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This document discusses how design thinking can offer business schools an alternative approach to management education. It argues that traditional case study methods focus too narrowly on analysis and have a static view of business. In contrast, design thinking uses abductive reasoning to unpack "wicked problems", takes a user-centered perspective, and emphasizes practice as much as theory. The document proposes that business schools adopt the "3Ps" of design thinking: positioning students in context, fostering engagement, and developing their mindsets. This approach could help business programs better teach innovation, entrepreneurship, and managing in dynamic markets.
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Data, Ethics and Healthcare
Support for the keynote "Data, Ethics and Health Care,”, Keynote, Creating Value in Health Care through Innovation Management, May 16,2019, Deusto, San Sebastien
AI and Managerial Decision Making
Support for the presentation • “Does AI Improve Managerial Decision-Making?”at the International Conference Airport Operational Excellence, Jan. 28-30 2019
Les enjeux éthique de l'IA
Support de mon intervention à Léonard Paris le 19 juin 2019 sur les enjeux éthique de l'IA
Technology and Innovation - Introduction
This document provides an introduction to a training module on digital technologies and innovation. It discusses artificial intelligence (AI) and its role in innovation. The training will cover topics like the building blocks of AI, digital economics, the internet of value, decision making, data ethics, and how AI can be used as a lever for innovation. It outlines the course administration, including grading based on a final exam and an innovation project. The introduction discusses using data to help management make better decisions and defines the key elements involved in applying analytics, including defining the problem, making predictions, evaluating outcomes, and taking action. It also outlines the machine intelligence continuum from rule-based automation to developing human-like intelligence.
Technologies and Innovation – Ethics
This document discusses ethics in digital technologies and business analytics. It addresses issues such as personal privacy, biases in automated decision-making, transparency in data collection and use, and the relationship between truth, trust and new technologies like blockchain. Key topics examined include personal data rights, accountability for data loss or misuse, limiting data extrapolation and manipulation, and understanding the context and limits of data science methods.
Technologies and Innovation – Decision Making
This document discusses digital technologies and innovation in decision-making. It covers several topics:
- The building blocks of algorithms and how they are improving productivity through reinforced learning and predictive analytics.
- Different types of decisions individuals, groups and organizations face ranging from programmed to non-programmed to knowledge-based decisions.
- Models for thinking including System 1 fast thinking vs System 2 slow thinking, and simple, complicated vs complex domains.
- The benefits and limitations of using models to make decisions. Common causes for poor decision-making include not understanding what is being measured or the measurement methods.
- Different types of machine learning including supervised, unsupervised, semi-supervised and reinforcement learning and examples of
Technologies and Innovation – The Internet of Value
The document discusses digital technologies and innovation, including the building blocks of innovation, digital economics, and the internet of value. It covers topics like the data revolution, time and space organization, analytical methods, and decision making with data ethics. The agenda includes an introduction and sessions on these various topics related to digital innovation.
Technologies and Innovation – Digital Economics
This document provides an overview of a workshop on digital technologies and innovation. It includes an agenda with sessions on the building blocks of innovation, digital economics, the internet of value, decision making, and data ethics. The document contains questions to prompt discussion on topics like the Fourth Industrial Revolution, how digital natives approach business, and how values are changing in a digitized world. It also includes introductory sections on data types, big data, the importance of context in data, and transforming data into actions.
Technologies and Innovation – Innovation
This document provides an agenda and overview for a session on digital technologies and innovation. The agenda includes introductions on the building blocks, sources, and practice of innovation. It discusses Peter Drucker's views on where innovative ideas come from by analyzing opportunities within companies, industries, and the social environment. It also summarizes theories on innovation from Joseph Schumpeter, who distinguished invention, innovation and imitation, and from Birkinshaw, who categorized types of innovation. The document outlines frameworks for innovation from Bessant and Tidd, Tidd et al., and Rothwell's five models of the innovation process. It discusses open innovation models like InnoCentive and examples from Spotify and Giffgaff. It concludes with perspectives
Technologies and Innovation - Introduction
This document provides an introduction and agenda for a course on digital technologies and innovation. The 3-sentence summary is:
The course aims to help students understand innovation in various industry settings through 6 sessions covering topics like artificial intelligence, digital economics, decision making, and data ethics. The objective is for students to analyze case studies, qualify available data, apply appropriate analytical methodologies, and integrate visual communications to transform data into calls for collective action. Students' grades will be based 50% on participation in an innovation project presentation and 50% on a final multiple choice exam.
Group 5 - Narayana Health
Narayana Health is an Indian healthcare organization that operates hospitals and cardiac care centers. It uses a low-cost business model focusing on economies of scale. Narayana Health leverages data and analytics to reduce costs, improve processes, and enhance patient care. It utilizes tools like PowerBI and Azure to conduct real-time analysis, predictive modeling, and AI-driven initiatives to optimize resource usage, surgery costs, and length of hospital stays. The organization's strategic use of data science supports its mission to deliver affordable, high-quality healthcare.
Group 4 - DHL
DHL uses data science and AI throughout its operations to improve efficiency, customer experience, and decision making. It analyzes data such as origin/destination, size/weight of shipments, vehicle locations, and customer interactions to predict delays, demand, and risks. Machine learning helps optimize routing, staffing, and warehouse management. AI tools identify damaged packages and monitor suppliers for risks. This informs decisions that lead to reliable delivery, increased profits and customer satisfaction.
