Open Data for Evidence-based Decision-making: Data-driven Government Resulting in Uncertainty and Polarization
Open accessAbstract
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 decision-making. 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.
AI snapshot
Written from the paper’s full text.
TL;DR
This paper argues that opening more government data does not necessarily produce a single, uncontested evidence-based policy, but can instead create uncertainty and polarization as stakeholders interpret the same data differently. Using a traffic and road-utilization case modeled in BPMN, it shows that evidence-based decision-making can support opposing policy directions and reduce trust in decisions.
Key findings
- In contrast to the common expectation that open data leads to evidence-based policy and trust, the paper finds that “the more data released, the more discussions happened about what is desired according to the data,” and that various directions derived from data can polarize decision-making.
- The same open data can support opposing evidence-based policy suggestions: pollution data may suggest reducing traffic, while economic data may suggest more traffic for trading, economic growth, and less unemployment, leading to societal polarization.
- The paper states there is “no single ‘best’ or ‘optimal’ outcome” behind evidence-based decision-making; directions depend on political preference and contemporary moods in society.
- Opening more data may result in a decline in trust in decision-making because the data does not show a consistent picture, and polarization can result in greater policy volatility.
Methods
- The study uses a case study of traffic and road-utilization datasets and models the decision-making process using Business Process Model and Notation (BPMN).
- It develops a conceptual policy cycle for opening data with six main steps and a data-driven evidence-based policy environment divided into four quadrants.
- The case illustration involves two institutions—a government data provider and business organizations—each with four working units, and describes how mechanistic evidence is used on the government side and statistical evidence on the business side.
Limitations
- The paper does not explicitly list limitations; it recommends further research using in-depth case studies to understand how data can be used in evidence-based policy-making and the types and role of data.
- Evident from the text: the analysis is based on a single illustrative case study of traffic and road-utilization data, with no empirical sample, data collection, or quantitative validation reported.
- Evident from the text: the BPMN model and four-quadrant framework are conceptual and illustrative rather than tested against multiple cases or real decision outcomes.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 18 cited sources checked · 0 supported · 0 flagged
- Findingreasoning
The various directions derived from the data can even polarize decision-making.
“The various directions derived from the data can even polarize decisionmaking.”
Quote in the paperSubstantial claim- Rests on an assumption: The traffic and road utilization case study is representative or sufficient to draw conclusions about how open-data-based evidence-based policy-making can create uncertainty and polarization.
- Findingown data
In contrast to the common belief, the more data released, the more discussions happened about what is desired according to the data.
“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.”
- Also stated in: § I. INTRODUCTION · plain
“In contrast to these expectations in the literature, we found that more open data released can result in more discussions about what is desired according to the data and can even polarize decision-making.”
Quote in the paperSubstantial claim- Rests on an assumption: The traffic and road utilization case study is representative or sufficient to draw conclusions about how open-data-based evidence-based policy-making can create uncertainty and polarization.
- Also stated in: § I. INTRODUCTION · plain
- Premisecitation
Evidence-based decision-making can reduce uncertainty about which direction to follow, because the decision to release the data is based on objective evidence on which all parties are likely to agree.
“First, it can reduce uncertainty situation about which direction to follow, as the decision to release the data is based on objective evidence on which all parties are likely to agree on [8], [17].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [8]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [17]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
To have objective evidence requires capable skills and expertise from officers in an organization.
“To have objective evidence requires capable skills and expertise from officers in an organization.”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [17]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [30]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [31]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
The more data opened, the more people can construct their perception of reality.
“In other words, the more data opened, the more people can construct their perception of reality.”
- Also stated in: § I. INTRODUCTION · plain
“The more data opened, the more people can construct their perception of reality consequent of opening large data.”
Quote in the paperSubstantial claim- Rests on an assumption: The traffic and road utilization case study is representative or sufficient to draw conclusions about how open-data-based evidence-based policy-making can create uncertainty and polarization.
- Also stated in: § I. INTRODUCTION · plain
- Premisecitation
Evidence constructing takes time.
“Evidenceconstructing takes time”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [17]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [30]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [31]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
These benefit factors can improve the inclusiveness and constructiveness of different points of view between actors while using the open data portal.
“These benefit factors, furthermore, can improve the inclusiveness and constructiveness of different point of views between actors while using the open data portal [3].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [3]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
Evidence-based policy-making can result in new insights on policy issues to the public.
“Second, evidence policy-making has to base on reliable facts, and this can result in new insights on the policy issues to the public [15].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [15]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Classified as probably not a substantial claim. 8
- May combine two claims. 2
Classified as probably not a substantial claim.
Findingown dataThe BPMN model shows how government and business organizations can use the data and give different interpretations.
