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Descriptive representation about transformation of company by using current technologies and tools for analytical processing and evaluation of diverse data

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Companies are currently producing and processing larger and larger amounts of data that they were not able to effectively process and subsequently use in the management of company processes in the past. There are several technologies and tools for the analytical processing and evaluation of diverse data, such as the Big Data technology. The Industry 4.0 concept (which is closely linked with the IoT) will bring an enormous growth of produced data into the company sphere. The information value of such data can signifi cantly affect managing and decision-making processes in a company. Here, we can see a synergy between man and technology where each influences the other. The purpose of this paper is to support the following statement: in the present business environment, we are facing the transformation of a company that, for efficient management and decision-making, needs: a) to capture and process all available data; b) to implement new tools into strategic decisions; and c) to integrate data through a single system. This article describes the possibilities of deploying the efficient use of new technologies (Big Data, Industry 4.0, and IoT) in management.
Wydawca
Rocznik
Strony
89--101
Opis fizyczny
Bibliogr. 29 poz., rys.
Twórcy
autor
  • University of Žilina, Faculty of Management Science and Informatics, Department of Management Theories, Univerzitná 8215/1, 010 26 Žilina, Slovak Republic
  • University of Žilina, Faculty of Management Science and Informatics, Department of Management Theories, Univerzitná 8215/1, 010 26 Žilina, Slovak Republic
autor
  • University of Žilina, Faculty of Management Science and Informatics, Department of Management Theories, Univerzitná 8215/1, 010 26 Žilina, Slovak Republic
Bibliografia
  • [1] Bezweek, S. and Egbu, C. (2010) Impact of Information Technology in Facilitating Communication and Collaboration in Libyan Public Sector Organisations, [Online], Available: http://usir.salford.ac.uk/12835/1/530.pdf [19 May 2017].
  • [2] Collett, S. (2012) Big Data majestátně vstupují do fi rem, Computerworld, 21 June, pp. 6–8.
  • [3] Datalan (2013) IT inovácie, o ktorých by ste mali vedieť, Datalan, 1 January, pp. 12–13.
  • [4] Davenport, T. and Dyché, J. (2013), Big Data in big companies, International institute for analytics, [Online], Available: http://docs.media.bitpipe.com/io_10x/io_102267/item_ 725049 /Big-Data-in-Big-Companies.pdf [21 May 2017].
  • [5] Drobný, M. (2012) Dátovo orientované rozhodovanie zvyšuje produktivitu firiem, Infoware, December, pp. 10–11.
  • [6] Garlasu, D., Sandulescu, V., Halcu, I., Neculoiu, G. and Grigoriu, O. (2013) A Big Data implementation based on Grid Computing – Conference: 11th RoEduNet International Conference – Networking in Education and Research, Sinaia, ROMANIA.
  • [7] Chander, PG., Randhakrishnan, T. and Shinghal, R. (2001) ‘Design issues in implementing a cooperative search among heterogeneous agents to aid information management’, AI Edam-Artifi cial Intelligence, vol. 15, no. 1, pp. 51–65.
  • [8] IBM (2015) Platforma IBM Big Data, [Online], Available: http://www-03.ibm.com/software/products/sk/category/SWP10 [20 May 2017].
  • [9] Jain, A. (2016) The 5 Vs of Big Data, [Online], Available: https://www.ibm.com/blogs/watson-health/the-5-vs-of-big-data/ [19 May 2017].
  • [10] Kabir, N. and Carayannis, E. (2013) ‘Big Data, Tacit Knowledge and Organizational Competitiveness’, Proceedings of the International Conference on Intellectual Capital Knowledge Management & Organizational Learning – Conference in George Washington University, Washington, pp. 220–227.
  • [11] Kim, W.C.H. and Mauborgne, R. (2012) Blue Ocean Strategy, Praha: Management Press.
  • [12] Koman, G., Holubčík, M. and Varmus, M. (2016) ‘Globalization aspects of creating cooperation in sport environment with support of Big Data;, 16th International Scientifi c Conference on Globalization and its Socio-Economic Consequences, Rajecké Teplice, Slovakia, pp. 2307–2314.
  • [13] Kubina, M., Varmus, M. and Kubinova, I. (2015) ‘Use of big data for competitive advantage of company’, Procedia Economics and Finance, vol. 26, pp. 561–565.
  • [14] Ma, XX. and Di, W. (2014) ‘Research on information security issues facing the era of big data’, Applied Mechanics and Materials, vol. 651–653, pp. 1913–1916.
  • [15] Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R. and Roxburgh, C. (2011) Big data: The next frontier for innovation, competition, and productivity, [Online], Available: http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/big-data-the-next-frontier-for-innovation [18 May 2017].
  • [16] Marr, B. (2014) Big Data: The 5 Vs Everyone Must Know, [Online], Available: https://www.linkedin.com/pulse/20140306073407-64875646-big-data-the-5-vs-everyone-must-know [19 May 2017].
  • [17] Meer, D. (2013) What Is ‘Big Data,’ Anyway?, [Online], Available https://www.forbes.com/sites/strategyand/2013/11/05/what-is-big-data-anyway/#1c763d021c76 [20 May 2017].
  • [18] Muntean, M. (2018) ‘Business Intelligence Issues for Sustainability Projects’, Sustainability, vol. 10(2), p. 335.
  • [19] Nemschoff, M. (2014) 7 Important Types of Big Data, [Online], Available: http://www.smartdatacollective.com/7-important-types-big-data/ [20 May 2017].
  • [20] Plant, R. (2014) Big Data Case Study: Tesco, [Online], Available: http://robertplantblog.com/wp-content/uploads/2014/Big-Data-Case-Study-Tesco.pdf [19 May 2017].
  • [21] Romero, J.M.P., Hallet, S.H. and Jude, S. (2017) ‘Leveraging Big Data Tools and Technologies: Addressing the Challenges of the Water Quality Sector’, Sustainability, vol. 9(12), p. 2160.
  • [22] Rouse, M. (2014) Semi-structured data, [Online], Available: http://whatis.techtarget.com/defi nition/semi-structured-data [19 May 2017].
  • [23] Savvas, A. (2014) „Big” fi rmy to s big daty vyhrály, [Online], Available: http://businessworld.cz/analyzy/big-fi rmy-to-s-big-daty-vyhraly-11472 [19 May 2017].
  • [24] Stanimirović, Z. and Mišković, S. (2014) Effi cient Mataheuristic Approaches for Eploration of Online Social Networks, [Online], Available: https://www.igi-global.com/gateway/chapter/85458#pnlRecommendationForm [19 May 2017].
  • [25] Štofková, J., Stríček, I. and Štofková, K. (2016) Data analysis in quality management of the network enterprise, [Online], Available: https://www.ingentaconnect.com/content/ rout/26by1f/2016/00000001/00000001/art00049 [19 May 2017].
  • [26] Wu, S.M., Chen, T., Wu, Y.J. and Lytras, M. (2018) ‘Smart Cities in Taiwan: A Perspective on Big Data Applications’, Sustainability, vol. 10(1), p. 106.
  • [27] Yildirim, A., Özdogan, C. and Watson, D. (2014), [Online], Available: https://www.igi-global.com/gateway/chapter/85450 [19 May 2017].
  • [28] Zhuge, H. (2002) ‘Knowledge fl ow management for distributed team software development’, Knowledge-Based Systems, vol. 15, No. 8, pp. 465–471.
  • [29] Zikopoulos, P., Eaton, C., Deroos, D., Deutsch, T. and Lapis, G. (2011) Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data, London: McGraw Hill Professional.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1c07bea3-a932-4368-87c1-bb778206e323
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