Data Governance Model Research Articles

Towards a Data Governance Model for Enhanced Data Quality Management: A study of Public Sector Organizations in Guyana

Public sector organisations in Guyana recognise the need to handle and manage sensitive data properly. Proper data governance is the solution. Identifying the factors that would influence the design of data governance models helps to ensure the creation of a solid model and data quality management. This study accumulated factors from the literature and presented them to IT professionals in Guyana. The Contingency Model served as a base for the identified factors to work together to create a company-specific model for data quality management. Thematic analysis was employed to analyse nine (9) interview transcriptions from medium to high-level personnel across different public sector organisations in Guyana. The factors identified were refined to the Guyanese context. Culture emerged as an indigenous factor in this study. This research resulted in an emergent model that can now be used to design data governance models for public sector organisations in Guyana

Data governance challenges on smart home involving multiple research sites

Abstract Background The development of Ambient Assisted Living (AAL) technology has great potential for ubiquitous health monitoring through collaboration between multiple research sites, but its use poses significant challenges to data governance. This study aims to investigate data governance challenges arising when smart home data flows across different research sites. Methods A four-analytic-dimension method was used to identify potential data-related issues that may occur when heterogeneous smart home data moves around multiple research sites. These dimensions include data domain, stakeholders, value, and governance goal. Results Data domain has difficulty in categorizing new data types generated by analytical models without a specified agreement, and determining the regulations for varying levels of anonymization and privacy protection is unclear in the AAL research field. Stakeholders in the AAL project may hold have different norms and practices for data use. Although some common values may be shared, such as improving the quality of homecare, conflicts may still arise and need to be regulated by data governance. Shifting AAL data between stakeholders may reduce its value, as different stakeholders may have varying interests. For instance, removing identifiable information from data to preserve privacy may reduce its value for researchers exploring individuals’ activity patterns. Governance goals are objectives for maintaining and facilitating data. While some governance goals can be widely acknowledged and shared, such as ensuring data security and privacy, conflicts may still exist over others, such as data access. Conclusions The big data generated by smart home technology highlights the need to develop proper data governance models to regulate data. This study’s implications include a better understanding of potential data-related challenges when data flows around multiple research sites, helping policymakers set up suitable data governance models. Key messages • AAL tech for health monitoring poses data governance challenges due to varying stakeholder norms and values. • Proper data governance models need to be developed for AAL data as it remains challenges in data domain, stakeholders, value, and governance goal.

The inclusive data governance models for algorithms – a dream of the already convinced or a realistic way of action?

ABSTRACT The article presents the implications of personal data governance models for algorithmic discrimination. It reflects on the significance of engaging with data subjects to prevent or mitigate discrimination by comparing the effectiveness of the top-down and bottom-up approaches to data governance. The paper goes beyond the option to either decline or agree to terms of service while entering online platforms and argues that bottom-up data governance models of data trusts and information fiduciaries can be more inclusive than the top-down data governance.

Pilot Federated Analysis Projects

The Cambrian explosion of Trusted Research Environments (TREs) will inevitably lead to increased data siloing and potentially decrease achievable data science. Federated access to data is a mitigation against this risk. The presentation will cover two major infrastructure demonstrator projects to address this need and start the UK journey towards interconnected TRE’s. TELEPORT was an infrastructure to connect TREs and allow all the data assets to be seen through a single pane of glass while leaving them in situ; the analysis can be performed on each data asset, as well as combining data from multiple TREs to derive knowledge. The analysis is performed within the construct of a pop-up TRE, which is a temporary project-specific TRE. The data governance model is operated in an as-is state, with all egress controls being applied upon extraction of research output. This method has been termed Federated Data. TREFX is a set of infrastructure based around international GA4GH standards and the RO-CRATE concept to provide a platform to send complex workflows to the TRE’s to be performed upon the data assets, with the output post egress checking to be re-packed into the inbound RO-CRATE prior to being returned to the researcher. This method addresses both federated analysis and method reproducibility. This project was extended to cover ADDI workloads for dementia. Both solutions were jointly developed by Swansea University / SeRP and share common elements. Future work will see both these products being combined into a hybrid approach as well as spin-out products and services.