“The BPMN model shows how the government and business organizations can use the data and give different interpretations.”
Quote in the paperSubstantial claim- Rests on an assumption: The BPMN model accurately captures the relevant decision-making process for open data policy.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Premisenone givenGovernment institutions and other non-governmental organizations often adapt evidence-based decision-making as an approach to minimize uncertainty and to reduce the potential polarization of the decision-making process.
“In summary, government institutions and other nongovernmental organizations often adapt evidence-based decision-making as an approach to minimize the uncertainty and to degrade the potential polarization of the decisionmaking process.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationEvidence-based policy-making is often expected to result in a single, best solution.
“Evidence-based policy-making is often expected to result in a single, best solutions [15], [16].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [15]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [16]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Classified as probably not a substantial claim.
InterpretationreasoningOpen data can be used to suggest different policies.
“Open data can be used to suggest different policies.”
Quote in the paperSubstantial claim- Rests on an assumption: The traffic and road utilization case study is representative or sufficient to draw conclusions about how open-data-based evidence-based policy-making can create uncertainty and polarization.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone givenThe policy cycle shown in Figure 1 might require several interactions to arrive at the best alternatives.
“The policy cycle shown in Figure 1 might require several interactions to arrive at the best alternatives.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
PremisecitationGood evidence requires skilled manpower.
“Good evidence requires skilled manpower”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [17]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [30]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [31]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
PremisecitationSolving quantitative or qualitative research problems requires people who are skilled and academically qualified.
“Solving quantitative or qualitative research problems, for example, requires people who are skilled and academically qualified.”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [17]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [30]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [31]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone givenThe policy process in opening data is never as linear as implied in the model.
“The policy process in opening data is never as linear as implied in the model.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningData-driven decision-making might potentially create uncertainty, polarization, and less trust in decisions because stakeholders can give different meanings to the data and arrive at different outcomes.
“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.”
Quote in the paperSubstantial claim- Rests on an assumption: The traffic and road utilization case study is representative or sufficient to draw conclusions about how open-data-based evidence-based policy-making can create uncertainty and polarization.
- Rests on an assumption: The BPMN model accurately captures the relevant decision-making process for open data policy.
The paper relies on this without support.
The traffic and road utilization case study is representative or sufficient to draw conclusions about how open-data-based evidence-based policy-making can create uncertainty and polarization.
“In this study, we investigate a case study of the traffic and road utility dataset.”
“To present how the evidence-based policy-making can in use open data, a case study of traffic and road datasets is investigated.”
The paper relies on this without support.
The BPMN model accurately captures the relevant decision-making process for open data policy.
“The business process for opening data policy is developed using evidence-based decision-making approach and modeled in Business Process Management Notation (BMPN).”
“The BPMN model shows how the government and business organizations can use the data and give different interpretations.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Section outline does not match the paper's headings
MinorThe introduction places the conceptual model in Section 4 and the conclusion in Section 7, but the paper labels Section IV as the conclusion. The described Sections 5-7 do not appear under those headings.
“Section 4, the conceptual model of opening a data cycle is described.The four quadrants of data-driven are provided in Section 5.In Section 6, a case study is presented showing how open can be used for evidence-based policy and can influence the uncertainty decisions in the case of opening data is illustrated.Finally, the conclusion and further research opportunities are discussed in Section 7.”
“## IV. CONCLUSION”
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- Authors
- Luthfi, A., & Janssen, M.
- Affiliations
- Islamic University of Indonesia (ID)
- Delft University of Technology (NL)
- Corresponding author
- Luthfi, Ahmad
- Venue
- International Journal on Advanced Science, Engineering and Information Technology
- Publisher
- Insight Society
- Published
- 2019-06-30
- Volume
- 9
- Issue
- 3
- Pages
- 1071-1078
- ISSN
- 2088-5334 (ISSN-L)
- 2460-6952
- OpenAlex
- W2953736122
- Cited by
- 12
- References
- 32
- Citations by year
- 2026: 1 · 2025: 1 · 2023: 3 · 2022: 3 · 2021: 1 · 2020: 3
- FWCI
- 7.32
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-sa
- Locations
- International Journal on Advanced Science Engineering and Information Technology (publishedVersion, cc-by-sa)
- Research Repository (Delft University of Technology) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Topics
- Social Sciences › Political Science and International Relations › E-Government and Public Services
- Physical Sciences › Engineering › Media Technology › Smart Cities and Technologies
- Social Sciences › Public Administration › Public Policy and Administration Research
- Keywords
- open data, evidence-based policy making, data-driven decision making, policy uncertainty, political polarization, public trust in government, citizen participation, traffic data
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