Big Data Governance Research Articles

Discussion on the Role of Big Data in Improving Public Sector Governance

In the current era, big data technology has deeply affected the operational mode of society. Big data has gradually become a strategic resource and social asset and continues to penetrate the specific affairs of the government. Big data governance has been promoted to a national strategic level by an increasing number of countries worldwide. Therefore, the study of big data and government management of modernization will help us to understand the basic characteristics of big data technology. and the connotation of government management of modernization, it will also help to accelerate the transformation of China's governance concepts, explore the impact of big data technology on China's Government Governance Paradigm Innovation, and promote the government's use of big data technology to improve its governance.

Progress and prospects in planning: A bibliometric review of literature in Urban Studies and Regional and Urban Planning, 1956–2022

The global population has rapidly urbanized over the past century, and the urbanization rate is projected to reach about 70% by 2050. In line with these trends and the increasing recognition of the significance of cities in addressing local and global challenges, a lot of research has been published on urban studies and planning since the middle of the twentieth century. While the number of publications has been rapidly increasing over the past decades, there is still a lack of studies analyzing the field's knowledge structure and its evolution. To fill this gap, this study analyzes data related to more than 100,000 articles indexed under the “Urban Studies” and “Regional & Urban Planning” subject categories of the Web of Science. We conduct various analyses such as term co-occurrence, co-citation, bibliographic coupling, and citation analysis to identify the key defining thematic areas of the field and examine how they have evolved. We also identify key authors, journals, references, and organizations that have contributed more to the field's development. The analysis is conducted over five periods: 1956–1975 (the genesis period), 1976–1995 (economic growth and environmentalism), 1996–2015 (sustainable development and technological innovation), 2016–2019 (climate change and SDGs), and 2020 onwards (post-COVID urbanism). Four major thematic areas are identified: 1) socio-economic issues and inequalities, 2) economic growth and innovation, 3) urban ecology and land use planning, and 4) urban policy and governance and sustainability. The first two are recurring themes over different periods, while the latter two have gained currency over the past 2–3 decades following global events and policy frameworks related to global challenges like sustainability and climate change. Following the COVID-19 pandemic, issues related to smart cities, big data analytics, urban resilience, and governance have received particular attention. We found disproportionate contributions to the field from the Global North. Some countries from the Global South with rapid urbanization rates are underrepresented, which may have implications for the future of urbanization. We conclude the study by highlighting thematic gaps and other critical issues that need to be addressed by urban scholars to accelerate the transition toward sustainable and resilient cities.

Big Data and Artificial Intelligence on the Blockchain: A Review

As organizations seek to derive greater value from rapidly expanding datasets, they face challenges related to big data governance including security, privacy, regulatory compliance, access controls, and building trust across entities. Blockchain has emerged as a promising technology for managing and analyzing big data in a controlled, transparent manner through its decentralized architecture and cryptographic foundations. This paper explores how blockchain can enable more secure sharing of big data that mitigates systemic impediments to wider application. We first examine unique properties of blockchain technology that allow distrusting parties to collaborate through immutable records, consensus protocols, smart contracts, and permissions management. We then profile real-world use cases in sectors like healthcare, supply chain logistics, and financial services where blockchain strengthens big data usage. Finally, current scaling and architectural limitations are discussed along with an outlook on the remaining technology gaps and skills needed to fully bridge blockchain, distributed data, advanced analytics, and machine learning. The paper ultimately argues blockchain may profoundly expand big data’s capabilities but still requires further innovation across hardware infrastructure, platforms, analytics interfaces, and organizational strategy.

The effect of big data governance on financial technology in Jordanian commercial banks: The mediation role of organizational culture

Big data has become much more widely used in recent years, particularly in the banking sector. Banks have begun to use big data to enhance customer experience, optimize operations, and create novel products and services. This study investigates the effect of big data governance on financial technology in Jordanian commercial banks. The study focuses on the four dimensions of big data governance: strategy and planning, ethics and social responsibility, data quality and management, and infrastructure and architecture. Additionally, the study considers the mediating role of organizational culture in the relationship between big data governance and financial technology. Primary data were collected from 250 IT employees working in Jordanian commercial banks, and the data were analyzed using PLS-SEM. The study findings indicate that big data governance has a significant positive effect on financial technology in Jordanian commercial banks. Additionally, it has been discovered that organizational culture partially mediates the relationship between big data governance and financial technology, emphasizing the significance of creating a culture that encourages the efficient use of data in fintech. In the context of Jordanian commercial banks, this study presents empirical evidence for the connection between big data governance and financial technology. The results indicate that big data governance measures should be applied while taking organizational culture into account as a potential mediator of the effects on financial technology. This study offers guidance on how to successfully integrate big data governance policies in commercial banks to advance financial technology for bank managers and policymakers.

