Government Data Research Articles

How to engineer sustainability: from resource-wise manufacturing towards ecosystem-wide impacts

Purpose This conceptual paper aims to present a perspective on how to engineer sustainability through the prism of Industry 4.0 technologies and outline propositions to guide future research. Design/methodology/approach This study presents a literature review developing four research propositions, focusing on the nine leading technologies underpinning Industry 4.0 to engineer economic, environmental and social sustainability dimensions. Findings The authors derive benefits and challenges of Industry 4.0 technologies across all three business model elements: value creation, value delivery and value capture. The authors derive those for the economic, environmental and social dimensions of sustainability. Thereupon, we develop several propositions for future research. Practical implications The authors provide suggestions to practice how to better achieve value in all three sustainability dimensions through implementing a business model perspective, ecosystem thinking, societal demands and Data Governance and AI integration. Social implications By linking societal aspects of Industry 4.0 technologies with environmental, and economic aspects, the authors provide several suggestions how to implement Industry 4.0. For instance, policymakers are recommended to support entire ecosystems than isolated solutions. Originality/value The paper contributes to extant literature by conceptualising how Industry 4.0 can leverage value in reaching sustainability in all three dimensions and produce broader ecosystems-wide impacts.

Unlocking the transformative potential of data science in improving maternal, newborn and child health in Africa: a scoping review protocol

IntroductionApplication of data science in maternal, newborn, and child health (MNCH) across Africa is variable with limited documentation. Despite efforts to reduce preventable MNCH morbidity and mortality, progress remains slow....

Modern Data Governance Technologies and Their Role in Regulatory Compliance: A Study on GDPR and HIPAA

Data governance has evolved into cornerstone of regulatory compliance in the data-driven organizations of today. Strict data privacy regulations as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) as well as the explosive increase in volume and complexity of data make effective data governance today vitally important. These guidelines, which follow notions of openness, responsibility, and data minimization, mandate that businesses provide data security, integrity, and access. Modern tools including Apache Atlas, AWS Lake Formation, Databricks Unity Catalog, and Alation are emerging as critical solutions allowing businesses to automate metadata management, enforce access limits, trace data history, and create auditable logs. In this paper, these tools are evaluated in relation to scalability, usability, compliance, and interoperability. Although these technologies substantially simplify regulatory compliance, they are not without limits including complexity, integration problems, and cost constraints. This article tackles future developments that may change data governance, the benefits and disadvantages of this, and emphasizes in a nutshell that these tools are to be used to assure compliance. Keywords Data Governance, GDPR Compliance, HIPAA Compliance, Metadata Management, Data Lineage, Access Control, Data Privacy, Regulatory Compliance, Blockchain in Data Governance, AI in Data Governance, Privacy-Preserving Technologies, Multi-Cloud Governance, Data Security, Audit Logging, Role-Based Access Control.

Data Cataloging as a Catalyst for Effective Data Governance and Improved Decision-Making

Data governance is a foundational component for organizations managing data as a strategic asset in today’s data- driven environments. Effective data governance ensures data quality, security, accessibility, and regulatory compliance, all of which are crucial for maintaining organizational integrity and competitive advantage. This paper explores how data cataloging, as a core tool within data governance frameworks, facilitates metadata management, data lineage tracking, and role-based access control (RBAC). We analyze the technical mechanisms through which data cataloging contributes to governance objectives, such as maintaining data quality and enabling regulatory compliance. Additionally, this study examines the impact of data cataloging on decision-making within organizations, highlighting improvements in decision accuracy, speed, and accountability. By drawing from examples in sectors such as finance and healthcare, this paper outlines the challenges associated with data cataloging implementation, including integration complexity and scalability issues. The paper concludes with future directions, focusing on artificial intelligence (AI)- enabled metadata tagging and cloud-native cataloging solutions, which promise to enhance data cataloging capabilities and make governance frameworks more agile and scalable. Keywords Data Governance, Data Cataloging, Metadata Management, Data Quality, Data Lineage, Role-Based Access Control (RBAC), Compliance, AI-Driven Cataloging, Cloud-Native Solutions, Data-Driven Decision-Making.

