Data Ownership and Open Data: The Potential for Data-Driven Policy Making

As part of the rhetoric surrounding the Smart City concept, cities are increasingly facing challenges related to data (management, governance, processing, storage, publishing etc.). The growing power acquired by the data market and the great relevance assigned to data ownership rather than to data-exploitation knowhow is affecting the development of a data culture and is slowing down the embedding of data-related expertise inside public administrations. Concurrently, policies call for more open data to foster service innovation and government transparency. What are the consequences of these phenomena when imagining the potential for policy making consequent to the growing data quantity and availability? Which strategic challenges and decisions do public authorities face in this regard? What are valuable approaches to arm public administrations in this “war on data”? The Smart Flanders program was initiated by the Flemish Government (Belgium) in 2017 to research and support cities with deﬁning and implementing a common open data policy. As part of the program, a “maturity check” was performed, evaluating the cities on several quantitative and qualitative parameters. This exercise laid to bare some challenges in the ﬁeld of open data and led to a checklist that cities can employ to begin tackling them, as well as a set of model clauses to be used in the procurement of new technologies.


From Smart City to Data City
For over a decade, city governments have been exploring what it can mean to be a "smart city". Considering the centrality of the contemporary urban concept occurring in various discourses on urbanism -from media studies, urban studies, geography, to architecture, and elsewhere -the ongoing role and application of associated ICT within future urbanisation seems inevitable. Turning the promises of the Smart City into practice, however, remains a challenge for cities today. Most agree that technology has some role to play in supporting or implementing policy, but how that role should be filled remains unclear and is often the result of trial and error. It is clear that Smart cities are partly digital, becoming places where information technology is combined with infrastructure, architecture, everyday objects, and even our bodies to address social, economic, and environmental problems (Townsend 2013). The rationale being that, to create a 'better' city, it should be turned into an 'intelligent machine' to both understand and manage complexities of urban life. Connectivity is thus a core feature, as are huge amounts of data collected, generated, and analysed.
The Smart City concept has also been criticized, inter alia for its selfcongratulatory tendency, the commercial interests at play, as well as its push of ICT and the potential consequences towards reinforcing a digital divide (Graham 2002;Hollands 2008). Handing over too much control over the public domain to private companies raises concerns regarding democracy and the commodification of the public space (Greenfield 2013;Peck and Tickell 2002;Townsend 2013). Both are a far cry from what would be labelled as "smart". The concept remains fuzzy, meaning different things to different people, from concerns about freedom/privacy to enthusiasm about efficiency, sustainability, economic growth, participation and generally a better world through technology (cf. Komninos and Mora 2018;Mattern 2017).
At the same time, city governments are exploring how the concept can actually contribute to their daily practices and which role technology can play in providing better or "smarter" services to citizens. Even the staunchest critics of the Smart City concept agree that data increasingly has a role to play in policy making (Hollands 2008). Some scholars, therefore, speak of Data Cities rather than Smart Cities (Powell 2014). While this of course has always been the case to greater or lesser extent, the sheer amount of data that is becoming available today, as well as the combination of data from different sources and domains, can provide new types of tools and insights to policy makers. This can be data that comes from Internet of Things solutions (e.g. sensors in public parking garages), structured information in internal reporting systems, detailed data on the public domain (e.g. from satellite imaging) and so on.
In order to fully unlock the potential of this data however, it needs to be more easily available and accessible than today. This is where open data comes in. The idea is that governments currently own (but do not use) a wealth of information related to divergent aspects of life in the city, but that this data is neither publicly available, nor easily interpretable. This has sparked a movement to encourage the opening of datasets in a structured and machine-readable way, under the "open data" moniker, which has gained significant traction across local and national governments. The Open Knowledge Foundation is one of the strong proponents of open data and has come up with what has become the generally accepted definition of open data: "Open means anyone can freely access, use, modify and share for any purpose (subject, at most, to requirements that preserve provenance and openness)" (OKFN 2015). This means that open data can be used for any goal at no cost, with the only (potential) exceptions being that reusers mention the source of the data or do not in any way prevent the data from being shared further on.
The idea here is clear: public organizations open up all kinds of data related to their operations, with the goal of having external developers create new services and applications ("apps") based on this data. In principle, this can mean a cost reduction for the public organizations that open data, as they do not need to build and maintain their own services and apps, an activity that is generally accepted as being highly cost intensive (Walravens 2015).
In practice however, a number of challenges remain and "merely" opening up data has not always proven equally successful (see e.g. Peled 2011;Lee et al. 2014). Opening up data already entails significant challenges to governments and public organizations before any data "leaves" the organization (e.g. setting up internal processes to safeguard internal data hygiene and quality control or implementing new or updating existing database systems). Relevant data can also be distributed over different government organizations or levels of governance, and some data applicable to the public may be under the control of private players that are less inclined to open it. After data are made available, the role of government is not necessarily played out. Ensuring that data is actually reused, and relevant applications are built, should also be considered a concern for these public organizations and open data policy makers.
In order to tackle some of these challenges, the Smart Flanders program 1 was initiated by the Flemish Government (Belgium) in early 2017. Smart Flanders is coordinated by IMEC, the largest non-profit technology research institute in Belgium, by an interdisciplinary team of researchers from communication sciences, organizational science, and computer science. The goal of the 3-year program is to support the thirteen so-called center cities in Flanders (by and large the biggest cities) and a representation of the Flemish Community in the Brussels Region (referred to as the 13 + 1), with defining and implementing a common open data policy. The program is followed up by a steering group consisting of representatives of the cities, the cabinets of the Flemish ministers for Urban Policy and for Innovation, the Flemish agencies responsible for Interior Policy and Information, the Knowledge Centre Flemish Cities, the Organization of Flemish Cities and Towns, and IMEC.
To achieve the goal of defining and implementing a joint open data policy, these cities needed to find common ground and collaborate in ways and on themes that were quite new to them. This paper will present some of the most significant challenges at play when it comes to open data in a city context today. It will summarize these points of attention in an Open Data Checklist that cities may reuse to assess their "open data readiness".

