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Technology and Innovation - Introduction

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Course support for Technology and Innovation

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Technology and Innovation - Introduction

  1. 1. Digital Technologies and Innovation Introduction April 2019 http://DSign4Methods.com
  2. 2. “For every complex problem, there's a solution that is simple, neat, and wrong." H.L. Mencken Is AI the future of innovation?
  3. 3. • Data is nothing more than answers waiting for you to ask the right question • Behind one door I’ve hidden the key to a great career, and a beer behind the other two • You can choose any door • To lend you a hand, I will show you one bottle of beer. • What are your chances of finding the key? What do we mean by a better decision?
  4. 4. ©2019 L. SCHLENKER Agenda Introduction Administrative Details Artificial Intelligence and Innovation The Building Blocks Introduction
  5. 5. 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
  6. 6. Working to help management take better decisions People-based approach to Data Science Applied Research, Consulting, ExecEd modules, Boot Camps
  7. 7. Course Portal: http://DSign4Methods.com ©2019 Business Analytics Institute The objective of this course is to build the students’ knowledge of the practice of innovation in a variety of industrial settings Introduction
  8. 8. This a place where managers and students of management can discuss and debate best practices 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
  9. 9. 10©2019 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.Dsign4methods.com Adminstration
  10. 10. 11 Innovation is a State of Mind ©2019 LHST sarl Introduction Session 1 The Building Blocks Session 2 Digital Economics Session 3 The Internet of Value Session 4 Decision Making Session 5 Innovation Session 6 Data Ethics
  11. 11. Grading Scale Participation: 50% of your grade will be based upon your innovation project Final exam: 50% of your grade will be based upon your results on the final multiple choice exam. Confirm or infirm the hypothesis that AI is a lever of innovation using an example from your experience. Insure that your analysis includes: • the conflict or the opportunity (why should your audience care about your story?) • the context (what skills, knowledge or experience has permitted this problem/opportunity to arise?) • the roadmap (how does this product, service, idea influence customer experience) • the happy end (how will your audience evaluate your story?) Introduction
  12. 12. ©2019 LHST sarl Introduction
  13. 13. Introduction
  14. 14. • 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 ©2019 L. SCHLENKER
  15. 15. 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
  16. 16. Lee SCHLENKER Results Actions Knowledge Context Data Process Interprets Decisions Measures Obtain Define Require Drive The ladder of initiatives™ Introduction
  17. 17. • Inputs • Prediction • Evaluation • Actions • Outcomes
  18. 18. • Scan the context • Qualify the data at hand • Choose the right method • Transform data into action Introduction
  19. 19. Introduction What is AI? Machine Learning Artificial Intelligence Nature Knowledge Intelligence Vision Self-learning Algorithms Mimic human Behavior Use Scenario Learns from data Solve Complex Problem Aim Sufficient Solution Optimal Solution Evaluation Metrics Accuracy Success
  20. 20. Introduction What does better mean? • Rule-based automation • Produce probabilistic predictions based on the data • Combine prediction with judgement to learn • Generate art and design • Extract and quantify emotional states • Develop mental constructs • Adapt to environmental needs The Machine Intelligence Continuum
  21. 21. Application Technology and Innovation Perception How do define the problem? Prediction What evidence will allow us to act? Evaluation How do we judge what better means? Action How does our decision translate into action? Training How does past data support our analysis? Input What types of data do we need to capture? Feedback How do we correct our errors Explication What have we learned from practice? Challenge : Data : Human Inteligence Machine Learning HI/AI ©2018 BAI
  22. 22. • 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

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