Business Big Data Research Articles

Data-Driven Inventory Management Solution for Procurement and Supply Chain of Utility Company

An efficient inventory management solution in procurement and supply chain management (SCM) plays a vital role in ensuring the nominal working capital and increasing profitability in a large company like National Utility Company (NUC). However, in Malaysian NUC, the SCM process in utility goods warehouse is still done manually. This caused various materials administration and storage issues such as mismatch demand between supplier and customers, inefficient resource utilization and project delay. Therefore, this paper aims to review studies in data-driven solution, inventory management solution and proposes a new data-driven inventory management model based on the business intelligence framework. This study starts with a literature review analysis of supply chain and inventory management concept with current practice in the big data business solution era. This is followed by the development of data-driven inventory management model for procurement and supply chain. As a result, this new model corroborates five major insights for best practice including inventory visibility management, the healthy outlook in material scheduling, inventory classification and segmentation, demand forecasting and service level planning. This paper concludes that the manual warehouse management can be digitally transformed into data-driven inventory management by leveraging on the BI platform which able to provide more business insights to a large company similar to the utility company.

Big-data business models: A critical literature review and multiperspective research framework

The emergence of “big data” offers organizations unprecedented opportunities to gain and maintain competitive advantage. Trying to exploit the strategic business potential embedded in big data, many organizations have started to renovate their business models or develop new ones, giving rise to the phenomenon of big-data business models. Although big-data business model research is still in its infancy, a significant number of studies on the topic have been published since 2014. We thus suggest it is time to perform a critical review and assessment of the literature at the intersection of business models and big data (analytics), thereby responding to recent calls for further research on and sustained analysis of big-data business models. In particular, our review uses three major criteria (big-data business model types, dimensions, and deployment) to assess the state of the big-data business model literature and identify shortcomings in this literature. On this basis, we derive and discuss five central research perspectives (supply chain, stakeholder, ethics, national, and process), providing guidance for future research and theory development in the area. These perspectives also have practical implications on how to address the current big-data business model deployment gap.

Big data adoption in academic libraries: a literature review

PurposeThe purpose of this paper is to analyse the publication of big data in the library from Scopus database by looking at the writing time period of the papers, author's country, the most frequently occurring keywords, the article theme, the journal publisher and the group of keywords in the big data article. The methodology used in this study is a quantitative approach by extracting data from Scopus database publications with the keywords “big data” and “library” in May 2019. The collected data was analysed using Voxviewer software to show the keywords or terms. The results of the study stated that articles on big data have appeared since 2012 and are increasing in number every year. The big data authors are mostly from China and America. Keywords that often appear are based on the results of terminology visualization are including, “big data”, “libraries”, “library”, “data handling”, “data mining”, “university libraries”, “digital libraries”, “academic libraries”, “big data applications” and “data management”. It can be concluded that the number of publications related to big data in the library is still small; there are still many gaps that need to be researched on the topic. The results of the research can be used by libraries in using big data for the development of library innovation.Design/methodology/approachThe Scopus database was accessed on 24 May 2019 by using the keyword “big data” and “library” in the search box. The authors only include papers, which title contain of big data in library. There were 74 papers, however, 1 article was dropped because of it not meeting the criteria (affiliation and abstract were not available). The papers consist of journal articles, conference papers, book chapters, editorial and review. Then the data were extracted into excel and analysed as follows (by the year, by the author/s’s country, by the theme and by the publisher). Following that the collected data were analysed using VOX viewer software to see the relationship between big data terminology and library, terminology clustering, keywords that often appear, countries that publish big data, number of big data authors, year of publication and name of journals that publish big data and library articles (Alagu and Thanuskodi, 2019).FindingsIt can be concluded that the implementation of big data in libraries is still in an early stage, it is shown from the limited number of practical implementation of big data analytics in library. Not many libraries that use big data to support innovation and services since there were lack of librarian skills of big data analytics. The library manager’s view of big data is still not necessary to do. It is suggested for academic libraries to start their adoption of big data analytics to support library services especially research data. To do so, librarians can enhance their skills and knowledge by following some training in big data analytics or research data management. The information technology infrastructure also needs to be upgraded since big data need big IT capacity. Finally, the big data management policy should be made to ensure the implementation goes well.Originality/valueThis paper discovers the adoption and implementation of big data in library, many papers talk big data in business and technology context. This is offering new idea for many libraries especially academic library about the adoption of big data to support their services. They can adopt the big data analytics technology and technique that suitable for their library.