Analysis on Data Governance Policy in the Health Sector of Zambia: Perspective of Big Data

The study explored the data governance policy in Zambia's health sector, focusing on big data. To fully realize the potential of data in our digital age, proper data governance is required. However, questions have been expressed concerning Zambia's health sector's ability to exploit data as a valuable resource due to the state of data governance. This study attempts to address this critical issue and shed light on the functions of big data governance aspects in the health sector. This work is significant because it has the potential to improve healthcare service quality, decision-making, and resource allocation through strong data governance. This study aims to bridge the gap between data governance and the requirements of big data in the Zambian health sector by reviewing existing policies, procedures, and practices. A mixed methods approach is used in the research, involving qualitative interviews with key informants and secondary data analysis. To get a full understanding of data governance difficulties, practices, and their alignment with big data demands, thematic and content analysis methodologies were used. The findings show that data governance is critical to managing data in the health sector, yet gaps in governance procedures impede the efficient implementation of ICT efforts. These concerns are exacerbated by challenges relating to competent human resources and ICT infrastructure. Accountability and transparency are also identified as areas for improvement. While this study offers insight on the existing state of affairs, it recognizes its limits and advocates for further research to investigate specific aspects of policy implementation. Nonetheless, this study contributes significantly to the field of big data and data governance in the Zambian health sector, encouraging stakeholders to work for an ICT driven, data-governed future that promotes openness, accountability, and improved healthcare results. Finally, this article emphasizes the importance of improving data governance procedures in Zambia's health sector, as well as the transformative potential of big data in healthcare as a catalyst for good change in the country's healthcare environment.

Exploring Mid-Market Strategies for Big Data Governance

Many data scientists struggle to adopt effective data governance practices as they transition from traditional data analysis to big data analytics. This qualitative multiple case study explored big data governance strategies used by data scientists employed in 3 mid-market companies in the greater Salt Lake City, Utah area who have strategies to govern big data. Data were collected via 10 semi-structured, in-depth, individual interviews and analysis of 4 organizational process documents. Four major themes emerged from the study: ensuring business centricity, striving for simplicity, establishing data source protocols, and designing for security. One key recommendation from the findings for data scientists is to minimize the data noise typically associated with big data. Implementing these strategies can help data scientists transition from traditional to big data analytics, which could help those organizations be more profitable by gaining competitive advantages. By implementing strategies relating to the segregation of duties, encryption of data, and personal information, data scientists can mitigate contemporary concerns relating to using private information in big data analytics.

Jump Filter: A Dynamic Sketch for Big Data Governance

PDF HTML XML Export Cite reminder Jump Filter: A Dynamic Sketch for Big Data Governance DOI: 10.21655/ijsi.1673-7288.00296 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:With the rapid development of information technology, the volume of data maintains exponential growth, and the value of data is hard to mine. This brings significant challenges to the efficient management and control of each link in the data life cycle, such as data collection, cleaning, storage, and sharing. Sketch uses a hash table/matrix/bit vector to track the core characteristics of data, such as frequency, cardinality, and membership. This mechanism makes the sketch itself metadata, which has been widely used in sharing, transmission, update, and other scenarios. The rapid flow characteristic of big data has spawned dynamic sketches. The existing dynamic sketches have the advantage of expanding or shrinking the capacity with the size of the data stream by dynamically maintaining a list of probabilistic data structures in a chain or tree structure. However, there are problems with the excessive space overhead and time overhead increasing with the increase in the dataset cardinality. This paper designs a dynamic sketch for big data governance on the basis of the advanced jump consistent hash. This method can simultaneously achieve the space overhead that grows linearly with the dataset cardinality and the constant time overhead of data processing and analysis, effectively supporting the demanding big data processing and analysis tasks for big data governance. The validity and efficiency of the proposed method are verified by the comparison with traditional methods on various synthetic and natural datasets. Reference Related Cited by

Preface to the Special Issue on Theories and Technologies for Big Data Governance

Preface to the Special Issue on Theories and Technologies for Big Data Governance

