class: center, middle, inverse, title-slide # Digital Transformation or Digital Revolution? ### <div class="line-block">Thierry Warin, PhD<br /> HEC Montréal</div> --- ## Introduction > The era of data is upon us. It is proliferating at an unprecedented pace, reflecting every aspect of our lives and circulating from satellites in space through the phones in our pockets. > The data revolution creates endless opportunities to confront the grand challenges of the 21st century. > Yet, as the scale and scope of data grow, so must our ability to analyze and contextualize it. --- ### Objectives - 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). --- ### Motivation - 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. --- ### Motivation - 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? --- ### Motivation - 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. --- ### Platform - 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. - Students will have access to a dedicated Data Science-based LMS: [https://warin.ca/lms/](https://warin.ca/lms/) --- ### Motivation 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: - The first 4 sessions will be about the **management of a digital transformation**, - The next 3 sessions will be about **programming in R**, - The final 5 sessions will be about the **latest developments in Machine Learning** and how they have been used in digital transformation projects within companies. During this course, a number **guest lecturers** who have overseen digital transformation projects will intervene. --- ### Themes - Introduction to Data Science Methods to apprehend the inner dynamics of the digital transformation - Use of 'useful' data - Use of algorithms applied to the digital transformation - Introduction to Machine Learning in the Context of the digital transformation --- ### Prerequisites - A taste for and openness to learning code, especially the functional language R --- ### Evaluation - Individual Participation [20%] - Team Project [30%] - Reflection Paper [50%] --- ### Further Information > [www.warin.ca](https://www.warin.ca) --- ### Further Discussions - Marty F. & Warin Th. “Concurrence et innovation dans les écosystèmes numériques à l’ère de l’intelligence artificielle”, Concurrences / Competition Law Review, Vol. 1, pp. 36-41, February 2020, https://www.concurrences.com/en/review/issues/no-1-2020/on-topic/digital-competition-en - Marty F. & Warin Th. “The use of AI by online intermediation platforms. Conciliating economic efficiency and ethical issues” (with Marty, F.), Delphi - Interdisciplinary Review of Emerging Technologies, Vol 2, Issue 4, pp. 217 - 225, 2019 [DOI: 10.21552/delphi/2019/4/11], https://doi.org/10.21552/delphi/2019/4/11 - Marty F. & Warin Th. "Keystone Players and Complementors: An Innovation Perspective" CIRANO Working Papers 2020s-61 https://cirano.qc.ca/files/publications/2020s-61.pdf