Employing big data in business organisation and business ethics

An ethical issues and distrust are pervasive in big data era. The largest data leaks in the social media's history were the controversy of Facebook data breach to unauthorised parties. Big data are useful for business to analyse large sets of heterogeneous data effectively. Because big data is seemingly accessible does not make it ethical for business in converting them into profits without users' concern. Big data needs ethical foundation that cannot be simplified just because data is accessible especially through social media. The objective of this study is to examine how the phenomenon of big data could affect the issues of ethics in business organisations. Then, it analyses the ethical challenges of big data in business for gain business value and competitive advantage. The study was a case study to analyse ethical challenges for organisation in deploying big data initiatives. This paper is basically a case study analysis. Qualitative approach was used to carry out this research where secondary data was retrieved from various credible journals and articles that are relevant to the issue discussed.

Employing Big Data in Business Organization and Business Ethics

An ethical issues and distrust are pervasive in big data era. The largest data leaks in the social media's history were the controversy of Facebook data breach to unauthorised parties. Big data are useful for business to analyse large sets of heterogeneous data effectively. Because big data is seemingly accessible does not make it ethical for business in converting them into profits without users' concern. Big data needs ethical foundation that cannot be simplified just because data is accessible especially through social media. The objective of this study is to examine how the phenomenon of big data could affect the issues of ethics in business organisations. Then, it analyses the ethical challenges of big data in business for gain business value and competitive advantage. The study was a case study to analyse ethical challenges for organisation in deploying big data initiatives. This paper is basically a case study analysis. Qualitative approach was used to carry out this research where secondary data was retrieved from various credible journals and articles that are relevant to the issue discussed.

Business Value of Telecom Operators’ Big Data

Big data is one of the infrequent resources in the 21st century, especially for Internet & Mobile Internet industries. Telecom operators have large amount of precious data. Through analysis of the features of telecom operators’ data, this study highlights the value of telecom big data in business and classifies it into two major categories. The first one is enhancing existing business to optimize traditional telecom services, like customer experience, telecom product design and network management. The other is developing derivative business such as advertisement, marketing, financial or credit services based on the strength of telecom big data. This preliminary research provides insights into the value development of telecom operators’ data on account of targeted discussion about practical application for telecom big data.

A Service-Oriented Foundation for Big Data

This paper provides a service-oriented foundation for big data. The foundation has two parts. Part 1 reveals 10 big characteristics of big data. Part 2 presents a service-oriented framework for big data. The framework has fundamental, technological, and socio-economic levels. The fundamental level has four big fundamental characteristics of big data: big volume, big velocity, big variety, and big veracity. The technological level consists of three big technological characteristics of big data: Big intelligence, big analytics, big infrastructure. The socioeconomic level has three big socioeconomic characteristics of big data: big service, big value, and big market. The article looks at each level of the proposed framework from a service-oriented perspective. The multi-level framework will help organizations and researchers understand how the 10 big characteristics relate to big opportunities, big challenges, and big impacts arising from big data. The proposed approach in this paper might facilitate the research and development of big data, big data analytics, business intelligence, and business analytics.

Research progress on big data security technology

As a new and energetic realm of economic development, an innovative engine of social development, and a strategic tool for shaping national competitiveness, big data significantly effects peoples lives. However, improved social awareness of data value and vigorous development of big data platforms mean that big data security is increasingly hindering the promotion of big data applications. Meanwhile, as big data technology and framework continue to evolve, researchers still have different understandings of the core ideas and key features of big data security, and a unified big data security framework has yet to be established. Currently, determining the state-of-the-art of big data security technology is urgently needed to provide reference for research aimed at solving key big data security issues. Following a typical big data system technology framework, this review builds a novel big data security technology framework around big data security requirements. With this framework, state-of-the-art key big data security technologies are systematically summarized from three aspects: big data secure sharing and trusted services, big data platform security, and big data security supervision, which includes the main security mechanisms involved in big data business processes and system technology frameworks. Finally, big data security technologys core issues and development trends are summarized.

