management practices.
To promote learning experience, students must also create activities. They are encouraged to contact companies and initiate small group meetings, discuss these meetings and other experiences using the professor’s data science platform.
[Deploy some information by clicking on the triangle-shaped pictogram]
Session 1. Introduction: The Industrial Revolution 4.0 [Click here]
The first session begins with a presentation of the lesson plan. During this presentation, the lesson plan will be contextualized through a discussion of the programming approach versus the software approach and the importance of exposure to data science. The data revolution for business will be illustrated as well as the history and recent developments in data science. Presentation of the development tools used during the course (R, RStudio, an instant messaging platform, a customized video conferencing platform for teamwork, etc.). Presentation of the international business field.
Session 2. Digital Strategy and Innovation [Click here]
This session will address the necessity for many firms to become a data-driven company. An overview of the challenges in building dynamic capabilities. Why is sustained innovation so difficult? In what ways do open and digital technologies accentuate these innovation challenges for incumbent firms and new entrants? We will induce the notion of innovation streams as well as a leader’s performance and/or opportunity gaps.
Session 3. Leadership, Innovation and Change [Click here]
This session will address the relevant elements to be considered to move from operations to execution. Innovation and digital disruption always involve large system change; change that often involves organizational identity shifts. This module focuses on the leader’s and his/her team’s role in leading large system change. We will explore the importance of identity and strategic clarity, thorough diagnoses, and detailed articulation of the firm’s future state. We then discuss tools to help lead power and politics, individual resistance to change, and maintaining control during turbulent transition periods. We finish with the issues of leaders and their personal roles (an associated leadership ironies) in leading large system change. We will illustrate these ideas with Ingrid Johnson at Nedbank and Sam Palmisano at IBM.
Session 4. Project Management [Click here]
This module starts with your organization’s strategy, objectives, and strategic intent. We focus on both analytics as well as passion. We discuss again performance and opportunity gaps with the Havas multimedia case. We then move to execution, conceptualizing the organization as a social and technical system in service of the firm’s critical tasks and associated interdependencies. We discuss the firm’s hardware (tasks and associated interdependencies, and structures, roles, and metrics) and its software (its capabilities and culture). We use this congruence model for diagnosing the roots of today’s performance gaps and/or factors that will get in the way of opportunistic strategic moves. We illustrate the congruence model and associated managerial problem solving with the Havas multimedia case.
Session 5. Programming and Data Science Systems 1/3: data management and manipulation [Click here]
Presentation of the Markdown language. Creation of a dynamic report. Presentation of the R language syntax. Use of R packages for data management. Presentation of CRAN and OPENCSI for packet access. Presentation of the principles of open science and reproducible research.
Session 6. Programming and Data Science Systems 2/3: Programming Interfaces (APIs) and Descriptive Statistics [Click here]
Use of Application Programming Interfaces (APIs) to build data pipelines. Explanations on obtaining descriptive statistics in R. Introduction to the use of APIs and introduction to the richness of unstructured data to inform empirical models in international business. We will use the EpiBibR package and the New York Times automatic programming interface to study the contribution of data sciences to the analysis of international risks, including pandemic risk and international political, cultural and economic responses.
Session 7. Programming and Data Science Systems 3/3: Building a data company [Click here]
In this session, we begin by reviewing examples of data science and the data science framework. We will use a case study to analyze the data utilization strategy of a Quebec company. We will look at this company’s internationalization strategy. Then, we will discuss data collection from R websites, data types and data formats and exploratory data analysis (EDA) and visualization. Merging data. Data cleansing in Table. Presentation of ggplot2 for data visualization. Examples of data used are data files from international business research papers.
Session 8. An Introduction to the Principles of Supervised and Unsupervised Learning [Click here]
In this session, we begin by reviewing examples of data science and the data science framework. We will use a case study to analyze the data utilization strategy of a Quebec company. We will look at this company’s internationalization strategy. Then, we will discuss data collection from R websites, data types and data formats and exploratory data analysis (EDA) and visualization. Merging data. Data cleansing in Table. Presentation of ggplot2 for data visualization. Examples of data used are data files from international business research papers.
Session 9. Introduction to the principles of decision trees and support vector machines [Click here]
This session covers basic classification approaches, such as decision trees and support vector machines (SVMs), and the challenges of using these highly flexible methods.
Session 10. Fintech, financial industry and new technologies: introduction to random forest methods and gradient reinforcement [Click here]
The financial industry is one of the first industries to have begun its digital transformation. The importance of managing internal data, but also of contextualizing it with external data while thinking about new data sources has accelerated this transformation. In this session, we discuss ensemble models, a powerful technique that allows for the combination of many models to create improved classifiers. We will use a case study based on a U.S. financial firm whose goal is to build predictive models to improve efficiency in decision making. We will also begin to address the ethical issues and statistical biases of data collection as well as the algorithmic methods themselves.
Session 11. Ethics in an International Context: Introducing Neural Network Principles [Click here]
This session covers the basics of deep learning, feature learning, feed-forward networks, training neural networks, convolutional neural networks (CNNs) and recurrent neural networks (RNNs). It will also be a session that will focus on the analysis of statistical biases and the ethical issue raised by the use of algorithms.
Session 12. Team Presentations [Click here]
Organized in teams of 3, students will produce a business analyst report on the digital transformation in a specific industry. This report should mobilize (1) the content seen in class and (2) the data science approach (APIs, data science platform, etc.).
Students are requested to take note of Article 12 of HEC Montreal’s regulations governing student activity which concern plagiarism and fraud, and to take particular note of the acts and gestures that are considered plagiarism or another academic violation (12.1), along with the applicable procedure (12.2) and sanctions (12.3), which range up to suspension and even expulsion from HEC. Violations are analyzed based on the facts and circumstances, and sanctions are applied accordingly.
Before the session:
That’s all !
For attribution, please cite this work as
Warin (2021, July 3). Thierry Warin, PhD: [Course] From Digital Transformation to Digital Revolution of Firms: Getting Your Hands Dirty. Retrieved from https://warin.ca/posts/course-digital-transf/
BibTeX citation
@misc{warin2021[course],
author = {Warin, Thierry},
title = {Thierry Warin, PhD: [Course] From Digital Transformation to Digital Revolution of Firms: Getting Your Hands Dirty},
url = {https://warin.ca/posts/course-digital-transf/},
year = {2021}
}