Research on Big Data Driven Innovation of Public Management Mode

Big data has the characteristics of large quantity, diversified data, low value density and fast speed. It provides unique and brand-new thinking through technology, and establishes our learning innovation, scientific and technological innovation and management innovation. With the arrival of the big data era, the modernization of government governance capacity has put forward realistic needs for big data, but there are still many specific directions worth in-depth study. In the future, government governance driven by big data should further clarify the concept and core value of big data from the perspective of government governance, focus on the research of big data application in specific governance areas, pay attention to the research of government big data platform construction, and deepen the technology driven research of big data on deepening government system reform. In the era of big data, for public managers, their information literacy will have a great impact on the actual work efficiency and significance. This paper analyzes the important role of information literacy from the perspective of the development of the times and the specific work needs of public managers. Today is an era of big data. Data sharing and data change the future. Nowadays, data is affecting our lives. With the popularization of the Internet and the development of network technology, an era of big data aggregation with huge quantity, unprecedented scale, and extremely fast generation and update speed contains unprecedented social and commercial value. The Internet has changed people's lives. At the same time, network information is also changing people's psychological activities. It is also necessary to focus on analyzing the difficulties of the current network public opinion guidance model, and put forward scientific suggestions on how to establish a sound public opinion guidance mechanism. It is hoped that the research in this paper can further optimize the existing public opinion guidance model and provide an important guarantee for stabilizing the network environment.

New Features, New Challenges and New Paths of Government Data Governance——Based on the background of digital government construction

The construction of digital government is an important tool to promote the modernization of government governance system and governance capacity, the rise and development of big data provides new ideas and methods. In this context, it is an inevitable trend to apply big data technology to various fields of government governance and to continuously optimize the government data governance system. By analyzing the characteristic factors of government data governance, this paper explores the problems and deficiencies of the government's big data governance system from three aspects: internal management, external environment and data itself, and puts forward decision-making suggestions for promoting the construction of digital government, and promotes the continuous optimization of the big data governance system and achieve high-quality economic and social development.

New Features, New Challenges and New Paths of Government Data Governance——Based on the background of digital government construction

The construction of digital government is an important tool to promote the modernization of government governance system and governance capacity, the rise and development of big data provides new ideas and methods. In this context, it is an inevitable trend to apply big data technology to various fields of government governance and to continuously optimize the government data governance system. By analyzing the characteristic factors of government data governance, this paper explores the problems and deficiencies of the government's big data governance system from three aspects: internal management, external environment and data itself, and puts forward decision-making suggestions for promoting the construction of digital government, and promotes the continuous optimization of the big data governance system and achieve high-quality economic and social development.

Big Data: An Analytical Approach - Strategy Implementation, Governance and Emergence

Due Big data is a term for an amalgamation of huge amount of varied and complex data which has the difficulty in analysing, storing and utilization. The process to perform research into this large collection of varied data collected from different data sources to get deeper analysis and insight is known as Big data analysis. With an ever growing technological landscape and digital societal changes by means of social media, IOT and other digital platform usage the need to access actionable insight and their corelation is required the most. If the capabilities are there, organisations can use the enormous datasets, but there are also many hurdles involved. Despite the potential benefits, a significant hurdle is to appropriately process and control the data. It requires thorough understanding of big data governance, adoption, implementation methodology and technological choices. The paper analysis the analytical methodologies, available technologies and tools required for the big data analytics. Big Data management's key features, problems, and difficulties have been examined. There have been discussions about multiple frameworks which perform analysis on different kinds of workloads , this include tools developed by open source community as well as OEM’s. The paper also explores its application in various domains in terms of today’s technological enhancements.

AI-powered information and Big Data: current regulations and ways forward in IFRS reporting