Development of a Data Portal System for Governance in Berau

The development of a Data Portal System for Berau Governance addresses the need for an integrated platform to effectively manage and disseminate government data. This system aims to enhance transparency, accountability, and public access to diverse datasets in alignment with the One Data Indonesia initiative. The portal consolidates various data categories, including news, announcements, agendas, publications, downloads, infographics, and visualizations, into a single platform, streamlining data access for both government agencies and the public. The system was developed using the Rapid Application Development (RAD) methodology, enabling iterative prototyping and quick adjustments based on feedback from key stakeholders. This approach ensured the system could meet the dynamic requirements of the government while remaining adaptable to future needs. The Laravel framework was chosen for its flexibility, scalability, and security features, allowing the system to provide a responsive, user-friendly experience while maintaining high levels of data integrity.The Data Portal System significantly improves the local government’s ability to manage and share information, fostering greater civic engagement and enhancing public trust in governance processes. Furthermore, the system facilitates more efficient public service delivery by making key data accessible in a structured and intuitive format. Regular assessments and updates are recommended to ensure the system continues to align with evolving user needs and governmental policies, further advancing data governance in Berau Regency.

Quantifying and Contextualizing Violent Collective Action Event Datasets

AbstractHow does newspaper-based event data compare to a government data source? While scholars have long recognized the importance of and biases present in newspaper-based event data, few studies have compared newspaper reports with official government data to better understand the severity and impact of such biases. We develop this comparison in the context of riots, a form of violent collective action that represents an important middle ground between peaceful protests and protracted civil conflict. Using newly collected police precinct-level government data from India, we compare these data to a high-quality newspaper source. Though similar at the aggregate level, newspaper riot reports correlate poorly with government data at the local level. We model the frequency of newspaper and government riot reports based on literacy, location, and other demographic characteristics to better understand the discrepancies between these two sources. We conclude that newspaper riot data does partially reflect aggregate riot trends, but the newspaper editorial process also plays an important role. Government data is better for within country comparisons and for analyzing event trends over time. Our findings suggest that using collective action event data from both sources may help ensure that results are not driven by biases in either data source.

Evaluation Methods, Indicators, and Outcomes in Learning Health Systems: Protocol for a Jurisdictional Scan.

In learning health systems (LHSs), real-time evidence, informatics, patient-provider partnerships and experiences, and organizational culture are combined to conduct "learning cycles" that support improvements in care. Although the concept of LHSs is fairly well established in the literature, evaluation methods, mechanisms, and indicators are less consistently described. Furthermore, LHSs often use "usual care" or "status quo" as a benchmark for comparing new approaches to care, but disentangling usual care from multifarious care modalities found across settings is challenging. There is a need to identify which evaluation methods are used within LHSs, describe how LHS growth and maturity are conceptualized, and determine what tools and measures are being used to evaluate LHSs at the system level. This study aimed to (1) identify international examples of LHSs and describe their evaluation approaches, frameworks, indicators, and outcomes; and (2) describe common characteristics, emphases, assumptions, or challenges in establishing counterfactuals in LHSs. A jurisdictional scan, which is a method used to explore, understand, and assess how problems have been framed by others in a given field, will be conducted according to modified PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. LHSs will be identified through a search of peer-reviewed and gray literature using Ovid MEDLINE, EBSCO CINAHL, Ovid Embase, Clarivate Web of Science, PubMed non-MEDLINE databases, and the web. We will describe evaluation approaches used both at the LHS learning cycle and system levels. To gain a comprehensive understanding of each LHS, including details specific to evaluation, self-identified LHSs will be included if they are described according to at least 4 of 11 prespecified criteria (core functionalities, analytics, use of evidence, co-design or implementation, evaluation, change management or governance structures, data sharing, knowledge sharing, training or capacity building, equity, and sustainability). Search results will be screened, extracted, and analyzed to inform a descriptive review pertaining to our main objectives. Evaluation methods and approaches, both within learning cycles and at the system level, as well as frameworks, indicators, and target outcomes, will be identified and summarized descriptively. Across evaluations, common challenges, assumptions, contextual factors, and mechanisms will be described. As of October 2024, the database searches described above yielded 3503 citations after duplicate removal. Full-text screening of 117 articles is complete, and 49 articles are under analysis. Results are expected in early 2025. This research will characterize the current landscape of LHS evaluation approaches and provide a foundation for developing consistent and scalable metrics of LHS growth, maturity, and success. This work will also serve to identify opportunities for improving the alignment of current evaluation approaches and metrics with population health needs, community priorities, equity, and health system strategic aims. Open Science Framework b5u7e; https://osf.io/b5u7e. DERR1-10.2196/57929.