Implementing Data Governance with Multi-Modal Privacy-Preserving Record Linkages between Restricted and Public Open Enclaves

Data centralization is increasingly non-viable in the changing landscape of data privacy and data governance. New models of data sharing are needed, in particular when privacy-preserving record linkages (PPRL) are being used to connect data within restricted data enclaves and public open enclaves. We share a privacy-preserving data-sharing infrastructure that employs a linkage honest broker implementation of PPRL connecting structured electronic health record data between the National Center for Advancing Translational Science (NCATS) National Clinical Cohort Collaborative (N3C), a restricted data enclave, with the National Cancer Institute (NCI) Cancer Imaging Archive (TCIA), a public open enclave. In particular we will address: Data governance models needed when linking data from restricted vs. public enclaves Specific privacy-preserving consideration when linking to imaging data which contain specific structured data and imaging considerations Privacy-preserving data aggregation when combining multi-modal data from restricted data enclaves with publicly discoverable imaging data

Content, controls, audience: contextualizing de-identification as a novel approach to enabling cross-sectoral data linkages

BackgroundCanadian privacy laws often distinguish personal health information (PHI) from non-health data. A recent legislative designation permits the Institute for Clinical Evaluative Sciences (ICES) cross-sectoral data linkages and analyses of health and non-health data but requires a discrete environment for each data type and only one environment to conduct research and analytics. It further stipulates that only de-identified, non-health data may enter the PHI environment where research and analytics are conducted, an added complication since data linkages necessarily require some identifiability. In order to consolidate its health and non-health data, then, ICES requires novel approaches to data governance and de-identification. ObjectiveTo establish a comprehensive data governance framework that optimizes cross-sectoral data linkages and classifies identifiability as existing on a spectrum dependent on content, controls and audience. ApproachOperating under legislative and regulatory definitions of de-identification that contemplate what is “reasonably foreseeable in the circumstances,” ICES is working with its privacy regulator to create a data governance framework and de-identification guidelines for its health and non-health data. ResultsWhile still awaiting regulatory approval, ICES will be a first-of-its-kind data integration unit whose data governance model is formally approved by its privacy regulator and an industry leader in novel approaches to de-identification and cross-sectoral data linkages. ConclusionsIdentifiability is determined by context, and data may be simultaneously identifiable and de-identified, depending on its contextual factors. ImplicationsICES’ approach to data governance and de-identification can offer important insights to other research and analytics-based organizations facing legislative barriers to cross-sectoral data linkages.

Application of Data Governance Models in the Context of the Digital Economy

In the context of the digital economy, the volume of data is growing exponentially, the types of data are becoming more diverse, and its value is increasing, often providing critical support for decision-making by enterprises and government institutions. Effective data governance is a crucial tool for maximizing data value and mitigating data risks. This article examines the application of data governance models in the digital economy, aiming to offer technical insights and guidance for data-driven enterprises and governments in China. By elevating their data governance standards in the new era, this approach will comprehensively enhance their ability to harness digital value and ensure security in the digital economy, ultimately driving the continued growth of both the digital economy and society.

A Māori data governance assessment of the NZ COVID Tracer app

PurposeDuring the COVID-19 pandemic, the NZ COVID Tracer App (NZCTA) was released as a digital intervention to support contact tracing processes in Aotearoa New Zealand. This paper examines whether NZCTA met the data governance requirements of the Indigenous Māori people. Māori are an interesting case study as they have unique Treaty and data sovereignty rights, and a higher risk of COVID-related mortality.MethodsThe NZCTA was assessed against 24 criteria drawn from the Māori Data Governance Model. The assessment drew on documentary sources and the authors’ knowledge of NZCTA and contact tracing process. Each criteria was assessed as ‘met’, ‘partially met’ or ‘not met’.ResultsOur retrospective assessment showed a mixed performance against the Māori Data Governance Model, with NZCTA only fulfilling seven of the 24 model criteria and failing to meet nine.ConclusionThere is significant room for improvement in future digital health interventions for Māori. Much work remains to be done in the Aotearoa public sector to uphold Māori data sovereignty and address systemic barriers to genuine partnership with Māori.

