The construction and development of the digital economy,digital society and digital government are facing some common basic problems.Among them,the construction of the data governance system and the improvement of dat...The construction and development of the digital economy,digital society and digital government are facing some common basic problems.Among them,the construction of the data governance system and the improvement of data governance capacity are short boards and weak links,which have seriously restricted the construction and development of the digital economy,digital society and digital government.At present,the broad concept of data governance goes beyond the scope of traditional data governance,which“involves at least four aspects:the establishment of data asset status,management system and mechanism,sharing and openness,security and privacy protection”.Traditional information technologies and methods are powerless to comprehensively solve these problems,so it is urgent to improve understanding and find another way to reconstruct the information technology architecture to provide a scientific and reasonable technical system for effectively solving the problems of data governance.This paper redefined the information technology architecture and proposed the data architecture as the connection link and application support system between the traditional hardware architecture and software architecture.The data registration system is the core composition of the data architecture,and the public key encryption and authentication system is the key component of the data architecture.This data governance system based on the data architecture supports complex,comprehensive,collaborative and cross-domain business application scenarios.It provides scientific and feasible basic support for the construction and development of the digital economy,digital society and digital government.展开更多
The study aimed to develop a customized Data Governance Maturity Model (DGMM) for the Ministry of Defence (MoD) in Kenya to address data governance challenges in military settings. Current frameworks lack specific req...The study aimed to develop a customized Data Governance Maturity Model (DGMM) for the Ministry of Defence (MoD) in Kenya to address data governance challenges in military settings. Current frameworks lack specific requirements for the defence industry. The model uses Key Performance Indicators (KPIs) to enhance data governance procedures. Design Science Research guided the study, using qualitative and quantitative methods to gather data from MoD personnel. Major deficiencies were found in data integration, quality control, and adherence to data security regulations. The DGMM helps the MOD improve personnel, procedures, technology, and organizational elements related to data management. The model was tested against ISO/IEC 38500 and recommended for use in other government sectors with similar data governance issues. The DGMM has the potential to enhance data management efficiency, security, and compliance in the MOD and guide further research in military data governance.展开更多
Data factors are becoming the core driving force in the intelligent transformation of libraries.Based on a systematic review of the progress in data governance practices in libraries both domestically and internationa...Data factors are becoming the core driving force in the intelligent transformation of libraries.Based on a systematic review of the progress in data governance practices in libraries both domestically and internationally,this study delves into the mechanism by which data governance promotes data factorization and proposes implementation paths for data governance oriented toward data factorization.The aim is to facilitate the intelligent transformation and high-quality development of libraries.展开更多
In the era of big data, data application based on data governance has become an inevitable trend in the construction of smart campus in higher education. In this paper, a set of data governance system framework coveri...In the era of big data, data application based on data governance has become an inevitable trend in the construction of smart campus in higher education. In this paper, a set of data governance system framework covering the whole life cycle of data suitable for higher education is proposed, and based on this, the ideas and methods of data governance are applied to the construction of data management system for the basic development status of faculties by combining the practice of data governance of Donghua University.It forms a closed-loop management of data in all aspects, such as collection, information feedback, and statistical analysis of the basic development status data of the college. While optimizing the management business of higher education, the system provides a scientific and reliable basis for precise decision-making and strategic development of higher education.展开更多
With a view to adopting to the globalized business landscape,organizations rely on third-party business relationships to enhance their operations,expand their capabilities,and drive innovation.While these collaboratio...With a view to adopting to the globalized business landscape,organizations rely on third-party business relationships to enhance their operations,expand their capabilities,and drive innovation.While these collaborations offer numerous benefits,they also introduce a range of risks that organizations must carefully mitigate.If the obligation to meet the regulatory requirements is added to the equation,mitigating the third-party risk related to data governance,becomes one of the biggest challenges.展开更多
Contemporary mainstream big data governance platforms are built atop the big data ecosystem components,offering a one-stop development and analysis governance platform for the collection,transmission,storage,cleansing...Contemporary mainstream big data governance platforms are built atop the big data ecosystem components,offering a one-stop development and analysis governance platform for the collection,transmission,storage,cleansing,transformation,querying and analysis,data development,publishing,and subscription,sharing and exchange,management,and services of massive data.These platforms serve various role members who have internal and external data needs.However,in the era of big data,the rapid update and iteration of big data technologies,the diversification of data businesses,and the exponential growth of data present more challenges and uncertainties to the construction of big data governance platforms.This paper discusses how to effectively build a data governance platform under the big data system from the perspectives of functional architecture,logical architecture,data architecture,and functional design.展开更多