Exploring the Cities' Points of View
In order to establish a state of the art around the topic of data/smart cities, a thorough, written, open questions survey was conducted with the cities. This survey asked the participating cities how they looked at the Smart City concept, whether and how they currently organize around it, how they spend resources on Smart City projects and how they think about technology and data. The survey also aimed to document whether any smart city policies were already in place and what these may entail.
This initial written survey was then complemented by a round of in-depth expert interviews with representatives of the 13 + 1 cities. These semi-structured interviews allowed us more insight into the motivations, concerns and challenges raised by trying to establish a smart city strategy. Fourteen interview sessions were held between April and October of 2017, with multiple representatives of the cities present. The profiles that participated in the interviews range from politicians, civil servants responsible for data management, ICT, geographical information, local economy, mobility and so on. Representatives from the following cities were interviewed: Aalst; Antwerp; Bruges; Genk; Ghent; Hasselt; Kortrijk; Leuven; Mechelen; Ostend; Roeselare; Sint-Niklaas; Turnhout and the Flemish Community Commission in Brussels. The interviews lasted between two and four hours and were transcribed for analysis. The data gathered in 2017 (Van Compernolle et al. 2018) is currently being updated during a new round of interviews taking place in Summer 2019. Where possible, we will complement the analysis with this new material. Later publications will focus on these new results and the evolutions we can derive from them over a two-year period.
Based on the insights coming from both this quantitative and qualitative data, a number of critical aspects were identified that cities can actively work on, with the goal of making a smart city and open data strategy more concrete. It became clear that many general challenges remain when it comes to implementing sound open data policies. These challenges came to the foreground during the Smart Flanders steering group meetings and were shared via the website 2 to generate wider debate (in Dutch). The following section will present and discuss these challenges.