More is better? The role of strategic data management in a lean manufacturing process

Purpose Lean manufacturing has been pivotal in emphasizing the reduction of cycle times, minimizing manufacturing costs and diminishing inventories. This research endeavors to formulate a lean data management paradigm, through the design and execution of a strategic edge-cloud data governance approach. This study aims to discern anomalous or unforeseen patterns within data sets, enabling an efficacious examination of product shortcomings within manufacturing processes, while concurrently minimizing the redundancy associated with the storage, access and processing of nonvalue-adding data. Design/methodology/approach Adopting a lean data management framework within both edge and cloud computing contexts, this study ensures the preservation of significant time series sequences, while ascertaining the optimal quantity of normal time series data to retain. The efficacy of detected anomalous patterns, both at the edge and in the cloud, is assessed. A comparative analysis between traditional data management practices and the strategic edge-cloud data governance approach facilitates an exploration into the equilibrium between anomaly detection and space conservation in cloud environments for aggregated data analysis. Findings Evaluation of the proposed framework through a real-world inspection case study revealed its capability to navigate alternative strategies for harmonizing anomaly detection with data storage efficiency in cloud-based analysis. Contrary to the conventional belief that retaining comprehensive data in the cloud maximizes anomaly detection rates, our findings suggest that a strategic edge-cloud data governance model, which retains a specific subset of normal data, can achieve comparable or superior accuracy with less normal data relative to traditional methods. This approach further demonstrates enhanced space efficiency and mitigates various forms of waste, including temporal delays, storage of noncontributory normal data, costs associated with the analysis of such data and excess data transmission. Originality/value By treating inspected normal data as nonvalue-added, this study probes the intricacies of maintaining an optimal balance of such data in light of anomaly detection performance from aggregated data sets. Our proposed methodology augments existing research by integrating a strategic edge-cloud data governance model within a lean data analytical framework, thereby ensuring the retention of adequate data for effective anomaly detection.

Presenting the data governance model in the country's tax affairs organization, a study in the smart tax system

Presenting the data governance model in the country's tax affairs organization, a study in the smart tax system

Preferences on Governance Models for Mental Health Data: Qualitative Study With Young People.

Improving access to mental health data to accelerate research and improve mental health outcomes is a potentially achievable goal given the substantial data that can now be collected from mobile devices. Smartphones can provide a useful mechanism for collecting mental health data from young people, especially as their use is relatively ubiquitous in high-resource settings such as the United Kingdom and they have a high capacity to collect active and passive data. This raises the interesting opportunity to establish a large bank of mental health data from young people that could be accessed by researchers worldwide, but it is important to clarify how to ensure that this is done in an appropriate manner aligned with the values of young people. In this study, we discussed the preferences of young people in the United Kingdom regarding the governance, sharing, and use of their mental health data with the establishment of a global data bank in mind. We aimed to determine whether young people want and feel safe to share their mental health data; if so, with whom; and their preferences in doing so. Young people (N=46) were provided with 2 modules of educational material about data governance models and background in scientific research. We then conducted 2-hour web-based group sessions using a deliberative democracy methodology to reach a consensus where possible. Findings were analyzed using the framework method. Young people were generally enthusiastic about contributing data to mental health research. They believed that broader availability of mental health data could be used to discover what improves or worsens mental health and develop new services to support young people. However, this enthusiasm came with many concerns and caveats, including distributed control of access to ensure appropriate use, distributed power, and data management that included diverse representation and sufficient ethical training for applicants and data managers. Although it is feasible to use smartphones to collect mental health data from young people in the United Kingdom, it is essential to carefully consider the parameters of such a data bank. Addressing and embedding young people's preferences, including the need for robust procedures regarding how their data are managed, stored, and accessed, will set a solid foundation for establishing any global data bank.

