Course support for the module "Digital Technologies and Innovation"

1. Digital Technologies and Innovation
Digital Economics
April 2018
http://DSign4Change.com

2. • 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?

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

4. 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

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. • “The truth is, 9 out of 10 startups
fail.”
• Behind every statistic is an
opinion:
• What to measure and how
to collect the data
• How to interpret, visualize,
and present the results
• Where to distribute the
results and amplify the
reach
• How to finance the
analysis….
We are what we measure (2017)
Carine Carmy
Introduction

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?
What is data?
Introduction

8. • From an objective point of view, information
refers to date in context that conveys
meaning to an individual.
• From a subjective point of view, we could
suggest that it’s the individual’s perspective of
the data that implies meaning.
• Given these definitions what meaning do
Wikileaks, Facebook or Whatapp have?
Assagne, The Conversation
Introduction

9. Categorical (nominal) Data
Data placed in categories according to
a specified characteristic
Categories bear no quantitative
relationship to one another
Examples:
- customer’s location (America,
Europe, Asia)
- employee classification (manager,
supervisor,
associate)
Ordinal Data
Data that is ranked or ordered according to
some relationship with one another
No fixed units of measurement
Examples:
- football rankings
- survey responses
(poor, average, good, very good, excellent)
Ratio Data
Continuous values and have a natural
zero point
Ratios are meaningful
Examples:
- monthly sales
- delivery times
Interval Data
Ordinal data but with constant differences
between observations
No true zero point
Ratios are not meaningful
Examples:
- temperature readings
- SAT scores
Introduction

10. Introduction

11. • Structured data refers to data that can be easily represented in
textual/numeric form and stored in a database.
• Structured data is often logically organized around a data model or
data object.
• Such models permit companies to compare and aggregate data in
databases, datamarts and data warehouses.
Introduction

12. • Data is considered « non-structured » if we
can’t predefine its attributes and store it in
a table or data base
• Examples of this kind of data include press
clippings, videoclips, and songs
• In reality, this data isn’t « non-structured » -
its just that its attributes involve
« complex » relationships
http://jean.marie.gouarne.online.fr/bi.html
Structures

13. Big Data

14. Big Data

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

16. • 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

17. • 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

18. 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

19. 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)

20. Analytics

21. • 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

22. • 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

23. • 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
• Grimes, S. (2008). Unstructured data and the 80
percent rule
• Schlenker, L. (2017). Data isn't just Data
Bibliography
Next Steps