Course support for Analytics in Action

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

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

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

4. • Améliorer la prise de décision
• L’économie digitale, La prise de décision,
L’intelligence artificielle, La communication
visuelle
• Votre partenaire pour transformer la
contrainte de RGPD en opportunité
http://baieurope.com
lee@baieurope.com
@DSign4Analytics
Skype : leeschlenker
©2017 Business Analytics Institute
Introduction

5. 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

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. 7©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

8. 8
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

9. 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

10. Analytics is the use of data, methods, analysis and
technology to help managers make better decisions.
1-10
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

11. • 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

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

13. Data Techniques
(1) data gathering, bringing together all the available data
into a set of instances;
(2) data aggregation/integration, grouping together all the
data from different sources;
(3) data cleaning, detecting erroneous or irrelevant data and
discarding it;
(4) user and session identification; identifying individual
users;
(5) attribute/variable selection, choosing a subset of relevant
attributes from all the available attributes;
(6) data filtering, selecting a subset of representative data to
convert large data sets into smaller data sets; and
(7) (7) data transformation, deriving new attributes from the
already available ones.
Fundamentals

14. Data Préparation
Data preparation is by far the most time-consuming part of Data Science, but case
studies rarely examines this
(1) data gathering, bringing together all the available data
into a set of instances;
(2) data aggregation/integration, grouping together all the
data from different sources;
(3) data cleaning, detecting erroneous or irrelevant data and
discarding it;
(4) user and session identification; identifying individual
users;
(5) attribute/variable selection, choosing a subset of relevant
attributes from all the available attributes;
(6) data filtering, selecting a subset of representative data to
convert large data sets into smaller data sets; and
(7) data transformation, deriving new attributes from the
already available ones.
Fundamentals

15. 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

16. • 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

17. • 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