Over the last decade, more and more data are collected and opened. Governments actively stimulate the opening of data to increase citizen engagement to support policy-making processes. Evidence-based policy-making is the situation whereby decisions made are based on factual data. The common expectation is that releasing data will result in evidence-based decision-making and more trust in government decisions. This study aims to provide insight into how evidence-based policy based on open data can result into uncertainty and even polarize the policy-making process. We analyze a case study in which traffic and road utilization datasets are used and model the decision-making process using the Business Process Model and Notation (BPMN). The BPMN model shows how the government and business organizations can use the data and give different interpretations. Data-driven decision-making might potentially create uncertainty, polarization, and less trust in decisions as stakeholders can give different meanings to the data and arrive at different outcomes. In contrast to the common belief, we found that the more data released, the more discussions happened about what is desired according to the data. The various directions derived from the data can even polarize decisionmaking. In other words, the more data opened, the more people can construct their perception of reality. For further research, we recommend understanding the types and role of data to create an evidence-based approach.@en

Governments have established Open Government Data Portals (OGDP) to open various types of datasets that can be used to increase transparency, accountability, and innovation. OGDP is becoming a strategic program for citizen engagement and empowering users. Nevertheless, many OGDP architectures focus merely on publishing data and do not support the actual data use. Therefore, this paper aims to develop a reference architecture (RA) that takes a broader set of requirements aimed at enabling the use of open data into account. The RA consists of recommended structures and integrations of the end-to-end user interactions and services. In this research, we use the DKAN open data management platform as the basis to design a full suite of cataloguing and visualising the end-to-end user interactions. Five layers are proposed providing functionalities for using data. Whereas most portals are focused on releasing data, our RA is focused on empowering users by providing functionalities for the use of data.@en

Government institutions collect and produce an extraordinary number of datasets to conduct and execute their programs and agendas. Various types of datasets collected by the governments can increase transparency and accountability, improve citizen engagement, and create value-added services for the public. Through the Open Government Data (OGD) initiatives, Non-Government Organisations (NGOs), private agencies, business enablers, data analysts, researchers, civil societies, and other open data stakeholders can take advantage of disclosing the government datasets. Despite its significance, the decision-making process to disclose government datasets is given limited attention and encounters several challenges. Although numerous datasets have been published to the public, many datasets remain undisclosed. Government institutions face several challenges in deciding to open datasets. First, the governments have not systematically analysed datasets to identify the benefits and disadvantages of opening datasets. Decision-makers, policy-makers, civil servants, and administrative officers do not know how to balance the advantages and disadvantages of opening datasets. Second, various stakeholders’ backgrounds may have different objectives and interests to analyse and disclose datasets. Third, the easy understanding of possible disadvantages of opening datasets results in moving away from the potential benefits due to the risk-avoiding culture in government. Therefore, these results in keeping datasets undisclosed. Furthermore, the stakeholders’ involvement in the decision-making process to open data, such as politicians, executive boards, decision-makers, civil servants, data analysts, and societies, all play essential roles and have different objectives for opening and using the datasets. For example, some decision-makers might have the authority to publish or keep the dataset closed. Some public servants might be risk-averse, whereas others might open datasets without considering possible negative consequences. As a result, the decision-making process becomes fuzzy, and the objectives of disclosing data are not reached. The different roles and interests of the heterogonous actors in the internal government organisation might create uncertainty and delay the decision-making process. Although there are guidelines, there are no decision-making tools to help governments decide to open their datasets. On the governments’ side, the potential disadvantages might easily dominate over the advantages. It is much easier for the decision-makers to keep a dataset closed than take the disadvantages of releasing a dataset. The lack of insights and expertise in estimating the potential advantages and disadvantages of opening data can also lead to uncertainty, which might result in avoiding the disclosure of datasets. Therefore, this research aims to develop Decision-making Support for Opening Government Data (DSOD). This DSOD accommodates a systematic approach to decide to open datasets. To achieve the objective of this research, we followed the Design Science Research (DSR) approach. The DSR approach results in developing a prototype of the DSOD as a design artefact and demonstrate it to the stakeholders. @en

Various types of stakeholders are often involved in the process of deciding to open data. However, the influence of multiple–actors on the decision-making process is ill-understood. Stakeholders play different roles and have different interests in opening and analyzing datasets. The objective of this paper is to understand the influence of the stakeholder’s roles and their interests in the decision-making process to open data. The roles-interest grid method is used to determine the stakeholder’s concerns and how they influence the decision-making process to open data. In addition to stakeholder theory, we employ muddling through and bounded rationality theories to create a comprehensive analysis of the decision-making process. Stakeholders are found to be diverse, where some are proponents of opening data, and others are risk-averse and do not favor disclosing data. Stakeholder’s responsible for the actual opening of data are often focused on the risks resulting in a tension between the ambitions of politicians to open data, and the practices of administrators and decision-makers. Understanding the stakeholder’s roles and their tensions can help to ensure better decisions are made. We recommend creating incentives for generating shared objectives.@en

Governments and publicly-funded research institutions increasingly open up data collected and created through research. One way to share and use data obtained through research is through Virtual Research Environments (VREs). Insights obtained through open data use in VREs can subsequently provide input for policy making. However, this process involves many privacy, security and trust issues both for VRE developers and for end-users. We still know very little about what strategies can be used to handle these security, privacy and trust issues. This 1,5 hour interactive workshop aims to discuss and refine strategies for handling privacy, security and trust issues of VREs and their users as developed in the European VRE4EIC project. The workshop will facilitate open discussions making use of the interactive Mentimeter tool to involve all participants in the sharing of practices and in sharing feedback on the strategies.@en