Blockchain-enabled Data Governance Framework for Enhancing Security and Efficiency in Multi-Cloud Environments through Ethereum, IPFS, and Cloud Infrastructure Integration

In today's digital landscape, the exponential growth of big data demands secure and efficient processing, particularly in complex multi-cloud environments. This paper proposes an innovative blockchain-enabled data governance framework, revolutionizing data management, processing, and security across diverse cloud infrastructures. The framework integrates cutting-edge technologies, including the Ethereum blockchain, the InterPlanetary File System (IPFS) protocol, and cloud solutions like OpenStack and Red Hat OpenShift. At its core, the framework utilizes Ethereum's robust smart contracts and consensus mechanisms to establish a decentralized and secure data governance model. This ensures data integrity, transparency, and immutability, mitigating risks associated with centralized storage and processing. The IPFS protocol complements blockchain by offering efficient data sharding and retrieval mechanisms, enhancing data accessibility and fault tolerance in distributed cloud environments. Through comprehensive testing and analysis, the proposed framework's value is demonstrated. Performance metrics, including throughput, latency, CPU utilization, and memory utilization, were meticulously evaluated to assess system efficiency and scalability. Results indicate high performance, with Ethereum's Proof of Authority (PoA) consensus mechanism enabling efficient transaction throughput of up to 1000 transactions per second. Additionally, the IPFS protocol exhibits effective data retrieval capabilities, with an average latency of 15 milliseconds for data access operations.

CIDACS' efforts towards an inclusive and dialogic data governance in Brazil: a focused literature review

The Centre for Data and Knowledge Integration for Health's (CIDACS) data governance efforts have primarily focused on legal, technical and operational procedures to provide high-quality linked administrative data for investigations on social determinants of health and the impact of social protection policies in low-income and vulnerable populations throughout Brazil. The Centre is moving towards an updated data governance model that incorporates the participation of, and consultation and dialogue with, data stakeholders, including groups covered by our linked data. To this end, this paper presents our procedures and challenges, outlining relevant considerations based on a focused literature review that aims to support the inclusion of societal participation in our revised data governance approach, which should be considered an ongoing process.

Smart Cities and data governance models

Smart Cities and data governance models

Data Governance Model For Nation-Wide Non-Profit Organization

According to Connolly's (2017) research, the context of nonprofit organizations exhibits variations when compared to commercial organizations or businesses, as supported by Zhang's (2010) study. Hence, it is imperative for both theoretical and empirical studies to contribute towards enhancing our comprehension of the strategy, implementation, and utilization of information systems in the specific context of nonprofit organizations. The investigation of information systems within the context of non-profit organizations offers a promising avenue for advancing the field of information systems research. This study focuses on the development of an information systems framework using the soft systems methodology, which has already been established. One opportunity for the advancement of information systems in non-profit organizations lies in the establishment of a comprehensive framework that facilitates adoption and is accompanied by robust data governance. This framework enables the analysis of data and the generation of valuable insights, thereby contributing to the development of information systems in the non-profit sector. The choice of data governance was informed by Zhang's (2010) research, which demonstrated that non-profit organizations face significant obstacles in the form of privacy and data security concerns. Furthermore, it is apparent that the preservation of data privacy plays a crucial role in the acceptance and utilization of information systems within non-profit entities. This research aims to contribute to the resolution of the issue by establishing a governance framework for information systems that effectively communicates to users the absence of data privacy risks associated with the systems employed by organizations. The objective of this study is to create a data governance model that will fill the research gap mentioned earlier and make a valuable contribution to the field of information systems research. The formation of the data governance model will involve the integration of soft systems methodology and the DAMA framework. The outcome of this study will be a data governance model specifically designed for a nationwide non-profit organization that utilizes microservices as its cutting-edge technology.

Managing public sector data: National challenges in the context of European Union’s new data governance models

In its regulatory enterprise to improve the conditions of data sharing and reuse, the European Union has enacted new legislation: the Data Governance Act (DGA). The DGA envisages new forms of sharing public sector data (PSD). Based on a legal analysis of the DGA and an in-depth study of data governance literature, this paper highlights what is at stake in the new regulatory framework and argues that more than the mere openness of more PSD will be necessary to ensure that the European Union policy goals are achieved, especially those concerning enhancing innovation for the common good. From this perspective, the paper argues that the public data trust model of data governance and the Responsible Research and Innovation approach offer two powerful tools for public sector data governance. In this context, this paper contributes to the debate about new data governance models and discusses tools and frameworks enabling the use of data for the common good. It also provides insights to public administration practitioners aiming to implement a framework for increased and sustainable PSD sharing.