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 enterpr...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.展开更多
at present,data security has become the most urgent and primary issue in the era of digital economy.Marine scientific data security is the most urgent core issue of marine data resource management and sharing service....at present,data security has become the most urgent and primary issue in the era of digital economy.Marine scientific data security is the most urgent core issue of marine data resource management and sharing service.This paper focuses on the analysis of the needs of marine scientific data security governance,in-depth development of marine scientific data security governance approaches and methods,and practical application in the national marine scientific data center,optimizing the existing data management model,ensuring the safety of marine scientific data,and fully releasing the data value.展开更多
With the rapid evolution of artificial intelligence(AI)technologies,the medical industry is undergoing a profound transformation driven by data intelligence.As the foundational element for intelligent diagnosis,precis...With the rapid evolution of artificial intelligence(AI)technologies,the medical industry is undergoing a profound transformation driven by data intelligence.As the foundational element for intelligent diagnosis,precision prevention,and public health governance,medical data is characterized by massive volume,complex structure,diverse sources,high dimensionality,strong privacy,and high timeliness.Traditional data analysis methods are no longer sufficient to meet the comprehensive requirements of data security,intelligent processing,and decision support.Through techniques such as machine learning,deep learning,natural language processing,and multimodal fusion,AI provides robust technical support for medical data cleaning,governance,mining,and application.At the data level,intelligent algorithms enable the standardization,structuring,and interoperability of medical data,promoting information sharing across medical systems.At the model level,AI supports auxiliary diagnosis and precision treatment through image recognition,medical record analysis,and knowledge graph construction.At the system level,intelligent decision-support platforms continuously enhance the efficiency and accuracy of healthcare services.However,the widespread adoption of AI in medicine still faces challenges such as privacy protection,data security,model interpretability,and the lack of unified industry standards.Based on a systematic review of AI’s key supporting technologies in medical data processing and application,this paper focuses on the compliance challenges and adaptation strategies during industry integration and proposes an adaptation framework centered on“technological trustworthiness,data security,and industry collaboration.”The study provides theoretical and practical insights for promoting the standardized and sustainable development of AI in the healthcare industry.展开更多
Purpose:This research endeavors to investigate the impact of open government data on corporate investment,emphasizing the exploration of underlying mechanisms,heterogeneous effects,and implications for investment effi...Purpose:This research endeavors to investigate the impact of open government data on corporate investment,emphasizing the exploration of underlying mechanisms,heterogeneous effects,and implications for investment efficiency.Utilizing the implementation of government data open platforms as a quasi-natural experiment,this study aims to elucidate how public data transparency affects firms’investment decisions and resource allocation.Design/methodology/approach:This study employs a staggered Difference-in-Differences(DID)model as its principal methodological framework.This approach facilitates causal inference by examining the differential changes in corporate investment between firms influenced by the data openness policy and those that remain unaffected over time.Findings:The findings indicate that open government data substantially enhance corporate investment levels.A mechanistic analysis identifies three principal channels through which this effect is mediated:alleviation of overall financing constraints,reduction of financing costs,and expansion of the financing scale.A heterogeneity analysis suggests that the positive impact is more pronounced in state-owned enterprises,high-tech firms,and companies experiencing elevated levels of macroeconomic uncertainty.Moreover,the transparency of government data improves the responsiveness of corporate investment to emerging opportunities,thereby augmenting the overall efficiency of corporate investment.Research limitations:This study primarily examined the influence of government data transparency on corporate investment,while not accounting for the effects of macroeconomic variability,internal corporate governance frameworks,and industry-specific regulatory policies.Practical implications:Government open data platforms can effectively boost corporate investment and resource allocation.Policymakers should focus on improving the quality and accessibility of these data,especially in areas with high economic uncertainty,to support business investments.Firms,particularly high-tech and financially constrained firms,can use open data to ease capital limitations and find investment opportunities.Regulators should promote data transparency to enhance economic vitality through efficient corporate investments.Originality/value:This study enhances the existing literature by offering causal evidence of the impact of open government data on corporate investment,a subject that has been relatively underexplored empirically.By employing a quasi-natural experiment centered on the implementation of government data platforms,this study adopts a robust methodological approach to address endogeneity issues,thereby advancing methodological rigor in investigations of public data governance and corporate behavior.展开更多
