Data Governance Model Research Articles

Big Tech platforms in health research: Re-purposing big data governance in light of the General Data Protection Regulation’s research exemption

The emergence of a global industry of digital health platforms operated by Big Tech corporations, and its growing entanglements with academic and pharmaceutical research networks, raise pressing questions on the capacity of current data governance models, regulatory and legal frameworks to safeguard the sustainability of the health research ecosystem. In this article, we direct our attention toward the challenges faced by the European General Data Protection Regulation in regulating the potentially disruptive engagement of Big Tech platforms in health research. The General Data Protection Regulation upholds a rather flexible regime for scientific research through a number of derogations to otherwise stricter data protection requirements, while providing a very broad interpretation of the notion of “scientific research”. Precisely the breadth of these exemptions combined with the ample scope of this notion could provide unintended leeway to the health data processing activities of Big Tech platforms, which have not been immune from carrying out privacy-infringing and socially disruptive practices in the health domain. We thus discuss further finer-grained demarcations to be traced within the broadly construed notion of scientific research, geared to implementing use-based data governance frameworks that distinguish health research activities that should benefit from a facilitated data protection regime from those that should not. We conclude that a “re-purposing” of big data governance approaches in health research is needed if European nations are to promote research activities within a framework of high safeguards for both individual citizens and society.

Common challenges of data governance

This paper focuses on the challenges that an organisation faces in the implementation of data governance. The inclusions in the paper range from the definition of ‘data governance’ to leadership participation, data strategy, roles and responsibilities, related budgets and ownership, training and communication plan, organisational cultural aspects (with respect to data) and business value for sustainability of data governance. The paper elaborates some of the challenges and highlights the lacunae in the process like business value of data governance. Before creating any governance structure, it is required to spend more time contemplating the ‘prime directive’ for data management. And the prime directive is straightforward and simple. It means to deliver to the end user, data that they have trust and confidence in, and that is precisely what they expect it to be, without the need for manual reconciliation and multiple transformations. This is the goal of data governance. The paper also provides in-depth information about data governance and why it is critical for organisations. In this paper, one will get an overview of the challenges involved and how data governance can help the organisation; a clear idea of the benefits and risks; a look at practical, real-world examples and the details of the components of an effective data governance programme. In addition, it will also articulate the characteristics of a data-driven culture and the 8-Point Data Governance Model that can be utilised to design and develop a scalable, fit-for-purpose data governance programme for any organisation.

Survey of Online Identity Management Techniques on Blockchain

Secure identity management is one of the controversial topics in the cybersecurity landscape. The user needs to rely on third-party providers for managing their personal information, which is a big security concern. Traditional identity systems typically suffer from single points of failure, lack of interoperability, and privacy issues such as encouraging mass data collection and user tracking. Blockchain technology has the potential to support novel data ownership and governance models with built-in control and consent mechanisms, which may benefit both users and businesses by alleviating these concerns. Blockchain-based systems provide the possibility for their users to insert their data in this distributed ledger. Users can trust the blockchain as it is leveraging consensus mechanisms to validate and gather the transactions in immutable blocks. The transparent and decentralized nature of the blockchain network enables the development of a non-refutable and unbreakable record of data, which is the fundamental feature of many applications, such as identity management.

Data Governance Maturity Level at the National Archives of the Republic of Indonesia

The National Archives of the Republic of Indonesia (ANRI) as an institution given mandate to carry out state duty in the field of archives has vision as a pillar of good governance and nation’s collective memory. To implement it, the study of the grand design of the archival system arranged. That is very related to the data governance implementation. Therefore, ANRI needs to know the maturity level of the data governance function which had been held. The assessment was done by referring to the Stanford Data Governance Model. The result showed that data governance is still at an initial level. The foundational aspects are on an average of 1,2 which contains awareness, formalization, and metadata. While on project aspects are on average of 1,5 consisting of stewardship, data quality, and master data. In total, ANRI is at the level of 1,35. ANRI needs to make improvements for data management planning activities referring to Data Management Body of Knowledge (DMBOK) with a focus on people, policies, and capabilities dimensions in all aspects. This research is expected to be helpful for ANRI to make improvements corresponding to the recommendations thus ANRI could implement national data archival properly.