Equitable Data Governance Models for the Participatory Sciences

AbstractWhen participants share data to a central entity, those who have taken on the responsibility of accepting the data and handling its management may also have control of decisions about the data, including its use, re‐use, accessibility, and more. Such concentrated control of data is often a default practice across many forms of participatory sciences, which can be extractive in some contexts and a way to protect participants in other contexts. To avoid extractive practices and related harms, projects can adopt structures so that those who make decisions about the data set and/or each datum are different from those responsible for executing the subsequent decisions about data management. We propose two alternative models for improving equity in data governance, each model representing a spectrum of options. With an individualized control model, each participant can place their data in a central repository while still retaining control of it, such as through simple opt‐in or opt‐out features or through blockchain technology. With a shared control model, representatives of salient participant groups, such as through participant advisory boards, collectively make decisions on behalf of their constituents. These equitable models are relevant to all participatory science systems, and particularly necessary in contexts where dominant‐culture institutions engage marginalized peoples.

Australian public perspectives on genomic data governance: responsibility, regulation, and logistical considerations.

Genomic sequencing generates huge volumes of data, which may be collected or donated to form large genomic databases. Such information can be stored for future use, either for the data donor themselves or by researchers to help improve our understanding of the genetic basis of disease. Creating datasets of this magnitude and diversity is only possible if patients, their families, and members of the public worldwide share their data. However, there is no consensus on the best technical approach to data sharing that also minimises risks to individuals and exploration of stakeholders' views on aspects of genomic data governance models-the ways genomic data is stored, managed, shared and used-has been minimal. To address this need, we conducted focus groups with 39 members of the Australian public exploring their views and preferences for different aspects of genomic data governance models. We found that consent and control were essential to participants, as they wanted the option to choose who had access to their data and for what purposes. Critically, participants wanted a trustworthy body to enforce regulation of data storage, sharing and usage. While participants recognised the importance of data accessibility, they also expressed a strong desire for data security. Finally, financial responsibility for data storage raised concerns for inequity as well as organisations and individuals using data in ethically contentious ways to generate profit. Our findings highlight some of the trade-offs that need to be considered in the development of genomic data governance systems.

Возможно ли лидерство «Группы двадцати» по вопросам управления данными? Есть ли шанс на сближение подходов?

Data is an infinite resource, a new form of capital in the knowledge economy. In absence of data regulation, global technological corporations seek to expand their influence at each link in data chains and use their market power to build monopolies. Data can generate not only profits for tech giants but also social value; however, market forces by themselves will not create data-based public goods. For this, government actions are needed at the county level, and interstate and multilateral institutional cooperation is required at the international level. To date, despite numerous initiatives to establish coordination mechanisms and data regulation, cooperation on data governance is highly fragmented and gridlocked due to contradictions and tough competition between players. One of the key disagreements is related to the regulation of cross-border data flows. The article explores contradictions between the regulatory practices of the central actors in the digital economy, the U.S., the European Union (EU) and China in the first place, and the approaches they promote in international institutions such as the World Trade Organization (WTO), the Group of G20 (G20), the Group of 7 (G7), and the Organisation for Economic Co-operation and Development (OECD). In particular, the authors look at the G20’s agenda on data governance and the initiative on free data flow with trust and consider its challenges and the risks of cooperation stagnation. The conclusion presents possible scenarios for future cooperation on data governance, their risks and perspectives, including the establishment of the Digital Economy and Data Governance Board by the G20, the G7’s initiative on shaping global digital order, deepening of cooperation on data governance within the G7 and the OECD, and the United Nations Conference on Trade and Development (UNCTAD) proposal on building a multilevel, distributed polycentric data-governance model with the UN playing a central role. The authors conclude with a proposal on cooperation within BRICS (Brazil, Russia, India, China and South Africa) aimed at shaping inclusive multilateral data governance.

Reimagining health services provision for neglected groups: The "personalization from below" phenomenon.

How can data-driven citizen science activities supporting health research and services provision meet the needs of unrepresented and neglected groups through increased personalization? In this short Perspective, we explore "personalization from below" as a concept designating forms of citizen science-based data altruism that specifically push for and enact a different understanding of both health services and personalization. We develop the argument that such phenomenon taking place outside "institutionalized" health-related practices could make health services provision more inclusive of values that matter to people. We contextualize instances of "personalization from below," discuss related data governance models and alternative public health interventions, and conclude by outlining three key arguments in favor of "personalization from below" and future research avenues.