Call for Special Issue Papers: Big Data in Business.

Call for Special Issue Papers: Big Data in Business.

Call for Special Issue Papers: Big Data in Business.

Big DataVol. 8, No. 1 Call for PapersFree AccessCall for Special Issue Papers: Big Data in BusinessDeadline for Manuscript Submission: March 20, 2020Guest Editor: Haitham NobaneeGuest Editor: Haitham NobaneeThe University of Oxford, The University of Liverpool, Abu Dhabi UniversitySearch for more papers by this authorPublished Online:14 Feb 2020https://doi.org/10.1089/big.2019.29032.cfp2AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail Big data is a fast-growing field that describes and analyzes huge amounts of structured, semi-structured, or unstructured data that comes from various sources that are vast and complex. It requires advanced analytics applications and sophisticated data-processing software. Big data has revolutionized the way businesses and institutions operate. Business entities from several sizes and sectors are benefiting from big data applications. Business entities revealed such benefits could improve efficiency and increase profitability and growth. Many business entities have several plans for using and implementing big data tools to improve operations, customer satisfaction and loyalty, enhancing corporate governance practices, fighting against fraud, cyber-attacks, money laundry, and other financial crimes. Big data has many other applications in business. This special issue is devoted to publishing high-quality papers that involve theoretical and practical aspects related to big data in business. Reviews on this topic are also welcome.Topics of interest for this special issue include, but are not limited to:The use of big data in business, marketing, finance, and accountingBig data and machine learning in business decision-making and performance evaluationBig data and enterprise risk managementBig data and corporate governanceBig data in marketing, promotions, and social networkingBig data in supply chain managementBig data and predictive algorithms in business, marketing, finance, and accountingBig data and business ethicsBig data and investment decisionsThe use of big data in financial institutions and marketsThe uses of big data in anti-financial crimes such as fraud and money laundry detection.Big data and online shopping.Monetizing big data for dynamic advertisementBig data and customer engagementBig data, machine-to-machine (M2M) analytics to improve product life-cycle managementBig data, automation, and internal efficiencyThe use of big data to compete, capture, innovate, and improve competitive advantageThe use of big data in creating new revenue streamsBig data and credit analysisBig data and insuranceBig data, business lending, and FinTechAll manuscripts should be submitted via the online submission system of Big Data. The Instructions for Authors can be found at www.liebertpub.com/big. The reviewing and selection of papers will be carried out in accordance with the standards of the journal.Submit your paper for peer review online: https://mc.manuscriptcentral.com/bigFiguresReferencesRelatedDetails Volume 8Issue 1Feb 2020 InformationCopyright 2020, Mary Ann Liebert, Inc., publishersTo cite this article:Guest Editor: Haitham Nobanee.Call for Special Issue Papers: Big Data in Business.Big Data.Feb 2020.1-1.http://doi.org/10.1089/big.2019.29032.cfp2Published in Volume: 8 Issue 1: February 14, 2020PDF download

RETRACTED ARTICLE: The big data visualization technology based ecosystem cycle on high speed network

Big Data is becoming a key strategy in the business sector, with the increasing number of corporate customer data tracking and collection practices, and the proliferation of multimedia content, such as the proliferation of multimedia and camera modules, and the proliferation of multimedia content. In this paper, we proposed a Big Data ecosystem cycle to create the data utilization strategies and analytic manpower for the success of data utilization with the data uptake. Big Data growth and investment with visualizations are critical to creating a significant role in the development of various Big Data business models. First of all, it is necessary to improve the data for innovation (Releasing Data for Innovation) and data visualization, to minimize the risk of privacy, and to mitigate the risk of privacy leaks to facilitate the promotion of Big Data utilization. It needs to comply with the standardization of Big Data requirements based on ISO/IEC geurigo 1 SC JTC data, and it has necessary to identify the scale of the existing architectures for the sake of accommodating and supporting the architecture that embrace the benefits of legacy architectures.