Architecture Design of Distributed Encrypted Storage and Computing Based on Fog Computing

With the progress of informatization, the problems of data transmission delay, untimely response and theft and tampering of user privacy data are becoming increasingly prominent. This paper proposes a distributed encryption storage and computing architecture based on fog computing, which uses the scalability and low latency of fog computing to solve the problems of data silos and response delays. Blockchain technology is introduced to build decentralized storage to prevent data tampering and ensure secure transmission and sharing. Hierarchical privacy protection measures are designed to store data in a hierarchical and classified manner to accurately control the scope of information acquisition. This method has good application characteristics and can effectively solve the problems of government data collection and calculation.

Critical Provocations for Synthetic Data

Training artificial intelligence (AI) systems requires vast quantities of data, and AI developers face a variety of barriers to accessing the information they need. Synthetic data has captured researchers’ and industry’s imagination as a potential solution to this problem. While some of the enthusiasm for synthetic data may be warranted, in this short paper we offer critical counterweight to simplistic narratives that position synthetic data as a cost-free solution to every data-access challenge—provocations highlighting ethical, political, and governance issues the use of synthetic data can create. We question the idea that synthetic data, by its nature, is exempt from privacy and related ethical concerns. We caution that framing synthetic data in binary opposition to “real” measurement data could subtly shift the normative standards to which data collectors and processors are held. And we argue that by promising to divorce data from its constituents—the people it represents and impacts—synthetic data could create new obstacles to democratic data governance.

Overview of AI regulation in healthcare: A comparative study of the EU and South Africa

This article provides a comparative analysis of the regulatory landscapes governing artificial intelligence (AI) in healthcare in the European Union (EU) and South Africa (SA). It critically examines the approaches, frameworks and mechanisms each jurisdiction employs to balance innovation with ethical considerations, patient safety, data privacy and accountability. The EU’s proactive stance, embodied by the AI Act, offers a structured and risk-based categorisation for AI applications, emphasising stringent guidelines for risk management, data governance and human oversight. In contrast, SA’s regulatory environment is characterised by its infancy and lack of specificity, with existing legislation such as the National Health Act and the Medicines and Related Substances Act providing a foundational but limited framework for addressing the unique challenges posed by AI in healthcare. The article delves into the dynamic nature of AI technologies, highlighting the need for continuous risk assessment, the importance of transparent and responsible data governance and the critical role of human oversight in ensuring patient safety and autonomy. It discusses the obligation of clear liability frameworks to address potential malfunctions and security breaches in AI applications. Through this comparative lens, the manuscript identifies regulatory gaps and proposes that the South African Law Reform Commission (SALRC) should play a predominant role in developing draft legislation for AI prior to the evolution of challenges related to these technologies.

Building Trust: Data Governance and Security in Edge-Cloud Integration

Building Trust: Data Governance and Security in Edge-Cloud Integration

Blockchain empowered dynamic access control for secure data sharing in collaborative emergency management

In the realm of emergency management involving multi-party collaboration, securely sharing data among multiple entities poses a significant challenge. Traditional centralized data platforms struggle with fine-grained access control for data from different sources and are susceptible to single-point failures and risks of forged authorization. This paper delves into the prospective application of blockchain technology for decentralized data governance. We propose a dynamic access control technology for multi-party collaborative emergency response, which combines blockchain and attribute-based access control technologies to enable dynamic access control in multi-party data sharing scenarios. A data sharing system for collaborative emergency management built with Hyperledger Fabric has been developed and subjected to a series of evaluation tests. The experimental results reveal that our blockchain-based system not only significantly enhances data security and privacy by leveraging decentralized control but also improves the efficiency and reliability of data sharing in emergency situations. Specifically, our findings demonstrate the system's ability to dynamically adapt access control policies based on changing emergency scenarios, effectively mitigating risks associated with single-point failures and unauthorized access. These outcomes underscore the viability and reliability of our approach, providing a robust framework for secure, scalable, and flexible data sharing in the demanding field of emergency management.