Challenges and Questions Related to (Open) Data Policies
The data shake is affecting the policy making domain by giving rise to different challenges in the wider landscape of data policy. Key ones are: • Data "hygiene": In some cases, digitization still is a significant challenge, but how can we generate awareness to the level of key individual public servants that work with data? How do we change working with data into an operational process that leads to good open data?
• IoT and open data: In the hype surrounding the Smart City concept a lot is made of the data generated by sensors and other IoT devices, but how do we publish data from these sensors in a proper way, dealing with the real-time aspect, the sheer volume of the data, archiving of data and so on? • Centralization vs decentralization: As a principle, open data lends itself quite well to decentralized publishing and the technical solutions are available, but how do we turn these into processes that work? This requires agreement on the roles of different levels of government. • Government and the market: where does the role of government end? When do private actors come into play? This is particularly relevant in the field of open data as well.

Data Hygiene in the Organization
The first challenges for most of the cities that were interviewed still relate to the digitization of internal processes and services towards citizens. This also entails having processes and procedures in place when it comes to working with data in the organization. It may seem counterintuitive but open data can actually offer significant short-term efficiency gains in this regard. By reusing data from other organizations or departments within the city, public workers can avoid wasting time looking for the most recent or complete information. This does however require that everyone in the organization that needs to work with data is aware of the importance of doing this in a structured, traceable, and repeatable way. That also means a data management plan at the level of the whole organization becomes an important tool to manage these processes. Very often, this is not or only partially present in the interviewed cities. It is however recognized as being of key importance and is under development in almost all cases. Keeping data hygiene within the organization under control and at a high level is a first long-term challenge and requirement to implement a sustainable open data policy. Cities also recognize that interoperability will increasingly be of great importance in this context. Making clear agreements on the ownership, use and publishing of data will only grow in importance, but it requires an investment on the part of the organization to ensure sufficient technical expertise and to make the right decisions in this complex area. Interoperability and the concept that data and applications can be seen as separate from each other should prevent data becoming "locked up" in applications provided by third-party vendors. Avoiding so-called vendor lock-in means that the relationship between a local government and its service suppliers can evolve from a typical client-supplier relationship into a partnership in which data are easier to move from one system to another when this is needed or desirable.

IoT and Open Data
The Internet of Things (IoT) is often mentioned in one breath with Smart City services and can mean an extra complicating factor when viewed from the perspective of the data these systems generate. The concept links to the idea that we can understand reality better by measuring as much as possible and by equipping the public space with all kinds of sensors that collect different types of data, policy can be informed by more evidence than ever before. Policy could be tailored to what is observed in the public space, even in real-time.
However, the idea of data-driven policy making comes with a number of complexities on different levels. Divergent actors need to collaborate in new ways and in new fields. One real life example from Flanders is using ANPR cameras to enforce a low emission zone in a city in which certain types of polluting vehicles are not allowed or need to pay a fine when they enter the zone. The sensors in this case are the smart cameras that can detect license plates and determine whether a car can enter the low emission zone or if a fine needs to be sent. To enable this, an elaborate collaboration between different actors needed to be realized, as data needs to be shared between different government organizations, police databases, companies deploying the infrastructure (the cameras in this case) and related software platforms, citizens who need to be informed about which types of cars can enter in the zone during which period and so on.
Next to the often-complex forms of collaboration or partnership between diverse actors, processing all the data generated by IoT solutions is another significant challenge. Clearly, when more sensors are deployed in the city, the amount of data these systems generate increases dramatically. All this data needs to be processed, a task often given to the third-party vendor supplying the solution, but what remains often unclear today is if and how the collected data should be archived. Historical analyses can yield very interesting insights to inform public policy or even allow for predictive analytics, but how long should these large datasets be stored? After which time period should data be erased, especially if personal information is included? Who is responsible for storing and providing access to the data? Who pays for these services? It is important to consider these questions when procuring IoT solutions from third parties and including these arrangements in contracts and agreements. Very often, this is not the case today.
Finally, and to the core of this contribution, a significant challenge related to IoT data is how to publish this data for reuse in a sustainable and cost-effective way. In the spirit of open data, providing potential reusers with real-time information coming from IoT solutions has the potential to generate all kinds of innovative services and applications (e.g. in the domains of mobility, air quality crowdedness and so on). This means however, that infrastructure needs to be made available to allow for a swift processing, publishing, and archiving of said data. Some solutions are available today, but they are often tied to a single vendor or solution. Furthermore, with the speed at which more IoT data is becoming available, this challenge will quickly become more prevalent and need to be addressed sooner rather than later.

