Technology Update : Data Governance

1. TUSSEN DROOM EN DAAD...
staan wetten in de weg en praktische bezwaren | Freek Bomhof
Willem Elsschot – foto Nationaal Archief NL CC BY-SA 3.0

2. ANALYSIS OF SOME BIG DATA
PROJECTS
Leaving out technology – what are the common denominators?

3. Technical barriers
Availability, licences, interoperability,
maintenance
Social barriers
Autonomy, acceptance, knowledge,
attitude, trust, biases, intrusiveness
Legal and ethical barriers
Privacy, security, retention, inclusion,
equality
Economic barriers
Costs, business model,
cannibalisation, capital
CONDITIONS FOR A SUCCESSFUL DATA-
DRIVEN INNOVATION
The question
Target group, needs, desires,
problems, information, behavior
The data
Meaning, availability, quality
The service
Predictive, prescriptive, boundary
conditions, stakeholders, side effects
The stakeholders
Collaboration set-up
Governance
Ownership, control, accountability
Tussen droom en daad...
the innovation
the ecosystem
the hurdles

4. Tussen droom en daad...
Flickr, Josh Rook, CC-BY-NC-ND 2.0
The innovation

5. THE RIGHT QUESTION
Bottom-up or top-down?
Explore possibilities of having data
Solve problems using data
Is there a different way to do this?
Does it address a real need?
Who is your target group anyway?
Tussen droom en daad...
Flickr, Benjamin Reay, CC-BY-NC 2.0

6. THE DATA - AVAILABILITY
What they say
We are not allowed to do so
What will you do with it?
We need to check the quality
We don’t have time for it
What do we get in return?
We don’t know where it is
You don’t know what it means
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Real reasons
You may see unwanted things
We don’t want to lose power
We’re ashamed of the quality
Actually, we don’t have it
We made a mess of the metadata
Your innovation is a threat to us
We have our own data scientist

7. THE DATA - QUALITY
Generic quality attributes
Provenance
Accuracy
Stability
Metadata
Liability / usage rights
Open data quality
5 star model TBL
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Open License; Computer readable; Open Format;
Uniform Resource Identifiers; Linked Data
source: 5stardata.info

8. THE SERVICE
Descriptive, predictive, prescriptive
Fully or semi-automated service
Time-critical or batch
Context aware, personalized
Efficiency, effectiveness
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flickr, Luca Daviddi, cc-by-sa2.0

9. Tussen droom en daad...
Flickr, WELS net, CC-BY-NC 2.0
The ecosystem

10. Tussen droom en daad...
TECHNICAL INNOVATION SYSTEMS
technical
innovation
system
knowledge development
guidance of search
market formation
resource mobilisation
support from
advocacy coalitions
R. Suurs, “Motors of sustainable innovation. Towards a theory
on the dynamics of technological innovation systems“
enterpreneurial activities
knowledge exchange /
diffusion

11. DATA INNOVATION CHANGES SECTORS
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Existing value chains are added a
“data layer” - with its own value
chain
Data flows in a different way than
physical goods or services
Competition and collaboration
have different dynamics
Example: John Deere and
Monsanto became competitors

12. THE MEDIA WORLD ALREADY KNEW
THIS…
It used to be the Broadcasters that
have the power
Then, the Program Councils were
powerful
Then, internet arrived: power at ISPs,
Settop box vendors, TV
manufacturers?
Now, the power is with the Netflix
recommendation engine?
Tussen droom en daad...
source: TNO; Regulation in the converged media-internet-telecom value web (Nooren, 2014)

13. DAMIAN: ECOSYSTEM ANALYSIS
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source: TNO; Regulation in the converged media-internet-telecom value web (Nooren, 2014)
Recommendation service delivery routes

14. WWW.BIGDATAGAME.EU
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15. TO TAKE CARE OF WHEN EXCHANGING DATA:
DATA GOVERNANCE 2.0
Data Governance Ecosystem
Entity A
(Org, persons…)
- Data Processes
- Data quality
- Availability
- Security
- Usability
- Auditability
- Accountability
Entity B
(Org, persons…)
- Data Processes
- Data quality
- Availability.
- Security
- Usability
- Auditability
- Accountability
Which data to share with whom
Interoperability
Technical platform incl.
access control
Business Model
Laws &
Regulation
Ownership &
Liability aspects
Privacy
Economic
sensitivity

16. Tussen droom en daad...
Flickr, Alastair Duncan, CC-BY-ND 2.0
Hurdles

17. TECHNICAL AND ECONOMIC HURDLES
Technical
Data formats and interoperability
Licenses, re-use
Ownership of data
Maintenance
Liability
Findability - linking
Storage, processing
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Economic / financial
Business model
Investing in the initial service
Costs for infrastructure and
maintenance
Market creation

18. SOCIAL AND LEGAL / ETHICAL HURDLES
Social
Autonomy
Technology acceptance
Capability
Knowledge
Attitude
Biases
Transparency and trust
Intrusiveness
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Legal
GDPR
Purpose specification
Re-identification
Data protection
Ethical
Self-determination and autonomy
Equality
Solidarity

19. 1% INSPIRATION AND 99% HARD WORK
The innovation is your motivator
The ecosystem is your environment
The hurdles are your dissatisfiers
Tussen droom en daad...
Source Wikimedia Commons; by Louis Bachrach, Bachrach
Studios, restored by Michel Vuijlsteke