Data Governance Maturity Level at the National Archives of the Republic of Indonesia

<em><span>The National Archives of the Republic of Indonesia (ANRI) as an institution given mandate to carry out state duty in the field of archives has vision as a pillar of good governance and nation’s collective memory. To implement it, the study of the grand design of the archival system arranged. That is very related to the data governance implementation. Therefore, ANRI needs to know the maturity level of the data governance function which had been held. The assessment was done by referring to the Stanford Data Governance Model. The result showed that data governance is still at an initial level. The foundational aspects are on an average of 1,2 which contains awareness, formalization, and metadata. While on project aspects are on average of 1,5 consisting of stewardship, data quality, and master data. In total, ANRI is at the level of 1,35. ANRI needs to make improvements for data management planning activities referring to Data Management Body of Knowledge (DMBOK) with a focus on people, policies, and capabilities dimensions in all aspects. This research is expected to be helpful for ANRI to make improvements corresponding to the recommendations thus ANRI could implement national data archival properly</span><span lang="EN-US">.</span></em>

Big Tech Platforms in Health Research: Re-purposing Big Data Governance in Light of the GDPR’s Research Exemption

The emergence of a global industry of digital health platforms operated by Big Tech corporations, and its growing entanglements with academic and pharmaceutical research networks, raise pressing questions on the capacity of current data governance models, regulatory and legal frameworks to safeguard the sustainability of the health research ecosystem. In this article, we direct our attention towards the challenges faced by the European General Data Protection Regulation (GDPR) in regulating the potentially disruptive engagement of Big Tech platforms in health research. The GDPR upholds a rather flexible regime for scientific research through a number of derogations to otherwise stricter data protection requirements, while providing a very broad interpretation of the notion of ‘scientific research’. Precisely the breadth of these exemptions combined with the ample scope of this notion could provide unintended leeway to the health data processing activities of Big Tech platforms, which have not been immune from carrying out privacy-infringing and socially disruptive practices in the health domain. We thus discuss further finer-grained demarcations to be traced within the broadly construed notion of scientific research, geared to implementing use-based data governance frameworks that distinguish health research activities that should benefit from a facilitated data protection regime from those that should not. We conclude that a ‘re-purposing’ of big data governance approaches in health research is needed if European nations are to promote research activities within a framework of high safeguards for both individual citizens and society.

Emerging models of data governance in the age of datafication

The article examines four models of data governance emerging in the current platform society. While major attention is currently given to the dominant model of corporate platforms collecting and economically exploiting massive amounts of personal data, other actors, such as small businesses, public bodies and civic society, take also part in data governance. The article sheds light on four models emerging from the practices of these actors: data sharing pools, data cooperatives, public data trusts and personal data sovereignty. We propose a social science-informed conceptualisation of data governance. Drawing from the notion of data infrastructure we identify the models as a function of the stakeholders’ roles, their interrelationships, articulations of value, and governance principles. Addressing the politics of data, we considered the actors’ competitive struggles for governing data. This conceptualisation brings to the forefront the power relations and multifaceted economic and social interactions within data governance models emerging in an environment mainly dominated by corporate actors. These models highlight that civic society and public bodies are key actors for democratising data governance and redistributing value produced through data. Through the discussion of the models, their underpinning principles and limitations, the article wishes to inform future investigations of socio-technical imaginaries for the governance of data, particularly now that the policy debate around data governance is very active in Europe.

Toward the Development of Data Governance Standards for Using Clinical Free-Text Data in Health Research: Position Paper.

