Course support for Analytics in Action

1. Analytics in Action
Digital Economics
February 2019
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. • How do the authors define a "Data
Scientist"?
• What does a data scientist need to know
about data?
• What is meant by a "big data problem"?
• What knowledge and skills would you
associate with a Data Scientist?
• With advances in artificial intelligence,
do companies really need data scientists?
Data Scientist, the sexiest job of the 21rst Century ?
Introduction

6. Inputs
Prediction
Evaluation
Actions
Outcomes

7. • 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?
The Data Explosion
Introduction

8. • 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

9. Introduction

10. Introduction

11. Big Data

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

13. 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

14. 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)

15. Lee SCHLENKER
Results
Actions
Knowledge
Context
Data
Process
Interprets
Decisions
Measures
Obtain
Define
Require
Drive
The ladder of initiatives™
Introduction

16. Analytics

17. • 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

18. • 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

19. • 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

20. • Davenport, T. and Patil, D.J., (2012) , Data Scientist,
the sexiest job of the 21rst Century, HBR
• Davenport, T. and Kirby, J., (2016) , Six Very Clear
Signs That Your Job Is Due To Be Automated , Fast
Company
• Fourquet, M. and Coursin, C. Le Miroir Digital ou la
nouvelle condition humaine numérique
• Agrawal , Gans and Goldfarb, (2018), Prediction
Machines
• Schlenker L., (2018), Digital Economics
Bibliography
Next Steps