Designing and Implementing an Advanced Big Data Governance Platform

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.

College Basic Development Status Data Management System Based on Data Governance Framework

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.

Understanding the Importance of Effective Third-Party Risk Management on Data Governance

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.

Data Component:An Innovative Framework for Information Value Metrics in the Digital Economy

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.

Research on the Realization Path and Application of a Data Governance System Based on Data Architecture

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.

Data Integrity and Risk

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.

An Innovative Mechanism of Blockchain Technology on Joint Governance Model

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.

Government Data Openness and Coverage. How do They Affect Trust in European Countries?

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.

Spatial data trusts:an emerging governance framework for sharing spatial data

Data Trusts are an important emerging approach to enabling the much wider sharing of data from many different sources and for many different purposes,backed by the confidence of clear and unambiguous data governance.Data Trusts combine the technical infrastructure for sharing data with the governance framework of a legal trust.The concept of a data Trust applied specifically to spatial data offers significant opportunities for new and future applications,addressing some longstanding barriers to data sharing,such as location privacy and data sovereignty.This paper introduces and explores the concept of a‘spatial data Trust’by identifying and explaining the key functions and characteristics required to underpin a data Trust for spatial data.The work identifiesfive key features of spatial data Trusts that demand specific attention and connects these features to a history of relevant work in thefield,including spatial data infrastructures(SDIs),location privacy,and spatial data quality.The conclusions identify several key strands of research for the future development of this rapidly emerging framework for spatial data sharing.

A visual analysis approach for data imputation via multi-party tabular data correlation strategies

Data imputation is an essential pre-processing task for data governance,aimed at filling in incomplete data.However,conventional data imputation methods can only partly alleviate data incompleteness using isolated tabular data,and they fail to achieve the best balance between accuracy and eficiency.In this paper,we present a novel visual analysis approach for data imputation.We develop a multi-party tabular data association strategy that uses intelligent algorithms to identify similar columns and establish column correlations across multiple tables.Then,we perform the initial imputation of incomplete data using correlated data entries from other tables.Additionally,we develop a visual analysis system to refine data imputation candidates.Our interactive system combines the multi-party data imputation approach with expert knowledge,allowing for a better understanding of the relational structure of the data.This significantly enhances the accuracy and eficiency of data imputation,thereby enhancing the quality of data governance and the intrinsic value of data assets.Experimental validation and user surveys demonstrate that this method supports users in verifying and judging the associated columns and similar rows using theirdomain knowledge.

健康医疗大数据共享关键问题及对策

从数据共享的概念和分类出发,归纳我国健康医疗大数据共享现状和关键问题。由于法律法规、管理制度和技术标准的缺失,我国健康医疗大数据共享模式面临共享效率不高、共享范围有限、数据冗余分散、系统重复建设等多方面挑战。借鉴国内外健康医疗大数据共享的建设经验,提出完善法律法规、兼顾多方利益、注重数据治理、推行通用信息标准等建议。以专病数据库建设为例,阐明健康医疗数据共享对改善医疗质量和临床科研的重要意义,以期为医疗机构的数据共享建设提供参考。

Stakeholder analysis of the governance framework of a national SDI dataset-whose needs are met in the buildings and address register of the Netherlands?

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.

Construction of a blockchain-based intelligent platform for government data

The management and application of government data are the bases for the construction of a digital government.Building an intelligent platform for government data by using artificial intelligence,blockchain,and other technical means to achieve open sharing,development,and utilization of data is an important link to promote continuously the construction of a digital government and improve the level of government management and service efficiency.According to the current construction status of government data platforms and the application prospect of blockchain technology,this study proposes to build a blockchain-based intelligent platform for government data.In combination with the technical advantages of blockchain,this study investigates the theory and technical logic of building a blockchain-based intelligent platform for government data and then proposes the theoretical model,architecture,operation mechanism,and core technology of platform construction.This study explores sthe implementation path of building a blockchain-based intelligent platform for government data from five aspects,i.e.,promoting technology upgrading,improving top-level design,strict supervision and regulation,strengthening collaborative research and judgment,and improving technical support.

Open sharing ofgovernment data and enterprise decision making

Data is a key factor of production in the so-called"digital economy"era.Thus,it is important to promote government data opening and sharing to advance the high-quality development of a digital economy.The article first constructs an evolutionary game model of government data opening and sharing(with local governments and enterprises as game participants)by combining realistic scenarios and evolutionary game models.Then,it discusses the evolutionary stabilization strategies under different scenarios in a categorical manner.Finally,it uses MATLAB to conduct numerical simulations to verify the accuracy of the model and analyze the key influencing factors.Several results were obtained.(1)the optimal evolutionary path to promote government data opening and sharing is for enterprises to choose to"use data"and for local governments to choose the"positive sharing"strategy,and the enterprises'decision is the internal driver.(2)The value of data assets provided by local governments when applying the"positive sharing"strategy,the cost of data used by enterprises,and the data value conversion rate of enterprises are the key factors influencing the decisions of both parties.To promote open sharing and exploitation of government data,enterprises should enhance their independent innovation capabilities,while governments should enhance the value of data assets and continuously optimize their business environments.

