This document provides an introduction to a course on business analytics. It outlines the course objectives to build students' knowledge of applying analytics in various industry settings. It discusses administrative details like the grading structure and course schedule. It also introduces fundamental concepts in data science and analytics, including common techniques. The document describes the case study methodology that will be used, involving analyzing organizations' data-driven business models and decision-making processes.

1. http://DSign4.education
Introduction
February 2018
Analytics in Action

2. Introduction
©2016 L. SCHLENKER
Agenda
Introduction
Administrative Details
The Fundamentals
Case Methodology

3. Course Portal ;
http://DSign4.education
©2017 Business Analytics Institute
Introduction
The objective of this course is to
build the students’ knowledge of the
practice of Business Analytics in a
variety of industrial settings

4. 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,
Professor ESC Pau
Mail : lee@lhstech.com
Skype : leeschlenker
Web : www.leeschlenker.com
Introduction

5. • Management is about taking decision
• Improving decision making through the study
of digital economics, managerial decision
making, machine learning and data
storytelling
• Partnership with SDMIMD to promote
analytics in management education
http://baieurope.com
lee@baieurope.com
@DSign4Analytics
Skype : leeschlenker
©2017 Business Analytics Institute
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 ?
Introduction
Schwab, K. (2017), The Fourth Industrial
Revolution

8. 8©2016 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.dsign4.education
Administrative
Details

9. 9
Analytics is all about making sense
of the data
©2016 LHST sarl
Day 1 Introduction
Day 2 Digital Economics
Day 3 Community Management
Day 4 Education
Day 5 Financial Services
Day 6 Health Analytics
Day 7 Public Service
Day 8 Privacy and Data Protection
Day 9 Visual CVs - Employment
Day 10 Wrap Up and Final Exam
Administrative
Details

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?
Administrative
Details

11. Decision
Trees
 Supervised
 Categorical
It’s sunny, hot,
normaly humid, and
windy – should I play
tennis?

12. • 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

13. Analytics is the use of data, methods, analysis and
technology to help managers make better decisions.
1-13
Fundamentals
psychological models
data
mining
cognitive science
decision theory
information theory
databases
Business
Analytics
neuroscience
statistics
evolutionary
models
control theory
Data science is the study of the generalizable
extraction of knowledge from data

14. • 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?
Data Explosion
Fundamentals

15. • Scan the context
• Qualify the data at hand
• Choose the right method
• Transform data into action
The Business Analytics Institute
https://baieurope.com
Fundamentals

16. Analytics
• Logic and Statistics
• Programming and Database
• Trade knowledge
• Data Storytelling
davidpritchard.org

17. Analytics

18. Data Science Challenges
Data preparation is by far the most
time-consuming part of Data
Science, but case studies rarely
address this
Fundamentals
(1)Data Quality
(2)Feature Extraction
(3)Machine Learning
(4)Data Storytelling
(5)Productizing
Chandan Rajah

19. 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

20. • 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

21. • 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