Challenges Of Big Data Management Research Articles

“Challenges Of Big Data Management”

“Challenges Of Big Data Management”

Unlocking the power and unveiling the challenges of big data management and analysis: insights from diverse domains

Unlocking the power and unveiling the challenges of big data management and analysis: insights from diverse domains

Emerging Concepts Using Blockchain and Big Data

Blockchain and big data are the emerging technologies that are on the highest agendas of the firms. These are significantly expected to transform the ways in which the business as well as the firm run. These are on the verge of increasing the expectation of the distributed ledgers, which would keep the firms away from struggling challenges. The concept of big data and Blockchain has been used up by various other concepts that would help secure and interpret the information. The ideal solutions offered by these technologies shall address the challenges of big data management as well as for analytics. In addition to that, Blockchain provides its own consensus method, which is the primary means to create an audit trail. This enables users to verify all transactions. The audit trail is a means of verifying the correctness and integrity of every transaction, regardless of who owns the asset. The Blockchain can also verify that different parties of a transaction are following an agreement and not breaking the agreement. Moreover, there have been continuous arguments in the concept of Blockchain at which the bitcoin is fundamental and there are several popular blockchain approaches developed which would deliver performance, security as well as privacy. Apart from this, the use of Blockchain plays a major role in adding an extra data layer for the big data analytics process. Big data is considered secure which cannot be further forged with the network architecture. The current paper shall be discussing the emerging concepts that are using Blockchain as well as big data.

Izazovi upravljanja podacima u Industriji 4.0

The paper was created by reviewing the literature and business practices, which include the challenges of big data management in Industry 4.0. 30 scientific papers by eminent authors were analyzed, which emphasize the importance of Big Data and propose the implementation of one of the data management methods. Significant results of improvement through the application of new technologies have been observed in all industries. The aim of this paper is to summarize the experience of the company and include solutions for the most efficient data management in business practice, starting from data collection and storage to making the most important decisions.

Big data management: implications of dynamic capabilities and data incubator

PurposeBig data management research and practice, however, have received enormous interest from academia and industry; the extant literature demonstrates that the authors have limited understanding and challenges in this knowledge-stream to fully capitalize with its potentials. One of the contemporary challenges is to accurately verify data veracity, and developing value from the verified data for an organization and its stakeholders. Consequently, the purpose of this paper is to develop insights on how the authors could strategically deal with the contemporary challenges in strategic management of big data, related to data veracity and data value.Design/methodology/approachThe inductive–constructivist approach is followed to develop insights at the intersection of dynamic capabilities theory and stakeholder relationship management concept, in order to strategically deal with the contemporary challenges in big data management, related to data veracity and data value.FindingsAt the intersection of dynamic capabilities theory and stakeholder relationship management concept, an implication is acknowledged, which has research and practical significance to strategically verify data source, its veracity and value. Following this implication, a framework of a data incubator is proposed to proactively develop insights on veracity and value of data. Empirical insights are also presented in this study to support this initial framework.Practical implicationsFor future research in strategic management of big data, academics will have contextual understanding on the particular interconnected and interdependent attributes from dynamic capabilities and stakeholder relationship management research streams to further enhance the understanding on big data management. For practice, these insights will be useful for executives to focus on specific attributes of the proposed data incubator to confirm data veracity and develop insights on how to design, deliver and evaluate stakeholder value based on the verified data.Originality/valueFollowing a synthesis at the intersection of dynamic capabilities theory and stakeholder relationship management concept, this study introduces a data incubator to meaningfully deal with the big data management challenges, related to veracity and value of data.

Role of big data management in enhancing big data decision-making capability and quality among Chinese firms: A dynamic capabilities view

This study examines the antecedents and influence of big data decision-making capabilities on decision-making quality among Chinese firms. We propose that such capabilities are influenced by big data management challenges such as leadership, talent management, technology, and organisational culture. By using primary data from 108 Chinese firms and utilising partial least squares, we tested the antecedents of big data decision-making capability and its impact on decision-making quality. Findings suggest that big data management challenges are the key antecedents of big data decision-making capability. Furthermore, the latter is vital for big data decision-making quality.

Big Data Management for Cloud-Enabled Geological Information Services

Cloud computing as a powerful technology of performing massive-scale and complex computing plays an important role in implementing geological information services. In the era of big data, data are being collected at an unprecedented scale. Therefore, to ensure successful data processing and analysis in cloud-enabled geological information services (CEGIS), we must address the challenging and time-demanding task of big data processing. This review starts by elaborating the system architecture and the requirements for big data management. This is followed by the analysis of the application requirements and technical challenges of big data management for CEGIS in China. This review also presents the application development opportunities and technical trends of big data management in CEGIS, including collection and preprocessing, storage and management, analysis and mining, parallel computing based cloud platform, and technology applications.

Big data management challenges in health research-a literature review.

Big data management for information centralization (i.e. making data of interest findable) and integration (i.e. making related data connectable) in health research is a defining challenge in biomedical informatics. While essential to create a foundation for knowledge discovery, optimized solutions to deliver high-quality and easy-to-use information resources are not thoroughly explored. In this review, we identify the gaps between current data management approaches and the need for new capacity to manage big data generated in advanced health research. Focusing on these unmet needs and well-recognized problems, we introduce state-of-the-art concepts, approaches and technologies for data management from computing academia and industry to explore improvement solutions. We explain the potential and significance of these advances for biomedical informatics. In addition, we discuss specific issues that have a great impact on technical solutions for developing the next generation of digital products (tools and data) to facilitate the raw-data-to-knowledge process in health research.

Perspective of Database Services for Managing Large-Scale Data on the Cloud: A Comparative Study

The influx of Big Data on the Internet has become a question for many businesses of how they can benefit from big data and how to use cloud computing to make it happen. The magnitude at which data is getting generated day by day is hard to believe and is beyond the scope of a human’s capability to view and analyze it and hence there is an imperative need for data management and analytical tools to leverage this big data. Companies require a fine blend of technologies to collect, analyze, visualize, and process large volume of data. Big Data initiatives are driving urgent demand for algorithms to process data, accentuating challenges around data security with minimal impact on existing systems. In this paper, we present many existing cloud storage systems and query processing techniques to process the large scale data on the cloud. The paper also explores the challenges of big data management on the cloud and related factors that encourage the research work in this field.

Big Data for Digital Government

“Big data” is one of the emerging and critical issues facing government in the digital age. This study first delineates the defining features of big data (volume, velocity, and variety) and proposes a big data typology that is suitable for the public sector. This study then examines the opportunities of big data in generating business analytics to promote better utilization of information and communication technology (ICT) resources and improved personalization of e-government services. Moreover, it discusses the big data management challenges in building appropriate governance structure, integrating diverse data sources, managing digital privacy and security risks, and acquiring big data talent and tools. An effective big data management strategy to address these challenges should develop a stakeholder-focused and performance-oriented governance structure and build capacity for data management and business analytics as well as leverage and prioritize big data assets for performance. In addition, this study illustrates the opportunities, challenges, and strategy for big service data in government with the E-housekeeper program in Taiwan. This brief case study offers insight into the implementation of big data for improving government information and services. This article concludes with the main findings and topics of future research in big data for public administration.