Purpose: This paper aims to assess if the extent of openness and the coverage of data sets released by European governments have a significant impact on citizen trust in public institutions.Design/methodology/approach...Purpose: This paper aims to assess if the extent of openness and the coverage of data sets released by European governments have a significant impact on citizen trust in public institutions.Design/methodology/approach: Data for openness and coverage have been collected from the Open Data Inventory 2018(ODIN), by Open Data Watch;institutional trust is built up as a formative construct based on the European Social Survey(ESS), Round 9. The relations between the open government data features and trust have been tested on the basis of structural equation modelling(SEM).Findings: The paper reveals that as European governments improve data openness, disaggregation, and time coverage, people tend to trust them more. However, the size of the effect is still small and, comparatively, data coverage effect on citizens' confidence is more than twice than the impact of openness.Research limitations: This paper analyzes the causal effect of Open Government Data(OGD) features captured in a certain moment of time. In upcoming years, as OGD is implemented and a more consistent effect on people is expected, time series analysis will provide with a deeper insight.Practical implications: Public officers should continue working in the development of a technological framework that contributes to make OGD truly open. They should improve the added value of the increasing amount of open data currently available in order to boost internal and external innovations valuable both for public agencies and citizens.Originality/value: In a field of knowledge with little quantitative empirical evidence, this paper provides updated support for the positive effect of OGD strategies and it also points out areas of improvement in terms of the value that citizens can get from OGD coverage and openness.展开更多
Data Integrity is a critical component of Data lifecycle management. Its importance increases even more in a complex and dynamic landscape. Actions like unauthorized access, unauthorized modifications, data manipulati...Data Integrity is a critical component of Data lifecycle management. Its importance increases even more in a complex and dynamic landscape. Actions like unauthorized access, unauthorized modifications, data manipulations, audit tampering, data backdating, data falsification, phishing and spoofing are no longer restricted to rogue individuals but in fact also prevalent in systematic organizations and states as well. Therefore, data security requires strong data integrity measures and associated technical controls in place. Without proper customized framework in place, organizations are prone to high risk of financial, reputational, revenue losses, bankruptcies, and legal penalties which we shall discuss further throughout this paper. We will also explore some of the improvised and innovative techniques in product development to better tackle the challenges and requirements of data security and integrity.展开更多
Joint prevention and control is a social organization model dealing with the governance of public health and security incidents.The governance models should have the features of multiple subjects co-governing and dist...Joint prevention and control is a social organization model dealing with the governance of public health and security incidents.The governance models should have the features of multiple subjects co-governing and distributed cooperating.Their purposes are to solve and improve the governance efficiency of dealing with public health and security incidents at the executive level.However,there are still many deficiencies in the current data governance and collaborative governance of joint prevention and control systems,which are mainly reflected in incomplete data collection,unimpeded data sharing,inflexible collaborative cooperation,and inadequate collaborative supervision.Therefore,a new innovative governance model is urgently needed.Blockchain technology is suitable for implementing multiparty data sharing and cooperation,and at the same time,it supports penetrating supervision and management.This paper studies the blockchain model for joint governance of public health and security incidents.It focuses on the multiagent collaborative prevention and control governance model,which provides a new opportunity for model innovation in data governance and in cooperative governance.展开更多
The increasing dependence on data highlights the need for a detailed understanding of its behavior,encompassing the challenges involved in processing and evaluating it.However,current research lacks a comprehensive st...The increasing dependence on data highlights the need for a detailed understanding of its behavior,encompassing the challenges involved in processing and evaluating it.However,current research lacks a comprehensive structure for measuring the worth of data elements,hindering effective navigation of the changing digital environment.This paper aims to fill this research gap by introducing the innovative concept of“data components.”It proposes a graphtheoretic representation model that presents a clear mathematical definition and demonstrates the superiority of data components over traditional processing methods.Additionally,the paper introduces an information measurement model that provides a way to calculate the information entropy of data components and establish their increased informational value.The paper also assesses the value of information,suggesting a pricing mechanism based on its significance.In conclusion,this paper establishes a robust framework for understanding and quantifying the value of implicit information in data,laying the groundwork for future research and practical applications.展开更多
In the digital economy,data assets have come to be regarded as the new oil,underscoring their critical role in modern business models and decision-making processes.In response,the Chinese government has prioritized th...In the digital economy,data assets have come to be regarded as the new oil,underscoring their critical role in modern business models and decision-making processes.In response,the Chinese government has prioritized the formalization and management of data assets,introducing policies aimed at enhancing their value.Given the unique nature of data assets,characterized by the potential for both depreciation and appreciation,precise methods for assessing value changes and realizing the appreciation of data assets are urgently needed.Effective data governance techniques,including data cleaning,acquisition,and integration,are essential for maximizing the economic potential of data assets.Against this backdrop,this survey explores two key issues from a data governance perspective:the enhancement of data asset value and the quantification of its changes.It is structured around two primary dimensions:first,by examining data assets'inherent properties and quality indicators,and second,by utilizing an“on-demand evaluation”approach that assesses value of data assets in response to the performance of downstream machine learning models.By advancing understanding of these issues,this study seeks to optimize strategies for maximizing the economic impact of data assets through refined data governance practices.展开更多
