Challenges and research approaches Data …...2019/06/15 · Data Governance and Smart Cities Challenges and research approaches Prof. Dr. Max von Grafenstein, LL.M. Co-Head of Research

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


GOVERNANCE INNOVATION

Internet Research (focusing on)


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


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?


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?


Data Governance in Smart Cities:

Promises and Challenges (very briefly)


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


… 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)


Spotlight: “Technical-organisational complexity”



§

Gap between normative expectations and factual abilities of stakeholders to meet them


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



An example of

“Smart City Traffic Management”


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?


Types of collected data


Hypothetical scenarios

Research

(esp. for traffic management)

Law enforcement (esp. traffic laws)

Mobile App

“Parking Slot Assistant”


One of the results


One of the resultsThere is no data that is not personal

data


A possible solutionData

Governance Board

TETechnical Infrastructure


Some decisive questions, in particular ...


Who exactly governs the data sharing?



How does the infrastructure look like?



How does the infrastructure look like?

Will we successfully coordinate to get there?


Data Governance Models as a Key to

The Success of Smart Cities


● Legal layer

● Organizational layer

● Technical layer

Data holder Data user Data subject

Data Governance: Three analytical layers


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


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


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

Documents

Challenges and research approaches Data …...2019/06/15 · Data Governance and Smart Cities Challenges and research approaches Prof. Dr. Max von Grafenstein, LL.M. Co-Head of Research