PurposeWith the help of “self-learning” algorithms and high computing power, companies are transforming Big Data into artificial intelligence (AI)-powered information and gaining economic benefits. AI-powered information and Big Data (simply data henceforth) have quickly become some of the most important strategic resources in the global economy. However, their value is not (yet) formally recognized in financial statements, which leads to a growing gap between book and market values and thus limited decision usefulness of the underlying financial statements. The objective of this paper is to identify ways in which the value of data can be reported to improve decision usefulness.Design/methodology/approachBased on the authors' experience as both long-term practitioners and theoretical accounting scholars, the authors conceptualize and draw up a potential data value chain and show the transformation from raw Big Data to business-relevant AI-powered information during its process.FindingsAnalyzing current International Financial Reporting Standards (IFRS) regulations and their applicability, the authors show that current regulations are insufficient to provide useful information on the value of data. Following this, the authors propose a Framework for AI-powered Information and Big Data (FAIIBD) Reporting. This framework also provides insights on the (good) governance of data with the purpose of increasing decision usefulness and connecting to existing frameworks even further. In the conclusion, the authors raise questions concerning this framework that may be worthy of discussion in the scholarly community.Research limitations/implicationsScholars and practitioners alike are invited to follow up on the conceptual framework from many perspectives.Practical implicationsThe framework can serve as a guide towards a better understanding of how to recognize and report AI-powered information and by that (a) limit the valuation gap between book and market value and (b) enhance decision usefulness of financial reporting.Originality/valueThis article proposes a conceptual framework in IFRS to regulators to better deal with the value of AI-powered information and improve the good governance of (Big)data.

Sustainable Insights for Energy Big Data Governance in China: Full Life Cycle Curation from the Ecosystem Perspective

With the development of the Energy Internet and the Internet of Things, diversified social production activities are making the interactions between energy, business, and information flow among physical, social, and information systems increasingly complex. As the carrier of information and the hub between physical and social systems, the effective management of energy big data has attracted the attention of scholars. This work indicates that China’s energy companies have carried out a series of activities that are centered on energy big data collection, as well as development and exchange, and that the energy big data ecosystem has begun to take shape. However, the research on and the application of energy big data are mainly limited to micro-level fields, and the development of energy big data in China remains disordered because the corresponding macro-level instructive governance frameworks are lacking. In this work, to facilitate the sustainable development of the energy big data ecosystem and to solve existing problems, such as the difficult-to-determine governance boundaries and the difficult-to-coordinate interests, and to analyze the structure and mechanism of the energy big data ecosystem, data curation is introduced into energy big data governance, and a paradigm is constructed for sustainable energy big data curation that encompasses its full life cycle, including the planning, integration, application, and maintenance stages. Key paradigmatic issues are analyzed in-depth, including data rights, fusion, security, and transactions.

ModelOps for enhanced decision-making and governance in emergency control rooms

For mission critical (MC) applications such as bushfire emergency management systems (EMS), understanding the current situation as a disaster unfolds is critical to saving lives, infrastructure and the environment. Incident control-room operators manage complex information and systems, especially with the emergence of Big Data. They are increasingly making decisions supported by artificial intelligence (AI) and machine learning (ML) tools for data analysis, prediction and decision-making. As the volume, speed and complexity of information increases due to more frequent fire events, greater availability of myriad IoT sensors, smart devices, satellite data and burgeoning use of social media, the advances in AI and ML that help to manage Big Data and support decision-making are increasingly perceived as “Black Box”. This paper aims to scope the requirements for bushfire EMS to improve Big Data management and governance of AI/ML. An analysis of ModelOps technology, used increasingly in the commercial sector, is undertaken to determine what components might be fit-for-purpose. The result is a novel set of ModelOps features, EMS requirements and an EMS-ModelOps framework that resolves more than 75% of issues whilst being sufficiently generic to apply to other types of mission-critical applications.

SmartAirQ: A Big Data Governance Framework for Urban Air Quality Management in Smart Cities

Rapid urbanization across the world has put an enormous burden on our environment. Cities from developing countries, in particular, are experiencing high air pollution levels. To address this challenge, the new WHO global air quality guidelines and various nations are mandating cities to implement clean air measures. However, these implementations are largely hindered by limited observations, siloed city operations, absence of standard processes, inadequate outreach, and absence of collaborative urban air quality management (UAQM) governance. The world is experiencing transformative changes in the way we live. The 4th industrial revolution technologies of artificial intelligence, Internet of Things, big data, and cloud computing bridge gaps between physical, natural, and personal entities. Globally, smart cities are being promulgated on the premise that technologies and data aid in improving urban services. However, in many instances, the smart city programs and UAQM services may not be aligned, thereby constraining the cumulative advantage in building urban resilience. Considering the potential of these technologies as enablers of environmental sustainability, a conceptual urban computing framework “SmartAirQ” for UAQM is designed. This interdisciplinary study outlines the SmartAirQ components: 1) data acquisition, 2) communication and aggregation, 3) data processing and management, 4) intelligence, 5) application service, 6) high-performance computing- (HPC-) cloud, and 7) security. The framework has integrated science cloud and urban services aiding in translating scientific data into operations. It is a step toward collaborative, data-driven, and sustainable smart cities.