Ethical Implications of AI-Driven Decision-Making in Cloud-Based Services

This paper explores the ethical implications of AI-driven decision-making in cloud-based services, highlighting key concerns such as algorithmic bias, data privacy, accountability, and transparency. As AI systems become increasingly embedded in cloud infrastructures, they raise significant ethical challenges that must be addressed to ensure responsible and fair deployment. The paper examines emerging trends, including explainable AI (XAI), automated fairness tools, and the importance of data governance, which offer solutions to mitigate these challenges. However, it also identifies ongoing issues such as bias in AI models, inadequate data privacy protection, cybersecurity vulnerabilities, and the lack of a consistent global regulatory framework. A survey of industry stakeholders reveals that data privacy and algorithmic bias are perceived as the most pressing challenges. The paper also discusses strategies to overcome these issues, such as fostering collaboration across stakeholders, investing in continuous monitoring, and developing global ethical standards for AI systems. Ultimately, the paper calls for a multi-faceted approach combining innovation, regulation, and accountability to ensure that AI-driven decision-making in cloud-based services is ethical, transparent, and beneficial for all stakeholders.

Unmasking inequalities of the code: Disentangling the nexus of AI and inequality

This article provides an interdisciplinary exploration of the complex dynamics between artificial intelligence (AI) and inequality, drawing upon social sciences and technology studies. It scrutinises the power dynamics that shape the development, deployment, and utilisation of AI technologies, and how these dynamics influence access to and control over AI resources. To do so, we employ Margaret Archer's social realism framework to illuminate the ways in which AI systems can reinforce various forms of inequalities. This theoretical perspective underscores the dynamic interplay between social context, individual agency, and the processes of morphostasis and morphogenesis, offering a nuanced understanding of how inequalities are reproduced and potentially transformed within the AI context. We further discuss the challenges posed by the access and opportunity divide, privacy and surveillance concerns, and the digital divide in the context of AI. We propose co-ownership as a potential solution to economic inequalities induced by AI, suggesting that stakeholders contributing to AI development should have significant claims of ownership. We also advocate for the recognition of AI systems as legal entities, which could provide a mechanism for accountability and compensation in cases of privacy breaches. Finally, we conclude by emphasising the need for robust data governance frameworks, global governance, and a commitment to social justice in navigating the complex landscape of AI and inequality.

Mapping the landscape of government data breaches: A bibliometric analysis of literature from 2006 to 2023

Data breaches in the government sector are severe threats to cybersecurity and illegal acts that flagrantly violate security and privacy laws. It undermines public trust in government data management. Unlike breaches in other sectors, these incidents can have far-reaching consequences and the potential to cause significant damage to governance and national security. This field has experienced rapid growth in recent years, which means an increase in urgency to address this challenge. This study conducted a bibliometric analysis to evaluate the scientific landscape of government data breaches. It analysed 107 articles published from 2006 to 2023 from the Scopus database. The key findings highlighted trends in scientific production, the most cited articles, country contributions, co-occurrence networks, thematic evolution, and factorial analysis. Ransomware has become one of the biggest challenges in cybersecurity, especially for governments. The use of increasingly sophisticated zero-day attack techniques makes it difficult for law enforcement to track the digital footprints of perpetrators. The regulations on data protection remain inadequate and require improvement. The existing legal framework is not strong enough to reduce data breaches in cyberspace. Therefore, to deal with cyber threats, such as ransomware, cyber risk management is essential and using cloud computing raises new challenges and opportunities in cybersecurity. These findings contribute to future research and practice as guidance for policymakers, system developers, researchers, and practitioners in improving cybersecurity protocols and shaping government regulations and policies.

Government data opening and corporate cash holdings: Evidence from a quasi-natural experiment in China

We investigate whether government data opening reduces corporate cash holdings. Using the establishment of city-level open government data platforms in China as a quasi-natural experiment, we find that government data opening significantly reduces firms’ cash holdings. The effect is more pronounced when firm uncertainty is higher, when firms are more financially constrained, when firms are non-state-owned, when firms trade less in the internal market, and when there are fewer alternative information sources in a region. Overall, our evidence suggests that government data opening reduces environmental uncertainty and firms’ precautionary cash holdings.