Centralization vs Decentralization
Another pertinent challenge or question in the field of open data relates to the way data are published and which actor takes up which role. The question should be framed in a broader debate on centralizing data versus decentralizing them. What remains crucial is that data are easy to find and use for potential reusers. The success of any open data policy will depend on this. Hence, it is important that a local government communicates about the data it makes available, but also that the data can be easily found by anyone looking for it (e.g. also from abroad). When data is published in a decentralized way, for example on the website of the municipality, it is important to describe the data according to standardized principles. By applying standards (like DCAT for example) to describe data, information about that data can automatically be picked up by regional, national and international open data portals, making them easily retrievable by anyone looking to reuse them (including commercial data portals such as Google Dataset Search for example).
Publishing data in a completely decentralized way is technically possible but entails a number of organizational challenges. Clear agreements need to be made about the standards used, the ways in which they are applied and the processes that need to be put in place to ensure data is published in the proper way, for example on a municipality's website. This requires a significant investment by local governments and since open data is rarely a priority, this remains a challenge. Additionally, the resources and skills required are not always present, particularly in smaller organizations. For them, a more centralized approach will prove far more sustainable.
The question then becomes who should take up the role of supporting smaller local governments with this challenge. In Belgium, because of its complex and federated structure, the regional Flemish government, provincial government, or intercommunal organizations could take up this role. Larger cities could take up some of the investment to support the smaller municipalities in their region. And new forms of collaboration between local governments are also coming to the foreground in different regions (e.g. around Brussels). Today, none of these actors are clearly positioned to take up such a role, but it is becoming increasingly clear (and urgent) that more collaboration in this area is needed to set up more sustainable data (sharing) policies.
The first question related to the core competences of government is then; who does what and who has a clear mandate to enforce certain policies if necessary? Today, this situation is fragmented and unclear in Flanders and by extent, Belgium. A broad governance of the Flemish public data landscape should be developed and formalized as soon as possible in order to avoid further fragmentation and an inefficient use of public resources.

Government and the Market
Next to the question of which level of government should take up which role, a second important question related to the core competences of government can be identified: which tasks should be for government and which should be taken up by private players? This is a political decision and choice for the most part and hence will evolve depending on dominant views at the time. As such, it is something of a moving target. This however does not mean this question should not be in the back of the minds of policy makers, as a choice for "more" or "less" government can have consequences for the quality of service provision to citizens.
A key challenge in this area of balancing public and private interest in the context of open data relates to stimulating reuse of open data: should it be a task of the government to ensure that data are actually reused? Most cities agree the local government has a role to play here, by (1) serving as the authentic source for published opened up data (2) ensuring data can be easily found and the threshold for reuse is kept as low as possible and (3) that local government engages in a dialogue with potential reusers so that the data that are published are relevant and of value for reuse. Since data are also made available for commercial reuse, it is not possible to exclude companies from this dialogue. A challenge then becomes how to avoid giving any company a competitive advantage (e.g. by giving them insight into available data or a roadmap for publishing certain datasets). Transparency on both the process and result of a dialogue are crucial here.
Another challenge is the relationship between government and third-party vendors: what are the options as a public organization in enforcing certain behaviour from its suppliers? A number of basic demands can be included in the contracts between the two, are e.g. penalty clauses also foreseen? What is the recourse when the systems of two vendors turn out not to be compatible even though this was ensured during the contracting phase and both suppliers point to each other? Often, local governments do not have the resources to engage in complicated lawsuits. There is no simple answer to these challenges, but the dialogue and transparent approach referred to in the previous paragraph can be part of the answer. Additionally, traditional procurement could be abandoned in some cases where innovative procurement allows for more flexibility on the part of the procuring organization.
A public organization is expected to serve the public interest. When working with and on data, this role becomes even more important, but also far more complex. More than ever, local governments should inform themselves on good practices in this field and clearly position themselves towards third-party vendors that promise the single solution to all of their challenges. By starting from a stronger base of information as well as some shared principles, local governments can evolve away from a traditional client-supplier relationship towards a partnership with market players. When it comes to open data, the role of government here is to strive for a maximal and broad reuse of data, through a transparent process and dialogue.

