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Data Governance and Smart CitiesChallenges and research approaches
Prof. Dr. Max von Grafenstein, LL.M.Co-Head of Research Program “Governance of Data-Driven Innovation and Cybersecurity”
ALEXANDER VON HUMBOLDT INSTITUTE FOR INTERNET AND SOCIETY2
Approach
ALEXANDER VON HUMBOLDT INSTITUTE FOR INTERNET AND SOCIETY3
GOVERNANCE INNOVATION
Internet Research (focusing on)
ALEXANDER VON HUMBOLDT INSTITUTE FOR INTERNET AND SOCIETY4
I. The evolving digital society: What are relevant concepts and theoretical approaches?
III. The knowledge dimension: What are emerging patterns of research and knowledge transfer in the digital age?
II. The governance of data-driven innovation and cybersecurity: What are the key factors of change in the relationship between actors, data and infrastructures in the digital society?
INN
OVATION
GOVE
RNAN
CE
Research Programs
ALEXANDER VON HUMBOLDT INSTITUTE FOR INTERNET AND SOCIETY5
Spotlight: “Governance of Data-Driven Innovation”
No mere compliance-perspective, but proactive approach
Regulatory perspective
● How can we unchain the innovative capacities in our data-driven economy and …● …, simultaneously, protect citizens and societies as a whole against the risks of innovation?
ALEXANDER VON HUMBOLDT INSTITUTE FOR INTERNET AND SOCIETY6
Spotlight: “Governance of Data-Driven Innovation”
No mere compliance-perspective, but proactive approach
Regulatory perspective
● How can we unchain the innovative capacities in our data-driven economy and…● … simultaneously, protect citizens and societies against the risks of innovation?
Enterprises’ (“entrepreneurial”) perspective
● How should we organise and cooperate to unchain (or at least, maintain) our innovative capacities…● … and react to external (disruptive) innovation in an optimal way?
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Data Governance in Smart Cities:
Promises and Challenges (very briefly)
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Promises ...
● Better urban planning (“more informed decisions”)
● Higher efficiency, new products, new business models (e.g. more efficient traffic management)
● Opening up data silos is deemed to be beneficial for social welfare, innovation, and competition
ALEXANDER VON HUMBOLDT INSTITUTE FOR INTERNET AND SOCIETY9
… and challenges
● Creation of data protection and security risks (“surveillance”)
● Lock-in effects for Smart Cities (risk to “digital sovereignty”)
● Involved stakeholders have very diverse incentives (not) to cooperate
● Technical-organisational (as well as legal) governance very complex (and opaque)
ALEXANDER VON HUMBOLDT INSTITUTE FOR INTERNET AND SOCIETY10
Spotlight: “Technical-organisational complexity”
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Spotlight: “Technical-organisational complexity”
§
Gap between normative expectations and factual abilities of stakeholders to meet them
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Example of data protection and security by design (GDPR)
Controller
(responsible for Art. 25 and 32 GDPR)
Processor
(responsible only for Art. 32 GDPR)
Manufacturer
(helps processor and controller to be
compliant with GDPR)
Processes data on behalf of
Spotlight: “Technical-organisational complexity”
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An example of
“Smart City Traffic Management”
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Hypothetical use case
Collection of (personal) data to make it freely accessible in favor of innovative traffic management
How can one set this up in a data protection-compliant way?
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Types of collected data
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Hypothetical scenarios
Research
(esp. for traffic management)
Law enforcement (esp. traffic laws)
Mobile App
“Parking Slot Assistant”
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One of the results
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One of the resultsThere is no data that is not personal
data
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A possible solutionData
Governance Board
TETechnical Infrastructure
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Some decisive questions, in particular ...
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Who exactly governs the data sharing?
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Who exactly governs the data sharing?
How does the infrastructure look like?
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Who exactly governs the data sharing?
How does the infrastructure look like?
Will we successfully coordinate to get there?
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Data Governance Models as a Key to
The Success of Smart Cities
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● Legal layer
● Organizational layer
● Technical layer
Data holder Data user Data subject
Data Governance: Three analytical layers
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A Preliminary Taxonomy of Data Governance Models
SINGLE SOURCE DATA CLEARINGHOUSE DATA POOL DECENTRALIZED MODEL
Data holder
Data users Data users
Data holders
Intermediary
Data users
Data holders Data users and holders
Intermediary
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Conclusion: Current research approach (most pressing, in our opinion)
Research goals
● Identification of fitting data governance models for diverse settings and problems● Recognition of values in the design of data governance models in Smart Cities
Research approaches
● Clarification of the ambiguous terminology applied to data governance models● Comparative empirical research on data governance solutions in Smart Cities
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Thank you
Prof. Dr. Max von Grafenstein LL.M.
Alexander von Humboldt Institute for Internet and SocietyBerlin Career College, University of the ArtsEinstein Center Digital Future