Group 3 - BBVA
BBVA underwent a transformation process focused on digitalization and improving the customer experience. It developed new data analytics capabilities including algorithms and machine learning models to gain insights from customer data and optimize processes like trading. This allows BBVA to better predict trends, evaluate outcomes, and take actionable steps to create opportunities for customers.
Group 2 - Byju's
Byju's is an Indian educational technology company that provides online video-based learning programs. It uses a freemium business model and has experienced rapid growth and revenue increases. Byju's strategically uses data by collecting information on how students engage with educational videos to improve content. It also uses data science techniques like behavioral analytics and association analysis to provide personalized learning experiences and tackle challenges in the education sector like standardization. Data allows Byju's to track student perception, predict engagement, evaluate content tagging, and take actions like feedback and customized learning paths. This individualization has improved the learning experience through adoption of data science.
Group 1 LinkedIn
LinkedIn uses Hadoop and other big data tools to analyze data from its over 400 million user profiles. It focuses on data science techniques like recommendation engines to build products that suggest connections, jobs, and skills comparisons to users. LinkedIn embeds data scientists with product teams and uses an AI Academy to equip employees to optimize AI experiences for members. It analyzes user data like profiles, activities, likes and searches to build a comprehensive understanding of professionals and their networks.
Analytics in Action - Introduction
This document provides an overview of a business analytics course. It includes the following key points:
- The objective of the course is to build students' knowledge of applying business analytics in various industrial settings.
- The course will cover fundamentals, case methodology, and involve analyzing case studies from industries like community management, education, and health analytics.
- Grading will be based on participation, a mid-term exam, and a final essay exam. Students will analyze case studies and apply appropriate data science techniques.
Analytics in Action - Storytelling
This document provides an introduction to data storytelling and visual communications. It discusses how visuals can help people remember information by appealing to how humans learn visually. Effective visuals use principles like proximity, similarity, closure and symmetry to group related elements. The document then outlines a process for designing visual CVs and storytelling, including defining a pitch, producing the message, testing and revising. An assignment section provides evaluation criteria and asks students to introduce themselves, their experience, and how they fit a company's needs in a visual storytelling format.
Analytics in Action - Data Protection
The document provides an overview of the General Data Protection Regulation (GDPR) and its implications for data protection and privacy. In 3 sentences: The GDPR imposes new obligations on companies regarding how personal data is collected and processed in order to protect European citizens' privacy rights. It requires companies to implement privacy by design principles and conduct data protection impact assessments. The GDPR aims to give citizens more control over their personal data and holds companies accountable for any data breaches or violations of individuals' privacy rights.
More from Lee Schlenker (20)
Technologies and Innovation – The Internet of Value
Technologies and Innovation – The Internet of Value
Celda
- 1. CELDA Porto l October 25 , 2014 Design in practice Improving management education Dr. Lee SCHLENKER Ph. D. l Professor @GroupeESCPau Dr. Sébastien CHANTELOT Ph. D.l Professor @GroupeESCPau
- 2. Can Design in Practice offer business schools an alternative vision for management education?
- 3. 1.Context 2.Hero 3.Roadmap 4.Happy end?
- 4. Introduction Improve quality of business school education Define design thinking conditions Storytelling approach
- 5. Context The bias of case study methodology Individual Analysis of selected elements and their relationship Teamwork Discussion and comparison of each stage resulting from individual analysis Group sessions Under the guidance of the professor, debate will rise to a wide variety of perspectives and opinions Markulis, 1985 Burns, 1984
- 6. Context Business is too narrowly defined Bennis and O'Toole, 2005 Snowdon and Boone, 2007 Disciplinarian silos Static view of the market How relevant is the curriculum? What is the link to Innovation and value?
- 7. Context Misguided focus on teamwork Dunne and Martin, 2006 Hackman, 2011
- 8. Context The need to redefine productivity Mintzberg, 2004 Sinofsky, 2013 Harder l better l faster
- 9. Hero User-centered design Martin, 2004 Brown, 2009
- 10. Hero Abductive reasoning Inductive Deductive Abductive Fann, 1970 Brown, 2009 generalize existing narrow down existing choices create space to generate new ideas
- 11. Hero Unbundle « wicked » problems Fann, 1970 Brown, 2009
- 12. Schlenker and Mendelson, 2008 Waloszek , 2012 Hero Stress practice as much as theory
- 13. People Mindsets Approaches Practices People experience collaborative work through processes Places Space Tools Gateways The design of place shapes process execution and improvement Process funnel information through Gateways inside / outside Intensity of experiences feed and strengthen processes Places Process Data Information content People design Places as mirrors of their own experience Place influences people’s motivation and performance. Place shapes learning opportunities Gateways inside / outside Roadmap The 3Ps of Design in Practice
- 14. Roadmap Position in student in context
- 15. Roadmap Foster student engagement
- 16. Roadmap Develop each student’s state of mind
- 17. Happy end?
- 18. Happy end ? The foundations of management MBA France India
- 19. Happy end? Managing innovation and entrepreneurship Graduate programme
- 20. Happy end? The onboarding guide
- 21. Conclusion XXXXX xxxxxxxxxxxx XXXXX xxxxxxxxxxxx XXXXX xxxxxxxxxxxx
- 22. Thank you! www.DSign4.education @DSign4Education lee@lhstech.com sebastien.chantelot@esc-pau.fr