The Data Shake: Opportunities and Obstacles for Urban Policy Making

Preface
Contents
Editors and Contributors
	About the Editors
	Contributors
Part IThe Data Shake: Open Questions and Challenges for Policy Making
1 The Data Shake: An Opportunity for Experiment-Driven Policy Making
	1.1 Introduction
	1.2 Evidence-Based Policy Making: New Chances Coming from the Data Shake
		1.2.1 About Evidence-Based Policy Making
		1.2.2 Evidence-Based Policy Making and the Data Shake: The Chance for Learning
	1.3 The Smart Revolution of Data-Driven Policy Making: The Experimental Perspective
		1.3.1 About Policy Experiments and Learning Cycles
		1.3.2 Policy Cycle Model Under Experimental Dimension
		1.3.3 The Time Perspective in the Experimental Dimension of Policy Making
	1.4 Conclusions: Beyond the Evidence-Based Model
	References
2 Data Ownership and Open Data: The Potential for Data-Driven Policy Making
	2.1 Introduction and Context
		2.1.1 From Smart City to Data City
		2.1.2 Exploring the Cities’ Points of View
	2.2 Challenges and Questions Related to (Open) Data Policies
		2.2.1 Data Hygiene in the Organization
		2.2.2 IoT and Open Data
		2.2.3 Centralization vs Decentralization
		2.2.4 Government and the Market
		2.2.5 Open Data Checklist
	2.3 Data and Procurement
		2.3.1 Examples of Model Clauses
	2.4 Discussion and Conclusion
	References
3 Towards a Public Sector Data Culture: Data as an Individual and Communal Resource in Progressing Democracy
	3.1 The Balance of a Data-Driven Democracy
	3.2 The Conflicting Logics of Emerging Public Sector Data Cultures
	3.3 The Project Democracy Data—Lessons on Cultivating Local Data Culture from the Swedish Social Services
		3.3.1 Proposals 1: Promote Holistic Data-Literacy
		3.3.2 Proposals 2: Design Your Data-Driven Services as if Democracy Depended on It (Because It Does)
		3.3.3 Proposals 3: Conceptualize Data as Democratic Artifact
	References
4 Innovation in Data Visualisation for Public Policy Making
	4.1 Introduction: Data Visualisation Between Decision Support and Social Influence
	4.2 Scoping the Experiences of Data Scientists
		4.2.1 Multiple Data Source Management
		4.2.2 Rigorous Data Integration
		4.2.3 Actionable Information Delivery
		4.2.4 Personalised User Experience
	4.3 A Critical Eye on Technology Innovation Trends
	4.4 Conclusions and Way Forward
	References
Part IIThe PoliVisu Project
5 Policy-Related Decision Making in a Smart City Context: The PoliVisu Approach
	5.1 Evidence-Based Policy Making and the Rise of ICT
	5.2 ICT-Enabled Policy Making in a Smart City Context
	5.3 The Unique Characteristics of the PoliVisu Approach
	5.4 Barriers and Limitations to the Full Exploitation of Data Potential in Policy Making
	5.5 Conclusion
	References
6 Turning Data into Actionable Policy Insights
	6.1 Introduction
	6.2 Policy Making Supported by Data
		6.2.1 Policy Design
		6.2.2 Policy Implementation
		6.2.3 Policy Evaluation
	6.3 Policy-Oriented Data Activities
		6.3.1 Differentiating Roles and Competences
		6.3.2 Balancing Flexibility and Usability
		6.3.3 Transforming Iterations into Experimental Drivers
	6.4 Conclusions
	References
7 Data-Related Ecosystems in Policy Making: The PoliVisu Contexts
	7.1 Introduction
	7.2 The PoliVisu Project as a Testbed for Digital Innovation
	7.3 Actors and Roles in Data-Related Policy Making Ecosystems
	7.4 Data-Related Relations
	7.5 Conclusion: Dealing with Complexity in the Era of the Data Shake
	References
8 Making Policies with Data: The Legacy of the PoliVisu Project
	8.1 Data Supported Policy Making Through the Eyes of the PoliVisu Pilots
		8.1.1 Data for Dialogue
		8.1.2 Between Precision and Usability
		8.1.3 Proneness to Iterative Process
		8.1.4 Actors Involved in Data Supported Policy Making
	8.2 Bottlenecks and New Practices Detected in Policy Making
		8.2.1 Bottlenecks
		8.2.2 New Practices and Knowledge
	8.3 Conclusions
		8.3.1 Lessons Learnt from the PoliVisu Project
		8.3.2 Some Recommendations
	References
Acknowledgments