Open government data initiatives are part of the endeavor process of governments to show that they are accountable and transparent organizations. Opening more datasets to external data analytics providers or other government organizations holds the potential to help governments to improve their processes by promoting a better understanding and enhancing the decision-making. Nevertheless, the decision-making to disclose datasets is challenging. Decision-makers often refuse to open their datasets due to several potential risks. In situations like the Dutch Customs, a dataset can contain competitive sensitive data, and multiple parties have to agree to open it. Given this complex situation, in this paper, we test a Bayesian-belief Network method for supporting the decision to open data. Our work contributes to Customs in their efforts to disclose more datasets and helping decision-makers in the process of evaluating data and defining strategies of how to move from closed to open decisions.@en

The public expects government institutions to open their data to enable society to reap the benefits of these data. However, governments are often reluctant to disclose their data due to possible disadvantages. These disadvantages, at the same time, can be circumstances by processing the data before disclosing. Investments are needed to be able to pre-process a dataset. Hence, a trade-off between the benefits and cost of opening data needs to be made. Decisions to disclose are often made based on binary options like “open” or “closed” the data, whereas also parts of a dataset can be opened or only pre-processed data. The objective of this study is to develop a decision tree analysis in open data (DTOD) to estimate the costs and benefits of disclosing data using a DTA approach. Experts’ judgment is used to quantify the pay-offs of possible consequences of the costs and benefits and to estimate the chance of occurrence. The result shows that for non-trivial decisions the DTOD helps, as it allows the creation of decision structures to show alternatives ways of opening data and the benefits and disadvantages of each alternative.@en

Open government data initiatives result in the expectation of having open data available. Nevertheless, some potential risks like sensitivity, privacy, ownership, misinterpretation, and misuse of the data result in the reluctance of governments to open their data. At this moment, there is no comprehensive overview nor a model to understand the mechanisms resulting in risk when opening data. This study is aimed at developing a Bayesian-belief Networks (BbN) model to analyse the causal mechanism resulting in risks when opening data. An explanatory approach based on the four main steps is followed to develop a BbN. The model presents a better understanding of the causal relationship between data and risks and can help governments and other stakeholders in their decision to open data. We use the literature review base to quantify the probability of risk variables to give an illustration in the interrogating process. For the further study, we recommend using expert’s judgment for quantifying the probability of the risk variables in opening data.@en

Governments are releasing their data to the public to accomplish benefits like the creation of transparency, accountability, citizen engagement and to enable business innovation. At the same time, decision-makers are reluctant to open their data due to some potential risks like misuse, sensitivity, ownership, and inaccuracy of the data. The goal of the study presented in this paper is to develop a Fuzzy Multi-Criteria Decision Making (FMCDM) approach to analyze the risks and benefits to determine the decision to open a dataset. FMCDM is chosen due to its capability to measure and weight the relative importance of the criteria. FMCDM need the weighting of criteria as input. For this Fuzzy Analytical Hierarchy Process (FAHP) is utilized by collecting input from experts’ knowledge and expertise. The scores for each criterion are summed up to rank the importance of the alternatives. Four main criteria are used, e.g. data sensitivity and data ownership representing risks criteria, and data availability and data trustworthy as benefits criteria. For each criterion, there were two sub-criteria identified. Four types of decisions to open data can be made: completely open, maintain suppression, provide limited access, and remain closed. A health patient record dataset is used to illustrate the approach. In further research, we recommend to develop automated approaches that take a dataset as an input and can provide an advice.@en

Stakeholders can have different views on the opening of data, and conflicts may arise between them. Several causes of disputes may arise during the decision-making process due to the diverse objectives, interests, and needs among the stakeholders that perceive their desires. Yet, no stakeholder taxonomy exists to guide this decision-making process. Direct and indirect stakeholders include open data providers, software developers, data scientists, privacy experts, decision-makers, users, open data evangelists, software developers, policy-makers and politicians. Using an iterative process, a stakeholders taxonomy was developed by classifying stakeholders based on their varying levels and views on openness. The taxonomy includes unaware, unknowledgeable, resistant, risk-averse, neutral, supportive, expert, champion, and leading roles. Each stakeholder proposes a unique mix of expertise, legitimacy, sense of urgency, perceived possible benefits, and risks. The stakeholder’s taxonomy can help to improve the adoption of the decision-making process to open data.@en

The trend of open data has spread widely in the government nowadays. The motivation to create transparency, accountability, stimulate citizen engagement and business innovation are drivers to open data. Nevertheless, governments are all too often reluctant to open their data as there might be risks like privacy violating and the opening of inaccurate data. The goal of the research presented in this paper is to develop a model for decision-making support for opening data by weighing potential risks and benefits using Bayesian belief networks. The outcomes can be used to mitigate the risks and still gain benefits of opening data by taking actions like the removing privacy-sensitive data from dataset. After the taking of actions the process can start over again and the risks and benefits can be weighed again. The iteration can continue till the resulting dataset can be opened. This research uses health patient stories dataset as an illustration of the iterative process. This shows how the decision-making support can help to open more data by decomposing datasets.@en