Company X is a provincial state-owned enterprise. After sufficient investigation and research, its financial personnel combined with the actual needs of the company, using EXCEL server, set up a scientific research pr...Company X is a provincial state-owned enterprise. After sufficient investigation and research, its financial personnel combined with the actual needs of the company, using EXCEL server, set up a scientific research project budget management system, so that the system can replace manual budget control work;At the same time, through the data association work with UFIDA and other software, after in-depth data governance, a data bridge is built to form logical basic big data, which realizes automatic writing of vouchers and real-time reading of data, and generates the required report forms with designed logical relationship, thus greatly improving the work efficiency of finance personnel and relevant management personnel.展开更多
The processing of data such as personal information constitute the infrastructure of automated administration.Under the multi-legal requirements for personal information protection,data security,and algorithm governan...The processing of data such as personal information constitute the infrastructure of automated administration.Under the multi-legal requirements for personal information protection,data security,and algorithm governance,the legal regulation of such administrative activities should pursue the value goal of balancing the use and protection of personal information,promoting the efficient,safe,and orderly flow of personal information,and forming a standardized order for the use of personal information,and build a systematic public governance mechanism.From the perspective of reflective law,the personal information governance of automated administration shall promote its self-regulation under the stimulation of external regulation.Fundamental rights are a combination of dual reflective structures of the political system and the legal system,forming the constitutional basis for the self-regulation of automated administration.The personal information protection system and digital administrative law are external regulations from the legal system,forming the legal basis and providing automated administration with scenario-based and classified governance ideas.The government data scenario focuses on the standardized governance of personal information processing activities.It takes the principles of fair information practice as its basic framework and the necessity to perform statutory duties as its legal basis.The algorithmic decision-making scenario forms its framework system based on the principle of algorithmic due process,which clarifies the information subject’s right not to be subject to automated decision-making and the mechanism for exercising the rights.It establishes the information flow order in public algorithms based on the procedural law regulations of prior algorithm designs.展开更多
Open data are currently a hot topic and are associated with realising ambitions such as a more transparent and efficient government,solving societal problems,and increasing economic value.To describe and monitor the s...Open data are currently a hot topic and are associated with realising ambitions such as a more transparent and efficient government,solving societal problems,and increasing economic value.To describe and monitor the state of open data in countries and organisations,several open data assessment frameworks were developed.Despite high scores in these assessment frameworks,the actual(re)use of open government data(OGD)fails to live up to its expectations.Our review of existing open data assessment frameworks reveals that these only cover parts of the open data ecosystem.We have developed a framework,which assesses open data supply,open data governance,and open data user characteristics holistically.This holistic open data framework assesses the maturity of the open data ecosystem and proves to be a useful tool to indicate which aspects of the open data ecosystem are successful and which aspects require attention.Our initial assessment in the Netherlands indicates that the traditional geographical data perform significantly better than non-geographical data,such as healthcare data.Therefore,open geographical data policies in the Netherlands may provide useful cues for other OGD strategies.展开更多
National spatial data infrastructures are key to achieving the Digital Earth vision.In many cases,national datasets are integrated from local datasets created and maintained by municipalities.Examples are address,buil...National spatial data infrastructures are key to achieving the Digital Earth vision.In many cases,national datasets are integrated from local datasets created and maintained by municipalities.Examples are address,building and topographic information.Integration of local datasets may result in a dataset satisfying the needs of users of national datasets,but is it productive for those who create and maintain the data?This article presents a stakeholder analysis of the Basisregistratie Adressen en Gebouwen(BAG),a collection of base information about addresses and buildings in the Netherlands.The information is captured and maintained by municipalities and integrated into a national base register by Kadaster,the Cadastre,Land Registry and Mapping Agency of the Netherlands.The stakeholder analysis identifies organisations involved in the BAG governance framework,describes their interests,rights,ownerships and responsibilities in the BAG,and maps the relationships between them.Analysis results indicate that Kadaster and the municipalities have the highest relative importance in the governance framework of the BAG.The study reveals challenges of setting up a governance framework that maintains the delicate balance between the interests of all stakeholders.The results provide guidance for SDI role players setting up governance frameworks for national or global datasets.展开更多