BackgroundClinical free-text data (eg, outpatient letters or nursing notes) represent a vast, untapped source of rich information that, if more accessible for research, would clarify and supplement information coded in structured data fields. Data usually need to be deidentified or anonymized before they can be reused for research, but there is a lack of established guidelines to govern effective deidentification and use of free-text information and avoid damaging data utility as a by-product.ObjectiveThis study aimed to develop recommendations for the creation of data governance standards to integrate with existing frameworks for personal data use, to enable free-text data to be used safely for research for patient and public benefit.MethodsWe outlined data protection legislation and regulations relating to the United Kingdom for context and conducted a rapid literature review and UK-based case studies to explore data governance models used in working with free-text data. We also engaged with stakeholders, including text-mining researchers and the general public, to explore perceived barriers and solutions in working with clinical free-text.ResultsWe proposed a set of recommendations, including the need for authoritative guidance on data governance for the reuse of free-text data, to ensure public transparency in data flows and uses, to treat deidentified free-text data as potentially identifiable with use limited to accredited data safe havens, and to commit to a culture of continuous improvement to understand the relationships between the efficacy of deidentification and reidentification risks, so this can be communicated to all stakeholders.ConclusionsBy drawing together the findings of a combination of activities, we present a position paper to contribute to the development of data governance standards for the reuse of clinical free-text data for secondary purposes. While working in accordance with existing data governance frameworks, there is a need for further work to take forward the recommendations we have proposed, with commitment and investment, to assure and expand the safe reuse of clinical free-text data for public benefit.

Data ecosystems for protecting European citizens’ digital rights

Purpose This paper aims to spark a debate by presenting the need for developing data ecosystems in Europe that meet the social and public good while committing to democratic and ethical standards; suggesting a taxonomy of data infrastructures and institutions to support this need; using the case study of Barcelona as the flagship city trailblazing a critical policy agenda of smart cities to show the limitations and contradictions of the current state of affairs; and ultimately, proposing a preliminary roadmap for institutional and governance empowerment that could enable effective data ecosystems in Europe. Design/methodology/approach This paper draws on lessons learned in previous publications available in the sustainability (Calzada, 2018), regions (Calzada and Cowie, 2017; Calzada, 2019), Zenodo (Calzada and Almirall, 2019), RSA Journal (Calzada, 2019) and IJIS (Calzada, 2020) journals and ongoing and updated fieldwork about the Barcelona case study stemming from an intensive fieldwork action research that started in 2017. The methodology used in these publications was based on the mixed-method technique of triangulation via action research encompassing in-depth interviews, direct participation in policy events and desk research. The case study was identified as the most effective methodology. Findings This paper, drawing from lessons learned from the Barcelona case study, elucidates on the need to establish pan-European data infrastructures and institutions – collectively data ecosystems – to protect citizens’ digital rights in European cities and regions. The paper reveals three main priorities proposing a preliminary roadmap for local and regional governments, namely, advocacy, suggesting the need for city and regional networks; governance, requiring guidance and applied, neutral and non-partisan research in policy; and pan-European agencies, leading and mobilising data infrastructures and institutions at the European level. Research limitations/implications From the very beginning, this paper acknowledges its ambition, and thus its limitations and clarifies its attempt to provide just an overview rather than a deep research analysis. This paper presents several research limitations and implications regarding the scope. The paper starts by presenting the need for data ecosystems, then structures this need through two taxonomies, all illustrated through the Barcelona case study and finally, concludes with a roadmap consisting of three priorities. The paper uses previous published and ongoing fieldwork findings in Barcelona as a way to lead, and thus encourage the proliferation of more cases through Cities Coalition for Digital Rights (CCDR). Practical implications This paper presents practical implications for local and regional authorities of the CCDR network. As such, the main three priorities of the preliminary roadmap could help those European cities and regions already part of the CCDR network to establish and build operational data ecosystems by establishing a comprehensive pan-European policy from the bottom-up that aligns with the timely policy developments advocated by the European Commission. This paper can inspire policymakers by providing guidelines to better coordinate among a diverse set of cities and regions in Europe. Social implications The leading data governance models worldwide from China and the USA and the advent of Big Data are dramatically reshaping citizens’ relationship with data. Against this backdrop and directly influenced by the General Data Protection Regulation (GDPR), Europe has, perhaps, for the first time, spoken with its own voice by blending data and smart city research and policy formulations. Inquiries and emerging insights into the potential urban experiments on data ecosystems, consisting of data infrastructures and institutions operating in European cities and regions, become increasingly crucial. Thus, the main social implications are for those multi-stakeholder policy schemes already operating in European cities and regions. Originality/value In previous research, data ecosystems were not directly related to digital rights amidst the global digital geopolitical context and, more specifically, were not connected to the two taxonomies (on data infrastructures and institutions) that could be directly applied to a case study, like the one presented about Barcelona. Thus, this paper shows novelty and originality by also opening up (based on previous fieldwork action research) a way to take strategic action to establish a pan-European strategy among cities and regions through three specific priorities. This paper can ultimately support practice and lead to new research and policy avenues.

