Introduction
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This document provides an introduction and agenda for a course on digital technologies and innovation. It outlines the following key points: - The course will help managers understand how technologies can enrich their careers and improve decision making through data analytics and machine learning. - The instructor has experience working with major tech companies and conducting case studies and conferences in Europe. - The objective is to build students' knowledge of applying business analytics in different industry settings. - Topics will include the data revolution, digital technologies, decision making, innovation, and social business. Participation and a final exam will determine grades.
Introduction
- 1. Digital Technologies and Innovation Introduction April 2018 http://DSign4Change.com
- 2. ©2016 L. SCHLENKER Agenda Introduction The Data Revolution Time, Space and Organization The Analytical Method Introduction
- 3. Module Facilitator I work with managers to help them understand how enterprise applications, web and mobile technologies can enrich their careers. The client portfolio in the ICT industry includes Microsoft, Apple, Ernst & Young, France Telecom, HP, IBM, Oracle and SAP . The work with the IT industry in Europe has included fifty partner and customer conferences, a dozen case studies, and various marketing support activities. Prof. Lee SCHLENKER, The Business Analytics Institute Mail : lee@lhstech.com Skype : leeschlenker Web : www.leeschlenker.com Introduction
- 4. • Management is about taking decisions • Improving decision making through the study of digital economics, managerial decision making, machine learning and data storytelling • Innovation isn’t a consequence of technology, but of a state of mind http://baieurope.com lee@baieurope.com @DSign4Analytics Skype : leeschlenker ©2017 Business Analytics Institute Introduction
- 5. Course Portal: http://DSign4Change.com ©2017 Business Analytics Institute The objective of this course is to build the students’ knowledge of the practice of Business Analytics in a variety of industrial settings Introduction
- 6. This a place where managers and students of management can discuss and debate best practises in the digital economy, new developments in data science and decision making. Ask questions and get practicable answers, and learn how to use data in decision making. Analytics for Management https://www.linkedin.com/ groups/13536539 Introduction
- 7. • How does the author define the “Fourth Industrial Revolution”?* • The concept of looking “outside-in” suggests that we must understand the shifting business context affects our work, our careers and our business. Give at least one example. • What are digital natives and how do they look at business differently? • How are values changing in a digitally intermediated world? A Fourth Industrial Revolution ? Schwab, K. (2017), The Fourth Industrial Revolution Introduction
- 8. 8©2017 LHST sarl • Analyze the context of each case to document the key processes of the organization or the market • Qualify the data at hand to understand the nature of the business challenges • Apply the appropriate methodologies in your predictive and prescriptive analyses, and • Integrate elements of visual communications in transforming the data into a call for collective action In this module , you will www.Dsign4change.com Adminstration
- 9. 9 Innovation is a State of Mind ©2018 LHST sarl Introduction Session 1 The Building Blocks Session 2 Data Session 3 Digital Technologies Session 4 Decision Making Session 5 Innovation Session 6 Social Business
- 10. Grading Scale Participation: 50% of your grade will be based upon your participation and engagement in class. Final exam: 50% of your grade will be based upon your results on the final multiple choice exam. • What is the organization’s business model? • Why does the organization focus on data? • Which data science techniques does the organization favor ? • What is the link between data science and decision making? • How is the Data Science team organized? • How does the organization use Data Science to propel growth? Adminstration
- 11. Decision Trees Supervised Categorical It’s sunny, hot, normaly humid, and windy – should I play tennis?
- 12. To help us understand the motivations, experience and objectives of the internal and external clients of the organization ROI Real time data ... Stockholders Competition “made in” “made by” ... The State Peu de barrières d’entrée Acquisitions, OPA... Partners Loyalty Real costs ... Clients The Enterprise Mobility Empowerment ... Employees Introduction
- 14. Introduction
- 15. • Management is all about taking better decisions • What do better decisions mean (faster, more impressive, more precise) ? • Is it observable – how is something more precise answer to a problem? • The challenge is deciding what we want to measure Lewis Mumford, Technics and Civilization Decision Making ©2016 L. SCHLENKER
- 16. • More data has been created in the past two years than in the previous history of the human race • « Strategists still confuse technology with purpose … instead of garnering context and empathy to inform change…” - Brian Solis • We have more and more data – but does this lead to better decisions? What is data? Introduction
- 17. • Scan the context • Qualify the data at hand • Choose the right method • Transform data into action The Business Analytics Institute https://baieurope.com Introduction
- 18. Lee SCHLENKER Results Actions Knowledge Context Data Process Interprets Decisions Measures Obtain Define Require Drive The ladder of initiatives™ Introduction
- 19. • What if the devices “completely go away to be absorbed into the fabric of our lives?” • Smart pills are an example of ambient technologies that integrate into our environments • These Invisibles will create a world in which we don’t see technology or sensors • Can technology become human – reacting to what makes each one of us unique? http://youtu.be/-hhOtjdkU34 ©2014 L. SCHLENKER Introduction
- 20. • Properties - digital experiences put in place to enrich organizational conversations • Platforms – digital technologies that create proximity between those that produce, and those that consume, experience • People – the managerial mindset • Practice - the operational realities of management Schlenker (2015) Introduction
- 21. • What is the organization’s business model? • Why does the organization focus on data? • How is the Data Science team organized? • Which data science techniques does the organization favor ? • What is the link between data science and decision making? • How does the organization use Data Science to propel growth Case Methodology Case Study
- 22. Case Groups Case Study Group 1 Community Management Group 2 Education Group 3 Financial Services Group 4 Health Analytics Group 5 Public Service Group 6 Privacy and Data Protection Group 7 Visual CVs - Employment
- 23. • Carr, N. The World Wide Cage • Anderson L. and Wladawsky-Berger, L. The 4 Things It Takes to Succeed in the Digital Economy • Pine, B. and Gilmore, J. (1999). The Experience Economy. St. Paul, Minn.: HighBridge Co. • Schlenker L., (2017), Digital Economics • Schwab, K. (2017), The Fourth Industrial Revolution Bibliography Next Steps