Course Support for Analytics in Action

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

2. 2
Analytics is all about making sense
of the data
©2016 LHST sarl
Introduction
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 Visual CVs - Employment
Day 9 Privacy and Data Protection

3. Introduction
©2016 L. SCHLENKER
Agenda
Introduction
The Data Revolution
Time, Space and Organization
The Analytical Method

4. 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

5. Analytics is the use of data, methods, analysis and
technology to to help managers make better decisions.
1-5
Introduction
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

6. • 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
Introduction

7. • Volume, velocity, variety, veracity
and value
• No longer just structured data
• Gathering data about relationships
rather than about people
• Quadratic relationships
• Data is no longer just data
Why do we have so much data? Introduction

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

9. Tranformational “Memory” itself becomes
the product — the "experience"
• The Experience Economy
• Service economy – value comes from services
embedded in the product
• Pine and Gilmore argued that differentiation today
comes from creating “experiences”
• Starbucks, Michelin, Hermès, Apple
• Companies provide “stages”, managers are “actors”,
customers are active “spectators”
The Basics

10. Introduction
• Place - changes in geography, time, physical
resources and budget
• Platform – enriching how information is produced
and consumed
• People – modifying the frame of reference
• Practice - impacting the reality of management
Schlenker (2015)

11. Analytics

12. • Orchestration : map information flows to client needs
• Appropriation : use the Internet in a business context
• Enrichment : use the services to produce value
• Collaboration : work together to solve client problems
• Data : information in relation to context
• Utilities : computer applications that cover
specific business tasks (word processing,
spreadsheets, etc.)
• Services : business models that meet specific
client needs
©2016 LHST sarl
Introduction

13. • Segment the market by
needs…
• Qualify your target
segment
• Develop your products
or services to meet the
need
• Measure the results
Tristan Kromer
The Basics

14. Introduction
• You're given the choice of three
doors: Behind one door is a car;
behind the others, goats.
• You pick a door, say No. 1
• The host opens another door, say
No. 3, which has a goat. He says to
you, "Do you want to pick door
No. 2?"
• Is it to your advantage to switch your
choice?

15. • 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
Case Study Questions
Technology

16. • Davenport, T. and Patil, D.J., (2012) , Data Scientist,
the sexiest job of the 21rst Century, HBR
• Fourquet, M. and Coursin, C. Le Miroir Digital ou la
nouvelle condition humaine numérique
• Grimes, S. (2008). Unstructured data and the 80
percent rule
• Schlenker, L. (2017). Data isn't just Data
Bibliography
Next Steps