Reform and innovation of artificial intelligence technology for information service in university physical education

This article analyzes the reform of information services in university physical education based on artificial intelligence technology and conducts in-depth and innovative research on it. In-depth analysis of the relationship between big data and the development and application of information technology such as the Internet, Internet of Things, cloud computing, to clarify the difference and connection between big data, informatization and intelligence. Artificial intelligence will bring opportunities for changes in data collection, management decision-making, governance models, education and teaching, scientific research services, evaluation and evaluation of physical education in our university. At the same time, big data education management in colleges and universities faces many challenges such as the balance of privacy and freedom, data hegemony, data junk, data standards, and data security, and they have many negative effects. In accordance with the requirements of educational modernization, centering on the goal of intelligent and humanized education management, it aims existing issues in college physical education management.

Emerging health data platforms: From individual control to collective data governance

AbstractHealth data have enormous potential to transform healthcare, health service design, research, and individual health management. However, health data collected by institutions tend to remain siloed within those institutions limiting access by other services, individuals or researchers. Further, health data generated outside health services (e.g., from wearable devices) may not be easily accessible or useable by individuals or connected to other parts of the health system. There are ongoing tensions between data protection and the use of data for the public good (e.g., research). Concurrently, there are a number of data platforms that provide ways to disrupt these traditional health data siloes, giving greater control to individuals and communities. Through four case studies, this paper explores platforms providing new ways for health data to be used for personal data sharing, self-health management, research, and clinical care. The case-studies include data platforms: PatientsLikeMe, Open Humans, Health Record Banks, and unforgettable.me. These are explored with regard to what they mean for data access, data control, and data governance. The case studies provide insight into a shift from institutional to individual data stewardship. Looking at emerging data governance models, such as data trusts and data commons, points to collective control over health data as an emerging approach to issues of data control. These shifts pose challenges as to how “traditional” health services make use of data collected on these platforms. Further, it raises broader policy questions regarding how to decide what public good data should be put towards.

Developing data governance standards for using free-text data in research (TexGov)

BackgroundFree-text data represent a vast, untapped source of rich information to guide research and public service delivery. Free-text data contain a wealth of additional detail that, if more accessible, would clarify and supplement information coded in structured data fields. Personal data usually need to be de-identified or anonymised before they can be used for purposes such as audit and research, but there are major challenges in finding effective methods to de-identify free-text that do not damage data utility as a by-product. The main aim of the TexGov project is to work towards data governance standards to enable free-text data to be used safely for public benefit.
 MethodsWe conducted: a rapid literature review to explore the data governance models used in working with free-text data, plus case studies of systems making de-identified free-text data available for research; we engaged with text mining researchers and the general public to explore barriers and solutions in working with free-text; and we outlined (UK) data protection legislation and regulations for context.
 ResultsWe reviewed 50 articles and the models of 4 systems providing access to de-identified free-text. The main emerging themes were: i) patient involvement at identifiable and de-identified data stages; ii) questions of consent and notification for the reuse of free-text data; iii) working with identifiable data for Natural Language Processing algorithm development; and iv) de-identification methods and thresholds of reliability.
 ConclusionWe have proposed a set of recommendations, including: ensuring public transparency in data flows and uses; adhering to the principles of minimal data extraction; treating de-identified blacklisted free-text as potentially identifiable with use limited to accredited data safe-havens; and, the need to commit to a culture of continuous improvement to understand the relationships between accuracy of de-identification and re-identification risk, so this can be communicated to all stakeholders.