Upgrading of IOT Big Data Governance Scheme in Microenterprise Governance

The accelerated development of the tertiary industry is the inevitable result of productivity improvement and social progress. Vigorously developing the tertiary industry is conducive to enhancing the stamina of agricultural production, promoting the socialization and professionalization of industrial and agricultural production, optimizing the production structure, promoting the full development of the market, and alleviating employment pressure so as to promote the sustained, rapid, and healthy development of the entire economy. Microenterprises are one of the carriers for the development of the tertiary industry. Microenterprises can effectively alleviate the country’s increasingly serious employment problems. However, with the saturated operation of microenterprises, the competition of microenterprises is becoming more and more intense. Small- and medium-sized enterprises are under the pressure of competition in terms of manpower, location, consumption level, and supply of goods. At the same time, small- and medium-sized enterprises are also facing problems such as high operating pressure, rising costs, financing difficulties, and heavy taxes and fees. In this environment, if small enterprises want to maintain long-term development, they must adapt to new forms and take active measures. Small- and medium-sized enterprises must use flexible and diverse forms of employment, actively adjust the industrial structure, vigorously develop the tertiary industry, and absorb more employed labor. The purpose of this paper is to study the use of IOT big data to optimize and upgrade the governance scheme of microenterprises. The purpose is to improve the governance effect of microenterprises and promote the development of microenterprises. This paper proposes optimal governance based on the collaborative algorithm and Pareto algorithm and conducts governance evaluation in multiple aspects of microenterprises, respectively. After obtaining the relationship with profit maximization, this paper finally analyzes and summarizes, obtains two optimal schemes, and evaluates and compares these two schemes with the traditional scheme. The results show that the profit maximization degree of the two schemes can reach 92%. Compared with the traditional scheme, the maximum profit of small enterprises after the implementation of governance has increased by about 30%.

Scenario modeling for government big data governance decision-making: Chinese experience with public safety services

In the public safety service context, government big data governance (GBDG) is a challenging decision-making problem that encompasses uncertainties in the arenas of big data and its complex links. Modeling and collaborating the key scenario information required for GBDG decision-making can minimize system uncertainties. However, existing scenario-building methods are limited by their rigidity as they are employed in various application contexts and the associated high costs of modeling. In this paper, using a design science paradigm, a model-driven scenario modeling approach is proposed to achieve flexible scenario modeling for various applications through the transfer of generic domain knowledge. The key component of the proposed approach is a scenario meta-model that is built from existing literatures and practices by integrating qualitative, quantitative, and meta-modeling analysis. An instantiation mechanism of the scenario meta-model is also proposed to generate customized scenarios under Antecedent-Behavior-Consequence (ABC) theory. Two real-world safety service cases in Wuhan, China were evaluated to find that the proposed approach reduces GBDG decision-making uncertainties significantly by providing key information for GBDG problem identification, solution design, and solution value perception. This scenario-building approach can be further used to develop other GBDG systems for public safety services with reduced uncertainties and complete decision-making functions.

Use of big data governance in several corporate sectors

Big data is rapidly in use by different organizations. It is another step towards technological advancement wsith tremendous benefits to almost all fields. Organizations need big data to find out the needs and priorities of the customers and to make strategies. A large amount of data is called big data. Big data is complex data that is not so easy to manage for that purpose big data governance is developed. It manages big data in all possible ways. The important information required is separated through big data governance techniques. This research is based on the importance of big data governance which is provided by digging out the big data governance frameworks, tools, and techniques for carrying out the data governance. There are many challenges and opportunities related to big data governance that are also mentioned in the paper. A brief discussion on the best practices of big data governance is provided along with a case study of the national pension service of South Korea. The report ends with a detailed conclusion based on the finding of the paper with some future insight.

The three legal dimensions of China’s big data governance

Big data governance has emerged as an important area of focus in recent times. In this article, we study the state of big data governance in China, discuss the balance of interests in big data, and seek to develop the theory of big data governance in law. In the past years, some academic achievements have been made in big data governance theories, personal data protection, and data property rights protection based on existing research related to big data governance. However, conflicts of interest in big data have been under-studied and under-analyzed. In this article, we conduct an in-depth study of the state of big data governance in China and develop a theoretical framework for constructing multi-subject governance based on the interest balancing approach, including the allocation of personal data rights and the rights of a data controller, to resolve the conflicts of interests in big data governance in the areas of private law, public law, and industry self-governance.