文摘The construction and development of the digital economy,digital society and digital government are facing some common basic problems.Among them,the construction of the data governance system and the improvement of data governance capacity are short boards and weak links,which have seriously restricted the construction and development of the digital economy,digital society and digital government.At present,the broad concept of data governance goes beyond the scope of traditional data governance,which“involves at least four aspects:the establishment of data asset status,management system and mechanism,sharing and openness,security and privacy protection”.Traditional information technologies and methods are powerless to comprehensively solve these problems,so it is urgent to improve understanding and find another way to reconstruct the information technology architecture to provide a scientific and reasonable technical system for effectively solving the problems of data governance.This paper redefined the information technology architecture and proposed the data architecture as the connection link and application support system between the traditional hardware architecture and software architecture.The data registration system is the core composition of the data architecture,and the public key encryption and authentication system is the key component of the data architecture.This data governance system based on the data architecture supports complex,comprehensive,collaborative and cross-domain business application scenarios.It provides scientific and feasible basic support for the construction and development of the digital economy,digital society and digital government.
文摘The study aimed to develop a customized Data Governance Maturity Model (DGMM) for the Ministry of Defence (MoD) in Kenya to address data governance challenges in military settings. Current frameworks lack specific requirements for the defence industry. The model uses Key Performance Indicators (KPIs) to enhance data governance procedures. Design Science Research guided the study, using qualitative and quantitative methods to gather data from MoD personnel. Major deficiencies were found in data integration, quality control, and adherence to data security regulations. The DGMM helps the MOD improve personnel, procedures, technology, and organizational elements related to data management. The model was tested against ISO/IEC 38500 and recommended for use in other government sectors with similar data governance issues. The DGMM has the potential to enhance data management efficiency, security, and compliance in the MOD and guide further research in military data governance.
文摘Data factors are becoming the core driving force in the intelligent transformation of libraries.Based on a systematic review of the progress in data governance practices in libraries both domestically and internationally,this study delves into the mechanism by which data governance promotes data factorization and proposes implementation paths for data governance oriented toward data factorization.The aim is to facilitate the intelligent transformation and high-quality development of libraries.
基金Special Project for Renovation and Procurement of Donghua University,Ministry of Education,China (No. CG202002845)。
文摘In the era of big data, data application based on data governance has become an inevitable trend in the construction of smart campus in higher education. In this paper, a set of data governance system framework covering the whole life cycle of data suitable for higher education is proposed, and based on this, the ideas and methods of data governance are applied to the construction of data management system for the basic development status of faculties by combining the practice of data governance of Donghua University.It forms a closed-loop management of data in all aspects, such as collection, information feedback, and statistical analysis of the basic development status data of the college. While optimizing the management business of higher education, the system provides a scientific and reliable basis for precise decision-making and strategic development of higher education.
文摘With a view to adopting to the globalized business landscape,organizations rely on third-party business relationships to enhance their operations,expand their capabilities,and drive innovation.While these collaborations offer numerous benefits,they also introduce a range of risks that organizations must carefully mitigate.If the obligation to meet the regulatory requirements is added to the equation,mitigating the third-party risk related to data governance,becomes one of the biggest challenges.
文摘Contemporary mainstream big data governance platforms are built atop the big data ecosystem components,offering a one-stop development and analysis governance platform for the collection,transmission,storage,cleansing,transformation,querying and analysis,data development,publishing,and subscription,sharing and exchange,management,and services of massive data.These platforms serve various role members who have internal and external data needs.However,in the era of big data,the rapid update and iteration of big data technologies,the diversification of data businesses,and the exponential growth of data present more challenges and uncertainties to the construction of big data governance platforms.This paper discusses how to effectively build a data governance platform under the big data system from the perspectives of functional architecture,logical architecture,data architecture,and functional design.
文摘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.
文摘at present,data security has become the most urgent and primary issue in the era of digital economy.Marine scientific data security is the most urgent core issue of marine data resource management and sharing service.This paper focuses on the analysis of the needs of marine scientific data security governance,in-depth development of marine scientific data security governance approaches and methods,and practical application in the national marine scientific data center,optimizing the existing data management model,ensuring the safety of marine scientific data,and fully releasing the data value.