Big Data Governance Model for Effective Operation in Cyberspace

Big Data Governance Model for Effective Operation in Cyberspace

Privacy Revolution in the Big Data Age: from Privacy Protection to Personal Data Governance

The arrival of the big data era also means the emergence of the privacy crisis. Nowadays, privacy is challenged and privacy protection theories have obvious limitations. In the big data age, the focus of privacy protection should be shifted from traditional physical protection to personal data governance. Personal data has many similarities with privacy but also some noted differences. Compared with privacy’s static and exclusive features, personal data is characterized by extraversion and fluidity, and is a combination of personal interests and property interests. Based on the above conditions and China’s data development practices, it is crucial to pay attention to the transformation process of the data governance model especially from the privacy protection to the personal data governance, which is the core of achieving the balance between personal data protection and data application. To promote personal data governance, the following strategies should be applied: a) adhering to the purpose of balancing data application and personal data protection; b) attaching importance to data values and allocating data interests on data flowing process; c) improving personal data protection legislation; d) inducing technological innovation.

Development of electronic medical records for clinical and research purposes: the breast cancer module using an implementation framework in a middle income country- Malaysia

BackgroundAdvances in medical domain has led to an increase of clinical data production which offers enhancement opportunities for clinical research sector. In this paper, we propose to expand the scope of Electronic Medical Records in the University Malaya Medical Center (UMMC) using different techniques in establishing interoperability functions between multiple clinical departments involving diagnosis, screening and treatment of breast cancer and building automatic systems for clinical audits as well as for potential data mining to enhance clinical breast cancer research in the future.ResultsQuality Implementation Framework (QIF) was adopted to develop the breast cancer module as part of the in-house EMR system used at UMMC, called i-Pesakit©. The completion of the i-Pesakit© Breast Cancer Module requires management of clinical data electronically, integration of clinical data from multiple internal clinical departments towards setting up of a research focused patient data governance model. The 14 QIF steps were performed in four main phases involved in this study which are (i) initial considerations regarding host setting, (ii) creating structure for implementation, (iii) ongoing structure once implementation begins, and (iv) improving future applications. The architectural framework of the module incorporates both clinical and research needs that comply to the Personal Data Protection Act.ConclusionThe completion of the UMMC i-Pesakit© Breast Cancer Module required populating EMR including management of clinical data access, establishing information technology and research focused governance model and integrating clinical data from multiple internal clinical departments. This multidisciplinary collaboration has enhanced the quality of data capture in clinical service, benefited hospital data monitoring, quality assurance, audit reporting and research data management, as well as a framework for implementing a responsive EMR for a clinical and research organization in a typical middle-income country setting. Future applications include establishing integration with external organization such as the National Registration Department for mortality data, reporting of institutional data for national cancer registry as well as data mining for clinical research. We believe that integration of multiple clinical visit data sources provides a more comprehensive, accurate and real-time update of clinical data to be used for epidemiological studies and audits.

Development and characteristics of the Provincial Overdose Cohort in British Columbia, Canada.

