A Conceptual Model of Decision-Making Support for Opening Data

Luthfi, Ahmad; Janssen, Marijn

doi:10.1007/978-3-319-71117-1_7

Cited by 11 publications

(14 citation statements)

References 28 publications

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“…Fig. 1 The policy cycle in opening data [7], 12] Figure 1 presents the iteration process on how the data providers like governments, business organizations, and other non-governmental institutions use the policy cycle to maintain and update the opening data to the public. There are six main steps in cycling the policy to open the dataset as can be seen in detail [7], [12], as follows:…”

Section: Open Government Data In Policy-makingmentioning

confidence: 99%

Open Data for Evidence-based Decision-making: Data-driven Government Resulting in Uncertainty and Polarization

Luthfi¹,

Janssen²

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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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.

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Section: Open Government Data In Policy-makingmentioning

confidence: 99%

Open Data for Evidence-based Decision-making: Data-driven Government Resulting in Uncertainty and Polarization

Luthfi¹,

Janssen²

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…The expectations to gain access to public data have increased extensively over the last few years [1]. The creation of transparency, accountability, citizen engagement, and to enable busi-ness innovation are the main drivers for governments to open more their data [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…Although initiatives to open data can create many benefits, they might also create disadvantages [1,5]. Risks refer to the chance that these disadvantages come true and its impact.…”

Section: Introductionmentioning

confidence: 99%

“…Potential risks include inaccuracy, sensitivity, privacy, inconsistency, and misuse of data. These risks result in the reluctance of governments to open their data [1]. In addition, two other reasons why governments and data providers are tend not to open their data: (1) opening public and/or private data are a comprehensive insight that may also able to meet risks like inappropriate interpretation of the data [4], and (2) a mistake in translating data or misuse of the data can endanger the reputation of data providers [6].…”

Section: Introductionmentioning

confidence: 99%

“…Bayesian-belief Network (BbN) is a method presented in this study because it allows constructing a causality relationship model between risk and effect variables [1,7]. The use of a BbN in the risks analysis can provide explanations how the disadvantages of the opening of data are created.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Causal Explanatory Model of Bayesian-belief Networks for Analysing the Risks of Opening Data

Luthfi

Janssen

Crompvoets

2018

Lecture Notes in Business Information Processing

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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 0understand 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.

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A Fuzzy Multi-criteria Decision Making Approach for Analyzing the Risks and Benefits of Opening Data

Luthfi

Rehena

Janssen

et al. 2018

Lecture Notes in Computer Science

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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.

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A Conceptual Model of Decision-Making Support for Opening Data

Cited by 11 publications

References 28 publications

Open Data for Evidence-based Decision-making: Data-driven Government Resulting in Uncertainty and Polarization

Open Data for Evidence-based Decision-making: Data-driven Government Resulting in Uncertainty and Polarization

A Causal Explanatory Model of Bayesian-belief Networks for Analysing the Risks of Opening Data

A Fuzzy Multi-criteria Decision Making Approach for Analyzing the Risks and Benefits of Opening Data

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