A Theory of Digital Economy Development:An Examination of the Practice of Chinese Platforms

Grasping the development trends and patterns of the digital economy and promoting the integrated development of the digital and the real economy is a strategic choice for China's construction of new competitive advantages.Based on case studies of the growth of e-commerce,this paper develops five microeconomic propositions about the following issues:the microeconomic features of data factor that differ from traditional production factors;optimal decision-making for digital enterprises;“data+platform”architecture,data transaction governance,and digital infrastructure supply.We apply these theoretical propositions to enterprise innovative practices in different application scenarios such as driverless vehicles and manufacturing digitalization.This paper provides a systematic and consistent theoretical framework for analyzing platform business model innovation and accurately identifying issues of institutional construction that promote the integrated development of the digital and the real economy.

Implementation of the FAIR Data Principles for Exploratory Biomarker Data from Clinical Trials

The FAIR data guiding principles have been recently developed and widely adopted to improve the Findability,Accessibility,Interoperability,and Reuse of digital assets in the face of an exponential increase of data volume and complexity.The FAIR data principles have been formulated on a general level and the technological implementation of these principles remains up to the industries and organizations working on maximizing the value of their data.Here,we describe the data management and curation methodologies and best practices developed for FAIRification of clinical exploratory biomarker data collected from over 250 clinical studies.We discuss the data curation effort involved,the resulting output,and the business and scientific impact of our work.Finally,we propose prospective planning for FAIR data to optimize data management efforts and maximize data value.

Capacitating local governments for the digital earth vision:lessons learnt from the role of municipalities in the South African spatial data infrastructure

The Digital Earth vision foresees the availability and accessibility of geospatial information to achieve the goals of sustainable development,economic growth and social well-being.In the case of urban areas,upto-date geospatial information is essential for managing a city towards achieving these goals.The rapid shift from rural to urban areas globally puts pressure on local governments and they often struggle to find and organise the resources required to collect and maintain geospatial information that can help to address urban growth challenges.A spatial data infrastructure(SDI)can facilitate the availability and accessibility of geospatial information towards addressing national objectives,however,the involvement of local governments in an SDI can be a challenge.In this paper,we critique the role of municipalities against the backdrop of the developments of the South African SDI(SASDI)to date.The critique identifies five high-level shortcomings of the SASDI that have led to the limited participation of municipalities.Based on the shortcomings,we provide recommendations for capacitating municipalities through SASDI so that the Digital Earth vision can also be achieved for municipalities.These recommendations are aimed at involving the local sphere of government in a national SDI and are equally applicable to other countries.

How to assess the success of the open data ecosystem?

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.

Terminology for a FAIR Framework for the Virus Outbreak Data Network-Africa

The field of health data management poses unique challenges in relation to data ownership, the privacy of data subjects, and the reusability of data. The FAIR Guidelines have been developed to address these challenges. The Virus Outbreak Data Network(VODAN) architecture builds on these principles, using the European Union’s General Data Protection Regulation(GDPR) framework to ensure compliance with local data regulations, while using information knowledge management concepts to further improve data provenance and interoperability. In this article we provide an overview of the terminology used in the field of FAIR data management, with a specific focus on FAIR compliant health information management, as implemented in the VODAN architecture.

Lessons from a Marine Spatial Planning data management process for Ireland

This paper presents a framework containing ten components to deliver a data management process for the storage and management of data used for Marine Spatial Planning(MSP)in Ireland.The work includes a data process flow and a recommended solution architecture.The architecture includes a central data catalogue and a spatial storage system.The components of the process are presented to maximise the reuse potential of any dataset within an MSP context.The terms‘Suitability’and‘Readiness’in the MSP context are offered as both formal and considered assessments of data,as is the applicability of a data stewardship maturity matrix.How data contained in such a storage system can be published externally to potential consumers of these data is also explored.The process presents a means of managing data and metadata to ensure data lineage is optimised by carrying information about the origin of and the processing applied to the data;to evaluate the quality and relevance of geospatial datasets for use in MSP decisions in Ireland.The process was piloted in the National Marine Planning Framework for Ireland in the development of draft map products;feedback from the public consultation is ongoing and not presented.