IntroductionBritish Columbia (BC), Canada declared a public health emergency in April 2016 for opioid overdose. Comprehensive data was needed to identify risk factors, inform interventions, and evaluate response actions. We describe the development of an overdose cohort, including linkage strategy, case definitions, and data governance model, and present the resulting characteristics, including data linkage yields and case overlap among data sources.MethodsOverdose events from hospital admissions, physician visits, poison centre and ambulance calls, emergency department visits, and coroner’s data were grouped into episodes if records were present in multiple sources. A minimum of five years of universal health care records (all prescription dispensations, fee-for-service physician billings, emergency department visits and hospitalizations) were appended for each individual. A 20% random sample of BC residents and a 1:5 matched case-control set were generated. Consultation and prioritization ensured analysts worked to address questions to directly inform public health actions.Results10,456 individuals suffered 14,292 overdoses from January 1, 2015 to Nov 30, 2016. Only 28% of overdose events were found in more than one dataset with the unique contribution of cases highest from ambulance records (32%). Compared with fatal overdoses, non-fatal events more often involved females, younger individuals (20 to 29 years) and those 60 or older. In 78% of illegal drug deaths, there was no associated ambulance response. In the year prior to first recorded overdose, 60% of individuals had at least one ED visit, 31% at least one hospital admission, 80% at least one physician visit, and 87% had filled at least one prescription in a community pharmacy.ConclusionWhile resource-intensive to establish, a linked cohort is useful for characterizing the full extent of the epidemic, defining sub-populations at risk, and patterns of contact with the health system. Overdose studies in other jurisdictions should consider the inclusion of multiple data sources.

Big Data Governance Model for Smart Water Management

Big Data Governance Model for Smart Water Management

Priorities for science to overcome hurdles thwarting the full promise of the 'digital agriculture' revolution.

The world needs to produce more food, more sustainably, on a planet with scarce resources and under changing climate. The advancement of technologies, computing power and analytics offers the possibility that ‘digitalisation of agriculture’ can provide new solutions to these complex challenges. The role of science is to evidence and support the design and use of digital technologies to realise these beneficial outcomes and avoid unintended consequences. This requires consideration of data governance design to enable the benefits of digital agriculture to be shared equitably and how digital agriculture could change agricultural business models; that is, farm structures, the value chain and stakeholder roles, networks and power relations, and governance. We argue that this requires transdisciplinary research (at pace), including explicit consideration of the aforementioned socio‐ethical issues, data governance and business models, alongside addressing technical issues, as we now have to simultaneously deal with multiple interacting outcomes in complex technical, social, economic and governance systems. The exciting prospect is that digitalisation of science can enable this new, and more effective, way of working. The question then becomes: how can we effectively accelerate this shift to a new way of working in agricultural science? As well as identifying key research areas, we suggest organisational changes will be required: new research business models, agile project management; new skills and capabilities; and collaborations with new partners to develop ‘technology ecosystems’. © 2018 The Authors. © 2018 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Deliver a Fit-For-Purpose Data Governance Model and Strategy That Act As a Key Business Enabler

Deliver a Fit-For-Purpose Data Governance Model and Strategy That Act As a Key Business Enabler

Toward a data governance model for the Kenya health professional regulatory authorities

PurposeThe purpose of this paper is to determine the status, drivers, and barriers to data governance at the health professional regulatory authorities in Kenya. This study aims to develop a model that can be used to establish a formal data governance program at these regulatory authorities.Design/methodology/approachThis study used data governance decision areas based on the study of Khatri and Brown (2010). Qualitative and quantitative research methods were used in this study to collect data.FindingsThis paper identified maintenance of quality of data, achieving customer satisfaction, ensuring data security and control, and achieving operational efficiency as the drivers of data governance at the regulatory authorities. The authorities are faced with lack of data governance awareness, lack of management ownership and support, as well as limited funding and resource allocations as barriers to data governance. This study proposed that for the authorities to increase their data governance, they need to identify their data as an asset, initiate more data quality management mechanism, restrict access to their data, create awareness, and increase management, ownership and support.Practical implicationsA data governance program for healthcare workforce data is necessary for healthcare planning which influences national policy in the healthcare and the overall delivery of health services in a country.Originality/valueThe paper proposes a model that health professional regulators in developing countries that are facing limited resources can be used to establish a formal data governance program.