文摘With the rapid evolution of artificial intelligence(AI)technologies,the medical industry is undergoing a profound transformation driven by data intelligence.As the foundational element for intelligent diagnosis,precision prevention,and public health governance,medical data is characterized by massive volume,complex structure,diverse sources,high dimensionality,strong privacy,and high timeliness.Traditional data analysis methods are no longer sufficient to meet the comprehensive requirements of data security,intelligent processing,and decision support.Through techniques such as machine learning,deep learning,natural language processing,and multimodal fusion,AI provides robust technical support for medical data cleaning,governance,mining,and application.At the data level,intelligent algorithms enable the standardization,structuring,and interoperability of medical data,promoting information sharing across medical systems.At the model level,AI supports auxiliary diagnosis and precision treatment through image recognition,medical record analysis,and knowledge graph construction.At the system level,intelligent decision-support platforms continuously enhance the efficiency and accuracy of healthcare services.However,the widespread adoption of AI in medicine still faces challenges such as privacy protection,data security,model interpretability,and the lack of unified industry standards.Based on a systematic review of AI’s key supporting technologies in medical data processing and application,this paper focuses on the compliance challenges and adaptation strategies during industry integration and proposes an adaptation framework centered on“technological trustworthiness,data security,and industry collaboration.”The study provides theoretical and practical insights for promoting the standardized and sustainable development of AI in the healthcare industry.
基金supported by the following grants:National Natural Science Foundation of China(Grant No.72271147)Natural Science Foundation of Guangdong Province(Grant No.2022A1515010573)+1 种基金STU Scientific Research Initiation(Grant No.STF21005,STF21004)the Philosophy and Social Science Planning Project of Guangdong Province(Grant No.GD21CGL12,GD23XYJ76).
文摘Purpose:This research endeavors to investigate the impact of open government data on corporate investment,emphasizing the exploration of underlying mechanisms,heterogeneous effects,and implications for investment efficiency.Utilizing the implementation of government data open platforms as a quasi-natural experiment,this study aims to elucidate how public data transparency affects firms’investment decisions and resource allocation.Design/methodology/approach:This study employs a staggered Difference-in-Differences(DID)model as its principal methodological framework.This approach facilitates causal inference by examining the differential changes in corporate investment between firms influenced by the data openness policy and those that remain unaffected over time.Findings:The findings indicate that open government data substantially enhance corporate investment levels.A mechanistic analysis identifies three principal channels through which this effect is mediated:alleviation of overall financing constraints,reduction of financing costs,and expansion of the financing scale.A heterogeneity analysis suggests that the positive impact is more pronounced in state-owned enterprises,high-tech firms,and companies experiencing elevated levels of macroeconomic uncertainty.Moreover,the transparency of government data improves the responsiveness of corporate investment to emerging opportunities,thereby augmenting the overall efficiency of corporate investment.Research limitations:This study primarily examined the influence of government data transparency on corporate investment,while not accounting for the effects of macroeconomic variability,internal corporate governance frameworks,and industry-specific regulatory policies.Practical implications:Government open data platforms can effectively boost corporate investment and resource allocation.Policymakers should focus on improving the quality and accessibility of these data,especially in areas with high economic uncertainty,to support business investments.Firms,particularly high-tech and financially constrained firms,can use open data to ease capital limitations and find investment opportunities.Regulators should promote data transparency to enhance economic vitality through efficient corporate investments.Originality/value:This study enhances the existing literature by offering causal evidence of the impact of open government data on corporate investment,a subject that has been relatively underexplored empirically.By employing a quasi-natural experiment centered on the implementation of government data platforms,this study adopts a robust methodological approach to address endogeneity issues,thereby advancing methodological rigor in investigations of public data governance and corporate behavior.