Open Data Checklist
The survey and interviews with the 13 cities have led to a number of insights related to publishing open data, some of which were outlined in the previous section. To make these insights accessible for reuse by other (local) governments, they are presented as a checklist in what follows. Government organizations that are exploring open data initiatives can use this checklist to ensure to cover some of the most significant challenges related to publishing open data in a sustainable way. The checklist consists of 6 main categories: • Problem (re)definition • Capacity and resources • Organizational culture • Governance • Partnerships • Risks In Table 2.1, we will very briefly list points of attention in each of these categories.

Data and Procurement
As hinted at a few times throughout this text, a key tool (local) governments have in this complex context is procurement and the relationship with technology suppliers. During the Smart Flanders programme, it was found that too few, unclear or very different provisions concerning data are included in contracts and agreements with suppliers. In view of the increasing importance of data in the urban context, however, it is extremely important for local authorities to pay attention to agreements concerning data that may be published as open data when awarding public contracts and concessions and when renegotiating existing agreements.
In order to meet this need, a document with model clauses has been drawn up in the context of Smart Flanders. Local authorities can use this when renegotiating existing concessions and public contracts or defining data sharing provisions for new public contracts or concessions with contractors and other third parties. These model clauses are based on the principles of the Open Data Charter, which was also drawn   Problem (re)definition "Open washing" This risk refers to a situation in which public organizations claim to open up, but only do so to comply with regulations. This is not a sustainable situation and waste of resources. Starting from a concrete case or project can avoid this up during the Smart Flanders programme. The Open Data Charter contains twenty general principles that together form the ambition of the 13 centre cities of Flanders. The Charter was also adopted by the government of Flanders and is available online.
The purpose of the model clauses is, among other things, to give the city organisation direct access to data and to regulate the responsibilities with regard to the publication of these data for re-use. In addition, a more uniform approach to data in tendering is provided. The main target audience of the document is local contracting authorities, but it can also be used by other contracting public authorities. The document has been conceived as a sort of guide, first briefly explaining what (open) data and linked open data are, and why it is important to consider them during procurement. It also follows the structure of a typical specification document, referring to selection criteria, award criteria and technical criteria. This distinction is of course critical when drawing up procurement specifications and authorities can decide to what extent they want to use the model clauses in one of these categories, depending on the solution that is being procured.

Examples of Model Clauses
By way of example and with the goal of inspiring others to take up similar initiatives in their localities, some of the model clauses are included below. It should be noted however that these are merely translated from Dutch, in accordance with existing legislation applicable within Flanders, and should be checked for conformity to local applicable law.
The contracting authority starts with the delineation of open data: "To make public and private information services possible, (static and dynamic) open data are essential, and this in all areas of policy making. The contracting authority therefore endorses the principle that all datasets, data and content that anyone is free to use, adapt and share for any purpose are referred to as open data, with the exception of those data and datasets of which the confidentiality is protected by law or may logically be expected, such as personal data, data compromising public order and security (hereinafter "Open Data")".
Subsequently, the contracting authority must indicate which data must be collected and consequently possibly published as open data: "The contracting authority entrusts the contractor with the collection of the following data (hereinafter the "Collected Data"): -[to be completed by the contracting authority]; The "Collected Data" that the contracting authority expects to be collected by the contractor (and of which the contracting authority becomes the owner) should be described and listed in as much detail as possible. After all, the contracting authority should only have the data that is relevant to it, be collected by the contractor.
In order to ensure that the data are eligible for re-use, it is important that clear agreements are made about the ownership of the data: "The procuring authority owns the Collected Data. The Contracting Authority has the right to copy, distribute, present, reproduce, publish and reuse the Collected Data. The Contracting Authority must have immediate access to and be able to make full use of the raw Data collected by the Contractor, both during and after the term of the Contract. This also applies to historical data. The Contractor may still use the Collected Data itself for the purposes for which it deems it necessary." Finally, for the purpose of this paper, data quality can be an important factor as well. The model clauses give the contracting authority the possibility to impose quality requirements on the contractors with regard to the Collected Data.
"At the request of the contracting authority, the contractor shall make available to the contracting authority a provisional version of the datasets, as well as the URLs referring to the opened datasets; If the result of the inspection shows that there are defects in the way in which the Collected Data were published, the contracting authority can opt to have these defects rectified by the Contractor, if it considers this to be appropriate. If the contractor fails to take remedial measures, the contracting authority may take an ex officio measure at the contractor's expense and risk. In addition, the contracting authority may include special penalties in the contract documents, which may be imposed if the contractor fails to take remedial measures.
Again, these examples only serve as an inspiration and should be adapted to the local context. The guide continues with a set of technical model clauses on how access to the data should be organised, how data can be published in a decentralised way, how sustainability of the published data should be organised, which metadata and other standards can be used and so on. The full document (in Dutch) is available with the authors by simple request or on smart.flanders.be.