Re-Allocation of Taxing Rights for Big Data Business Models

The ongoing digitalisation of the economy challenges the international tax system. Specifically, the current rules of source taxation do not fully reflect the paradigm of taxing where value is created. In digital business models, firms often do not need a physical presence to be economically active in a given jurisdiction and, thus, avoid being taxable in this jurisdiction. In this paper, we make three distinct contributions. First, we review all major digital trends and business models for their scope to create value in a given jurisdiction without a physical presence there. Second, we present a concept that integrates non-physical value creation into the current system. The concept is based on sustained user relationships (SURE) that may be operated from abroad and exploited for data collection, advertising and platform services. Third, we discuss the operational aspects of implementing the concept, both in economic and legal terms: nexus rules and profit allocation rules. We conclude that that SURE concept may have important advantages over its main alternatives: Other than proposals based on digital user contributions, it does not require ring-fencing of a small segment of the digital economy. And in contrast to the concept of “marketing intangibles” (and more radical concepts like DBCFT or CCCTB), it does not advocate a general shift towards taxation in the market jurisdiction, which is difficult to justify under the logic of the current system of source taxation.

Stochastic Configuration Networks Based Adaptive Storage Replica Management for Power Big Data Processing

In the power industry, processing business big data from geographically distributed locations, such as online line-loss analysis, has emerged as an important application. How to achieve highly efficient big data storage to meet the requirements of low latency processing applications is quite challenging. In this paper, we propose a novel adaptive power storage replica management system, named PARMS, based on stochastic configuration networks (SCNs), in which the network traffic and the data center (DC) geodistribution are taken into consideration to improve data real-time processing. First, as a fast learning model with less computation burden and sound prediction performance, the SCN model is employed to estimate the traffic state of power data networks. Then, a series of data replica management algorithms is proposed to lower the effects of limited bandwidths and a fixed underlying infrastructure. Finally, the proposed PARMS is implemented using data-parallel computing frameworks (DCFs) for the power industry. Experiments are carried out in an electric power corporation of 230 million users, China Southern power grid, and the results show that our proposed solution can deal with power big data storage efficiently and the job completion times across geodistributed DCs are reduced by 12.19% on average.

BDBA(Big Data Business Analytics) 시스템과 SCM 관계에 관한 연구

BDBA(Big Data Business Analytics) 시스템과 SCM 관계에 관한 연구

To Be or Not to Be? Big Data Business Investment Decision-Making in the Supply Chain

The development of Big Data technology initiates an emerging research question of whether and how to invest in Big Data business for supply chain members to establish sustainable competitive edge. The aim of our study was to assess investment in Big Data business and its sustainable effects on supply chain coordination. We considered a two-stage supply chain with one supplier and one retailer who may or may not invest in Big Data business. Five decision-making modes were proposed based on the investment portfolios. The impacts of Big Data business on the profit of the supply chain and its members were analyzed and it was confirmed that a coordination scheme could achieve supply chain coordination. The results indicated that when the Big Data cost met a certain threshold, the profit of the supply chain and its members would increase whether supply chain members choose to invest in Big Data business individually or jointly. A reasonable cost allocation of Big Data business between supply chain members was provided when both members invest in Big Data. In addition, after the members invested jointly, a revenue-sharing contract could be applied to perfectly coordinate the supply chain.

Performance evaluation sql-on-hadoop: a case study of Hortonworks and Cloudera

Currently companies in the world have focused on the Big Data business which has become an invaluable tool in assisting business processes and data analysis. SQL-on-Hadoop is a small part of the Big Data Platform that has been developed to date. Our research implements Big Data Platform on Cloudera and Hortonworks using TPC-H Benchmarks on SQL-on-Hadoop systems and evaluates the characteristics and performance of query processing machines in each scenario applied to each platform. We focuses on evaluating the two Big Data Platforms, Cloudera and Hortonworks, to determine the advantages and disadvantages of each platform based on the TPC-H Benchmark that has been recognized as a Decision Support System to compare the two with four different scenario that run on the same configurations. The results obtained are Cloudera with Impala can process queries with a ratio of up to 41x faster than LLAP-Tez and 200x faster than Hive-Spark.