Open Government Data (OGD) as a catalyst for smart city development: Empirical evidence from Chinese cities

While existing smart city models recognize the importance of data, they often overlook the specific role of Open Government Data (OGD) for urban development. This study addresses this gap by adapting the Smart City Model to explicitly include OGD as a critical component. Drawing on panel data from the 2022–2024 Chinese Cities Digitalization Evolution Index, we employ Structural Equation Modeling (SEM) to empirically examine the direct and indirect effects of OGD, digital infrastructure, and digital economy on smart city development. Our analysis identifies four key pathways, revealing that while digital infrastructure positively influences smart city development directly, the indirect pathways incorporating OGD demonstrate stronger effects. OGD plays a pivotal role by significantly enhancing the digital economy and digital infrastructure, as well as directly contributing to smart city development. This research contributes to the smart city literature by moving beyond discussions of individual components to empirically test the relationships between these elements. By positioning OGD as a catalyst, we provide a nuanced understanding of the mechanisms through which data-driven initiatives empower smart city development. Our findings offer valuable insights into the multifaceted ways OGD serves as a driving force for urban innovation, challenging the traditional view of government data as a passive resource. This study highlights the importance of OGD as a strategic asset for policymakers seeking to harness the potential of data-driven urban governance. We conclude with policy recommendations for leveraging OGD to support sustainable and efficient smart city development.

Can Big Data Aggregation Help Businesses Save Energy and Reduce Emissions? Quasi-Natural Experiment in Big Data Comprehensive Test

Aggregating big data pieces is critical for increasing enterprise resource allocation efficiency, reducing energy usage, and lowering carbon emissions intensity. This research aims to investigate the impact of big data aggregation on energy efficiency and carbon emission intensity among Chinese enterprises. To this end, it employs primary financial data from Chinese listed companies from 2009 to 2021 and carbon emissions data disclosed in social responsibility reports, sustainable development reports, and environmental reports. The findings revealed that the aggregation of big data elements dramatically reduces the intensity of carbon emissions in firms in the pilot regions. The decrease effect is more effective in economically developed places and regions with higher degrees of digitization, particularly for organizations in high-energy-consuming industries, and it is more robust for small and non-state-owned businesses. The aggregation of big data elements mainly aids firms in pilot regions in lowering energy consumption and emissions by increasing technical innovation and energy usage efficiency. To create a new national competitive advantage, we should actively promote the gradual expansion of the comprehensive pilot zone for big data, advance the in-depth application of big data in environmental governance, and better capitalize on the dividends of big data aggregation.

Can the CPTPP’s Digital Format on Data Flows Serve a Role Model for the WTO?

Digital trade has grown at an unprecedented pace due to advancements in information technologies. The global COVID-19 pandemic further highlighted the importance of international digital transactions and created momentum for the negotiation of digital rules at the World Trade Organization (WTO). Large technology companies rely on free access to data and cross-border data flows, making them influential in shaping digital rule-making at bilateral, regional, and multilateral levels. The Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) is currently the leading regional trade agreement (RTA) on digital trade, which aligns with the market-based approach. Australia, Singapore, and Japan, the main parties to the CPTPP, now play a key role in WTO digital negotiations. Their proposals, largely based on the CPTPP’s context, aim to liberalize data flows. However, many WTO members remain cautious of this approach, considering it may compromise regulatory autonomy and essential common interests, such as data sovereignty, privacy, and consumer protection.Though the CPTPP’s e-commerce chapter provides a comprehensive benchmark, fundamental problems with establishing cross-border digital rules remain unsolved at the WTO. This article will examine the limits of, and obstacles to, the realization of the CPTPP approach within the trade system, and propose a preliminary and progressive arrangement for global data governance.

Accelerating Vertical Federated Learning

Privacy, security and data governance constraints rule out a brute force process in the integration of cross-silo data, which inherits the development of the Internet of Things. Federated learning is proposed to ensure that all parties can collaboratively complete the training task while the data is not out of the local. Vertical federated learning is a specialization of federated learning for distributed features. To preserve privacy, homomorphic encryption is applied to enable encrypted operations without decryption. Nevertheless, together with a robust security guarantee, homomorphic encryption brings extra communication and computation overhead. In this paper, we analyze the current bottlenecks of vertical federated learning under homomorphic encryption comprehensively and numerically. We propose a straggler-resilient and computation-efficient accelerating system that reduces the communication overhead in heterogeneous scenarios by 65.26% at most and reduces the computation overhead caused by homomorphic encryption by 40.66% at most. Our system can improve the robustness and efficiency of the current vertical federated learning framework without loss of security.