文摘Purpose: This paper aims to assess if the extent of openness and the coverage of data sets released by European governments have a significant impact on citizen trust in public institutions.Design/methodology/approach: Data for openness and coverage have been collected from the Open Data Inventory 2018(ODIN), by Open Data Watch;institutional trust is built up as a formative construct based on the European Social Survey(ESS), Round 9. The relations between the open government data features and trust have been tested on the basis of structural equation modelling(SEM).Findings: The paper reveals that as European governments improve data openness, disaggregation, and time coverage, people tend to trust them more. However, the size of the effect is still small and, comparatively, data coverage effect on citizens' confidence is more than twice than the impact of openness.Research limitations: This paper analyzes the causal effect of Open Government Data(OGD) features captured in a certain moment of time. In upcoming years, as OGD is implemented and a more consistent effect on people is expected, time series analysis will provide with a deeper insight.Practical implications: Public officers should continue working in the development of a technological framework that contributes to make OGD truly open. They should improve the added value of the increasing amount of open data currently available in order to boost internal and external innovations valuable both for public agencies and citizens.Originality/value: In a field of knowledge with little quantitative empirical evidence, this paper provides updated support for the positive effect of OGD strategies and it also points out areas of improvement in terms of the value that citizens can get from OGD coverage and openness.
文摘Data Integrity is a critical component of Data lifecycle management. Its importance increases even more in a complex and dynamic landscape. Actions like unauthorized access, unauthorized modifications, data manipulations, audit tampering, data backdating, data falsification, phishing and spoofing are no longer restricted to rogue individuals but in fact also prevalent in systematic organizations and states as well. Therefore, data security requires strong data integrity measures and associated technical controls in place. Without proper customized framework in place, organizations are prone to high risk of financial, reputational, revenue losses, bankruptcies, and legal penalties which we shall discuss further throughout this paper. We will also explore some of the improvised and innovative techniques in product development to better tackle the challenges and requirements of data security and integrity.
基金“Shandong Social Science Planning and Research Project”/“Shandong Academy of Social Sciences Innovation Project”(20BCXJ01)Shandong Provincial Major Technology Innovation Projection under Grant 2018CXGC0703.
文摘Joint prevention and control is a social organization model dealing with the governance of public health and security incidents.The governance models should have the features of multiple subjects co-governing and distributed cooperating.Their purposes are to solve and improve the governance efficiency of dealing with public health and security incidents at the executive level.However,there are still many deficiencies in the current data governance and collaborative governance of joint prevention and control systems,which are mainly reflected in incomplete data collection,unimpeded data sharing,inflexible collaborative cooperation,and inadequate collaborative supervision.Therefore,a new innovative governance model is urgently needed.Blockchain technology is suitable for implementing multiparty data sharing and cooperation,and at the same time,it supports penetrating supervision and management.This paper studies the blockchain model for joint governance of public health and security incidents.It focuses on the multiagent collaborative prevention and control governance model,which provides a new opportunity for model innovation in data governance and in cooperative governance.
基金supported by the EU H2020 Research and Innovation Program under the Marie Sklodowska-Curie Grant Agreement(Project-DEEP,Grant number:101109045)National Key R&D Program of China with Grant number 2018YFB1800804+2 种基金the National Natural Science Foundation of China(Nos.NSFC 61925105,and 62171257)Tsinghua University-China Mobile Communications Group Co.,Ltd,Joint Institutethe Fundamental Research Funds for the Central Universities,China(No.FRF-NP-20-03)。
文摘The increasing dependence on data highlights the need for a detailed understanding of its behavior,encompassing the challenges involved in processing and evaluating it.However,current research lacks a comprehensive structure for measuring the worth of data elements,hindering effective navigation of the changing digital environment.This paper aims to fill this research gap by introducing the innovative concept of“data components.”It proposes a graphtheoretic representation model that presents a clear mathematical definition and demonstrates the superiority of data components over traditional processing methods.Additionally,the paper introduces an information measurement model that provides a way to calculate the information entropy of data components and establish their increased informational value.The paper also assesses the value of information,suggesting a pricing mechanism based on its significance.In conclusion,this paper establishes a robust framework for understanding and quantifying the value of implicit information in data,laying the groundwork for future research and practical applications.
基金supported by the National Natural Science Foundation of China(Nos.62202126 and 62232005)the Natural Science Foundation Project of Heilongjiang Province of China(No.YQ2024F005).
文摘In the digital economy,data assets have come to be regarded as the new oil,underscoring their critical role in modern business models and decision-making processes.In response,the Chinese government has prioritized the formalization and management of data assets,introducing policies aimed at enhancing their value.Given the unique nature of data assets,characterized by the potential for both depreciation and appreciation,precise methods for assessing value changes and realizing the appreciation of data assets are urgently needed.Effective data governance techniques,including data cleaning,acquisition,and integration,are essential for maximizing the economic potential of data assets.Against this backdrop,this survey explores two key issues from a data governance perspective:the enhancement of data asset value and the quantification of its changes.It is structured around two primary dimensions:first,by examining data assets'inherent properties and quality indicators,and second,by utilizing an“on-demand evaluation”approach that assesses value of data assets in response to the performance of downstream machine learning models.By advancing understanding of these issues,this study seeks to optimize strategies for maximizing the economic impact of data assets through refined data governance practices.