Discussion and Conclusion
In its most basic operationalization, the idea of opening up government data where possible, holds a great deal of potential. It can give citizens more insight into how and why certain decisions are made at the political level. It can also stimulate innovation, with new services, apps, efficiency gains, jobs, and economic activity as a result. Lastly, it can lead to more and better interaction between citizens and governments and so on.
Yet, it is questionable whether it actually does, or what the conditions should be for this to take place. This is made explicit in the political premise of the Smart City concept, in how politicians frame their view on Open Data: the concept is quite popular across the political spectrum, as it can be employed in very different rhetoric; as an argument for a smaller government (not building services and applications, but making sure data are available so that others can do so) or one for more government effort (e.g. in relation to transparency, engagement with citizens, active participation, development of data-related software solutions, standardization activities and so on).
Whatever viewpoint taken, a "Smart City" should include, at the very least, access to data. However, as reuse of open data does not 'just happen' and requires interaction, stimulation or incentives in some cases, the question becomes at which point the role of public officials is played out in this realm (Walravens et al. 2018). It is clear that the government body providing open data has a role to play, but to which extent? In what forms should it make data easily available, but also understandable or interpretable for citizens? For which types of data or in which domains? How can open data be privacy-compliant? Herein lies the potential for a-perhaps counterintuitivedemocratic deficit of open data: even if data are available in a Smart City context, it does not mean they are "usable, useful or used" (Open Knowledge International 2019).
One part of the answer seems to lie in avoiding a purely top-down or bottomup approach (Shepard and Simeti 2013), but rather aiming to bring together the relevant parties from the quadruple helix (government, companies, research and citizens) as mentioned above. Engaging the quadruple helix, and particularly citizensvia truly participatory and inclusive means, in complex urban challenges with technical components like (open) data -remains a massive challenge. Such an approach requires sufficient time and means to facilitate discussion, properly defining urban challenges, getting the roles of all involved stakeholders clear and setting up a stepby-step approach to act. Only through such an approach can a more sustainable open data policy be developed, that further enables a Smart City.
Opening data remains something of a chicken-and-egg problem: sufficient investment is needed on the side of the government in order to publish significant amounts or relevant data, but reusers will only generate innovative applications and services once enough data are available.
The research presented in this paper shows that cities certainly see the potential value of open data, but a number of challenges remain. In order to develop sustainable open data policies, a number of conditions have to be met. These have been summarized as points of attention presented in an Open Data Checklist. Additionally, procurement is a key process in government which unfortunately does not always sufficiently take provisions on data into account. This contribution illustrated how using the same model clauses related to data, open data and linked open data can create benefits for both contracting government organisations, as well as technology suppliers. Taking factors related to problematization, organizational culture, governance, partnerships and a number of risks into account, as well as optimising procurement strategies can help local governments make more informed decisions when designing or developing an open data policy for their constituency.  (2014). After his studies, for several years he worked as a lawyer advising young technology companies then becoming a researcher at imec-SMIT. He currently works on several projects related to (open) data, data driven governance and smart cities, including the VLAIO projects 'Smart Retail Dashboard' and the VLAIO COOCK project 'Open City' Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.