This document provides an overview of a workshop on digital technologies and innovation. It includes an agenda with sessions on the building blocks of innovation, digital economics, the internet of value, decision making, and data ethics. The document contains questions to prompt discussion on topics like the Fourth Industrial Revolution, how digital natives approach business, and how values are changing in a digitized world. It also includes introductory sections on data types, big data, the importance of context in data, and transforming data into actions.

1. Digital Technologies and Innovation
Digital Economics
April 2019
http://DSign4Methods.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. 3
Innovation is a State of Mind
©2019 LHST sarl
Introduction
Session 1 The Building Blocks
Session 2 Innovation
Session 3 Digital Economics
Session 4 The Internet of Value
Session 5 Decision Making
Session 6 Data Ethics

4. ©2019 L. SCHLENKER
Agenda
Introduction
The Data Revolution
Time, Space and Organization
The Analytical Method
Introduction

5. • 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 ?
Schwab, K. (2017), The Fourth Industrial
Revolution
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. • Inputs
• Predictions
• Evaluation
• Actions
• Outcomes

10. 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

11. 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
Analytics

13. Big Data

14. Analytics

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? Analytics

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

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 BasicsAnalytics

19. Analytics

20. • 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
©2019 L. Schlenker
Analytics

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

22. • 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
Analytics