文摘Company X is a provincial state-owned enterprise. After sufficient investigation and research, its financial personnel combined with the actual needs of the company, using EXCEL server, set up a scientific research project budget management system, so that the system can replace manual budget control work;At the same time, through the data association work with UFIDA and other software, after in-depth data governance, a data bridge is built to form logical basic big data, which realizes automatic writing of vouchers and real-time reading of data, and generates the required report forms with designed logical relationship, thus greatly improving the work efficiency of finance personnel and relevant management personnel.
基金2021 National Social Science Fund of China Major Project“Research on the Social Impact of Internet Platforms and Its Governance Path”(Project approval Number 21&ZD195)the“Research on Legal Issues of Anti-terrorism Legislation and Human Rights Protection”under Shandong Provincial Youth Innovation Team Development Program for Universities(Project Number 2022RW016)of the Shandong Provincial Department of Education in 2022。
文摘The processing of data such as personal information constitute the infrastructure of automated administration.Under the multi-legal requirements for personal information protection,data security,and algorithm governance,the legal regulation of such administrative activities should pursue the value goal of balancing the use and protection of personal information,promoting the efficient,safe,and orderly flow of personal information,and forming a standardized order for the use of personal information,and build a systematic public governance mechanism.From the perspective of reflective law,the personal information governance of automated administration shall promote its self-regulation under the stimulation of external regulation.Fundamental rights are a combination of dual reflective structures of the political system and the legal system,forming the constitutional basis for the self-regulation of automated administration.The personal information protection system and digital administrative law are external regulations from the legal system,forming the legal basis and providing automated administration with scenario-based and classified governance ideas.The government data scenario focuses on the standardized governance of personal information processing activities.It takes the principles of fair information practice as its basic framework and the necessity to perform statutory duties as its legal basis.The algorithmic decision-making scenario forms its framework system based on the principle of algorithmic due process,which clarifies the information subject’s right not to be subject to automated decision-making and the mechanism for exercising the rights.It establishes the information flow order in public algorithms based on the procedural law regulations of prior algorithm designs.
文摘Open data are currently a hot topic and are associated with realising ambitions such as a more transparent and efficient government,solving societal problems,and increasing economic value.To describe and monitor the state of open data in countries and organisations,several open data assessment frameworks were developed.Despite high scores in these assessment frameworks,the actual(re)use of open government data(OGD)fails to live up to its expectations.Our review of existing open data assessment frameworks reveals that these only cover parts of the open data ecosystem.We have developed a framework,which assesses open data supply,open data governance,and open data user characteristics holistically.This holistic open data framework assesses the maturity of the open data ecosystem and proves to be a useful tool to indicate which aspects of the open data ecosystem are successful and which aspects require attention.Our initial assessment in the Netherlands indicates that the traditional geographical data perform significantly better than non-geographical data,such as healthcare data.Therefore,open geographical data policies in the Netherlands may provide useful cues for other OGD strategies.
基金Jantien Stoter is funded by the H2020 European Research Council(ERC)under the European Union’s Horizon 2020 Research and Innovation Framework Programme[grant agreement No 677312 UMnD].
文摘National spatial data infrastructures are key to achieving the Digital Earth vision.In many cases,national datasets are integrated from local datasets created and maintained by municipalities.Examples are address,building and topographic information.Integration of local datasets may result in a dataset satisfying the needs of users of national datasets,but is it productive for those who create and maintain the data?This article presents a stakeholder analysis of the Basisregistratie Adressen en Gebouwen(BAG),a collection of base information about addresses and buildings in the Netherlands.The information is captured and maintained by municipalities and integrated into a national base register by Kadaster,the Cadastre,Land Registry and Mapping Agency of the Netherlands.The stakeholder analysis identifies organisations involved in the BAG governance framework,describes their interests,rights,ownerships and responsibilities in the BAG,and maps the relationships between them.Analysis results indicate that Kadaster and the municipalities have the highest relative importance in the governance framework of the BAG.The study reveals challenges of setting up a governance framework that maintains the delicate balance between the interests of all stakeholders.The results provide guidance for SDI role players setting up governance frameworks for national or global datasets.
