The importance of Big Data Analytics technology in business management
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Institute of Management of the Warsaw University of Life Sciences
Faculty of Social and Economic Sciences, Cardinal Stefan Wyszyński University in Warsaw
Department of Management, Organisation and Economics at the Faculty of Physical Education, Józef Piłsudski University of Physical Education in Warsaw
Faculty of the University of Social Sciences
These authors had equal contribution to this work
Publication date: 2023-10-31
Cybersecurity and Law 2023;10(2):270-282
Data processing, artificial intelligence and IoT technologies are on the rise. The role of data transfer security systems and databases, known as Big Data, is growing. The main cognitive aim of the publication is to identify the specific nature of Big Data management in an enterprise. The paper uses the bibliographic Elsevier and Springer Link databases, and the Scopus abstract database. The distribution of keywords, drawing attention to four main areas related to research directions, is indicated, i.e., Big Data and the related terms „human”, „IoT” and „machine learning”. The paper presents the specific nature of Big Data together with Kitchin and McArdle’s research, indicating the need for a taxonomic ordering of large databases. The precise nature of Big Data management, including the use of advanced analytical techniques enabling managerial decision-making, was identified. The development of Cyber Production Systems (CPS), based on BD, integrating the physical world of an enterprise with the digitisation of information as the concept of Digital Twins (DTs), was also indicated. CPS offer the opportunity to increase enterprise resilience through increased adaptability, robustness and efficiency. With DTs, manufacturing costs are reduced, the product life cycle is shortened, and production quality increases.
Basu J.R., Abdulrahman M.D., Yuvaraj M., Improving agility and resilience of automotive spares supply chain: The additive manufacturing enabled truck model, „Socio-Economic Planning Sciences” 2023, vol. 85.
Chamoso P., Rodriguez F. de la Prieta S., Bajo J., Classification of retinal vessels using a collaborative agent-based architecture, „AI Communications” 2018, vol. 31.
Chen X., He C., Chen Y., Xie Z., Internet of Things (IoT) – blockchain-enabled pharmaceutical supply chain resilience in the post-pandemic era, „Frontiers of Engineering Management” 2022, vol. 10, p. 82–95.
Dong Z. et al., Blockchained supply chain management based on IoT tracking and machine learning, „Journal Wireless Computer Network” 2022, vol. 127.
Gassmann O., Opening up the innovation process: towards an agenda, „R &D Management” 2006, no. 3.
Kitchin R., McArdle G., What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets, „Article in Big Data & Society” 2016, vol. 3, no. 1.
Le-Nguyen K., Dyerson R., Harindranath G., Exploring knowledge management software implementation from a knowing-in-practice perspective, „Information Systems Frontiers” 2018, vol. 20.
Li D.F., Liu P., Li K.W., Big Data and Intelligent Decisions: Introduction to the Special Issue, „Group Decision and Negotiation” 2021, vol. 30.
Mayer-Schonberger V., Cukier K., Big Data: A Revolution that will Change How We Live, Work and Think, London 2013.
Monostori L. et al., Cyber–physical systems in manufacturing, „CIRP Annals” 2016, vol. 65, no. 2.
Orenga-Roglá S., Chalmeta R., Methodology for the Implementation of Knowledge Management Systems 2.0, „Business & Information Systems Engineering” 2019, vol. 61.
Pizło W., A. Parzonko, Virtual Organizations and Trust [in:] Trust, Organizations and the Digital Economy. Theory and Practice, eds. J. Paliszkiewicz, K. Chen, New York 2022.
Prabhakaran V., Kulandasamy A., Integration of recurrent convolutional neural network and optimal encryption scheme for intrusion detection with secure data storage in the cloud, „Computational Intelligence” 2021, vol. 37.
Premkumar R., Sathya Priya S., Service Constraint NCBQ trust orient secure transmission with IoT devices for improved data security in cloud using blockchain, „Measurement: Sensors” 2022, vol. 24.
Rodič B., Industry 4.0 and the new simulation modelling paradigm, „Organizacija” 2017, vol. 50, no. 3.
Ruppert T., Abonyi J., Integration of real-time locating systems into digital twins, „Journal of Industrial Information Integration” 2020, vol. 20.
Sardar T.H., Ansari Z., Distributed Big Data Clustering using MapReduce-based Fuzzy C-Medoids, „Journal of Institution of Engineers (Indie). Ser. B” 2022, vol. 103.
Sbai I., Krichen S., A real-time Decision Support System for Big Data Analytic: A case of Dynamic Vehicle Routing Problems, „Procedia Computer Science” 2020, vol. 176.
Schmidt S., Oelsnitz von der D., Innovative business development: identifying and supporting future radical innovators, „Leadersh Educ Personal Interdiscip J” 2020, vol. 2.
Schmitt R. et al., Enhancing Resiliency in Production Facilities Through Cyber Physical Systems, Berlin 2017.
Snellman L.C., Virtual teams: Opportunities and challenges for e-leaders, „Procedia – Social and Behavioral Sciences” 2014, no. 110.
Sun J. et al., Text visualization for construction document information management, „Automation in Construction Volume” 2020, vol. 111.
Tao F., Qi Q., L. Wang, Nee A.Y.C., Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison, „Engineering” 2019, vol. 5.
Xie J., Chen C., Supply chain and logistics optimization management for international trading enterprises using IoT-based economic logistics model, „Oper Manag Res” 2022, vol. 15.
Zhang C., Chen Y., A review of research relevant to the emerging industry trends: industry 4.0, IoT, block chain, and business analytics, „Journal of Industrial Integration and Management” 2016, vol. 5, no. 1.
Zhuang C. et. al., Digital Twin-based Quality Management Method for the Assembly Process of Aerospace Products with the Grey-Markov Model and Apriori Algorithm, „Chinese Journal of Mechanical Engineering” 2022, vol. 35.
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