Big-Data Business Models: A Critical Literature Review and Multi-Perspective Research Framework

The emergence of ‘big data’ offers organizations unprecedented opportunities to gain and maintain competitive advantage. Trying to exploit the strategic business potential embedded in big data, many organizations have started to renovate their business models or develop new ones, giving rise to the phenomenon of big-data business models (BDBMs). Although BDBM research is still in its infancy, a significant number of studies on the topic have been published since 2014. We thus suggest it is time to perform a critical review and assessment of the literature at the intersection of business models and big data (analytics), thereby responding to recent calls for further research on and sustained analysis of BDBMs. In particular, our review uses three major criteria (BDBM types, dimensions, and deployment) to assess the state of the BDBM literature and identify shortcomings in this literature. On this basis, we derive and discuss five central research perspectives (supply chain, stakeholder, ethics, national, and process), providing guidance for future research and theory development in the area. These perspectives also have practical implications on how to address the current BDBM deployment gap.

Research on information security and privacy protection model based on consumer behavior in big data environment

SummaryIn the big data era, information security is becoming more and more important. The recent scandal of the data privacy on Facebook sounded the warning bell for us. Based on the utility theory, this paper proposes that the disclosure behavior of consumer personal information is mainly influenced by two factors, which are perceived risk and perceived benefit, respectively. When consumers make decisions, they weigh the risks and benefits. If the income is greater than the risk, the consumer is willing to provide the information on the website and vice versa. This paper analyzes the serious threats to the safety and privacy of commercial big data and proposes a multi‐level security protection theoretical model for business information security and privacy protection from the perspective of business big data security. The security functions and core technologies of each layer are described in detail, which provide a useful and in‐depth theoretical exploration for practical engineering. Finally, based on the privacy protection of large data and the principle of RBAC, a model of user privacy information security system is proposed.

Technology in the 21st century: New challenges and opportunities

Although big data, big data analytics (BDA) and business intelligence have attracted growing attention of both academics and practitioners, a lack of clarity persists about how BDA has been applied in business and management domains. In reflecting on Professor Ayre's contributions, we want to extend his ideas on technological change by incorporating the discourses around big data, BDA and business intelligence. With this in mind, we integrate the burgeoning but disjointed streams of research on big data, BDA and business intelligence to develop unified frameworks. Our review takes on both technical and managerial perspectives to explore the complex nature of big data, techniques in big data analytics and utilisation of big data in business and management community. The advanced analytics techniques appear pivotal in bridging big data and business intelligence. The study of advanced analytics techniques and their applications in big data analytics led to identification of promising avenues for future research.

Digital Business: The Transition into New Age Dealing; Emphasizing Inputs from Cloud Computing and Big Data—An Indian Case

<p class="lego-element">Business in today’s age changing rapidly, there are many reasons for this kind of transition of business houses and corporate bodies. Moreover, people in today’s age are using more online and digital means for the business and thus overall business systems are emphasize digital sources of revenue as well as digital channels. The concept of creation and dependencies of tools and technologies for financial economics systems is called Digital Economy and the growth of this leads to the development and familiarity with digital products and services among the service seekers and these ultimately are resulted in newer and competitive advantages in the digital space and system. Digital technology and its nature is also changing and has also challenged the existing business models. Here the key driving forces are included the capacity of innovation as well as the public availability of IT in different way. Digital business today is not a concept but it is about that stage of IT and Computing applications in different models viz. in marketing, ICT, social, business houses, healthcare, education and so on. Importantly these models may be seen as the convergence of technologies/ fully connected/integrated stage. In today’s age business is not only seen with computing and digital means but the traditional business systems also getting benefits from the ICT and here among the emerging names, few important include cloud computing, the Internet of Things, mobile and Big Data. It is important to note that this paper emphasizes the recent applications and role of Cloud Computing and Big Data in Business with a case of India. Moreover different financial aspects have also been described and analysed.</p>