Trying to make sense of the deluge of data, and of decisions based on data.

1. What happens when data start
living their own life?
Daniel Kaplan [dkaplan@fing.org]
Charles Népote [cnepote@fing.org]

2. Data used to be
Data used to be
ad hoc
ad hoc
constructions to
constructions to
fill variables in
fill variables in
programs
programs
Siloed within programs
Inconsistent within the organization
Highly contextual
Maximized
Badly maintained
Regulated with a focus on processes

3. Data used to be
Data used to be
ad hoc
ad hoc
constructions to
constructions to
fill variables in
fill variables in
programs, until…
programs, until…
The digitization of
The digitization of A new world of
A new world of
daily life and of the
daily life and of the innovation and
innovation and
physical world
physical world co-opetition
co-opetition
Natively digital content Open Innovation
User-generated content Highly cooperative and
complex value chains
Traces
Loosely coupled
Captas organizations/projects/services
Web of things A web of APIs and mashups
Location… Cross-channel communications

4. The digitization of
The digitization of A new world of
A new world of
daily life and of the
daily life and of the innovation and
innovation and
physical world
physical world co-opetition
co-opetition
Pivots around
Pivots around
Data take on
Data take on Identity //
Identity
a life of their own
a life of their own Services //
Services
Data
Data
Produced « just in case » Needs data that are…
Open-ended … meaningful & reliable
… consistent
Mixed and mashed
… documented
Circulated, lent, sold … linked
Infinitely re-used … accessible
in highly diverse contexts … cheap
… reusable…

5. Pivots around
Pivots around
Data take on
Data take on Identity //
Identity
a life of their own
a life of their own Services //
Services
Data
Data
1st-order consequences
Needs A deluge New possibilities
infrastructures of data that for [real-time] knowledge
(identity/cloud/ requires management, production,
semantics/ curation, analyses, decisions,
security…) filtering… forecasting…

6. Pivots around
Pivots around
Data take on
Data take on Identity //
Identity
a life of their own
a life of their own Services //
Services
Data
Data
2nd-order consequences
Retaining control over Power shifts, Questioning
algorithmic decisions Big questions « raw » data

7. 3 Areas for Concrete Applications
« Smart » Cities
Personal Data Open Data

8. The Open Data Transformation

9. Open Data?
The Basics
Data accessible on the web
Machine-readable
"Raw"
Non-exclusive, non-discriminatory
licensing agreements
The Advanced Version

10. Driving forces behind Open [Public] Data
Economic Institutional
▋ Liberalization ▋ Lack of money
▋ Growth found in ▋ Achieve more with
service-based less, produce
innovation non-tax revenues
▋ "Information wants ▋ Transparency &
to be free" participation drive
Open
Data
▋ Web of data Complexity
▋
▋ Semantic web ▋ Demand for
▋ Web 2.0 participation &
▋ Web of things empowerment
▋ Open source ▋ Consumerism
▋ Datamining ▋ Low trust in
▋ Dataviz institutions
Technological Societal

11. A few building blocks
Reference
docs Government
Mapping End-user IT Local
data info businesses govts.
Observation "Grey" Other Public
data Data docs businesses Actors services
Production Directory Research Media
data data
Financial
data Citizens NGOs
Transparency,
accountability
Reveal Produce
Improved Efficiency,
Facts information
services productivity
Uses Innovative New
Provide Improve services Outcomes knowledge
Interfaces services
Service Citizen
Create new
coproduction empowerment
services
Democratic
participation
<Special thanks to Tim Davies, Practical Participation>

12. Open Corporate Data

13. "Collaborative"
Open Data

14. Challenges
Standards
(& adoption)
Volume/Diversity
Tools, Platforms
of Open Data
Uses, Ecosystem

15. Who’s Smarter in the Smart City?

16. What do we expect from the « Smart City »?
Efficiency
“Trillions of digital devices, connected through the Internet,
Productivity are producing a vast ocean of data.
Savings
And all this information – from the flow of markets to the
pulse of societies – can be turned into knowledge. (…)
Environment
Competitivity With this knowledge we can reducecosts, cut waste, and
improve the efficiency, productivity and quality of everything
Growth from companies to cities. (…)
Attractiveness
Given all this low-cost technology and networking, what
Quality of life
wouldn’t you enhance ? What wouldn’t you connect ? What
Cultural life information wouldn’t you mine for insight ? What service
wouldn’t you provide for a customer, citizen, student or
Services
patient ? The answer is, we will do all these things. Because
Security we can — and because we must.”
Transparency
IBM
Participation…

17. 1- System? System of Systems? System^^n?

18. 2- Beyond « Service »

19. While "they" try to make the city smart…

20. The « city »?
Large urban Are all
services Smart Cities
operators? Alike?
Everybody?
3- Who becomes smart in the smart city?

21. Risks and dangers
Misunderstandings
Soft authoritarianism
Innovation inhibition
Obsolescence
Less resilience

23. Shared Personal Data:
Revolutionizing customer relationship

24. Personal Data Are the Lifeblood
of Contemporary Marketing

25. They're also its poison

26. There Is Another Way

27. Let's Take Up a New Challenge:
Empowering Consumers by Sharing With Them
All the Personal Data that Businesses Own About
Them
Graphic:
MyDex
"If I Know Something About You, You Know It, Too!"

28. Everyone Stands to Benefit

29. The New Market
for Personal Information Management Services
Collecting, gathering, producing,
Knowing oneself better,
storing, referencing, classifying…
and acting upon it
one's data
Comparing offers, Sharing (or not), checking,
expressing one's needs, updating one's data
group buying…
Managing one's relationship with Analyzing, visualizing, modelizing,
organizations… And with other comparing… one's personal data
consumers

30. This Is Much More Than a Weird Idea
AMEE / Avoco Secure / billmonitor / British Gas /
Callcredit / EDF Energy / E.ON / Garlik / Google / Lloyds
Banking Group / MasterCard / Moneysupermarket.com /
Mydex / npower / RBS / Scottish Power / Scottish
Southern Energy / The UK Cards Association / Three /
Visa / Google…

31. Are You Ready to Turn
Customer Relationship on its Feet?
Experimenting consumer empowerment,
through the sharing and reuse of the personal data
that organizations own about them
http://fing.org/?-MesInfos-les-donnees-personnelles-

32. What happens when data start
living their own life?
Daniel Kaplan [dkaplan@fing.org]
Charles Népote [cnepote@fing.org]