Posudzovanie pripravenosti podniku na nasadenie veledát (big data)
Klíčová slova:
Big data, model, investovanie, implementácia veledátAbstrakt
V dnešnej dobe môže využívanie veledát poskytnúť firmám konkurenčnú výhodu tým, že lepšie pochopia potreby zákazníkov a zlepšia svoje podnikové procesy. Na druhej strane implementácia technológií veledát nie je ľahká a vyžaduje si veľa významných zmien v podnikových procesoch, organizačných zmien, nasadenie nových zariadení a kvalifikovaných zamestnancov. Z týchto dôvodov je na zodpovednosti manažérov kvalifikovane posúdiť pripravenosť a vhodnosť podmienok v podniku a na základe toho sa rozhodnúť o vhodnom čase a spôsobe implementácie veledát. To však nie je možné, pokiaľ manažéri nemôžu exaktne posúdiť pripravenosť podniku na nasadenie technológie veledát. Pre kvalifikované rozhodnutie je potrebné vziať do úvahy rôzne kritériá, ktorých zhodnotenie vyžaduje spoluprácu vysoko kvalifikovaných expertov, ako aj dostupnosť zdrojov na implementáciu inovácie. Dôsledkom náročnosti posudzovania pripravenosti na nasadenie technológie veledát je skutočnosť, že tieto sa najskôr začali implementovať do veľkých podnikov, v ktorých je okrem vhodných podmienok k dispozícii aj dostatočný kapitál na investovanie do inovácií. Iná situácia je v menších podnikoch, ktoré zväčša nemajú dostatok interných expertov, ani kapitál na prípravu kvalifikovaných rozhodnutí o investícii do technológie veledát. Preto bol na Katedre Aplikovanej informatiky navrhnutý model posudzovania pripravenosti podniku na nasadenie veledát a tento bol implementovaný do diagnostického nástroja podporujúceho iniciačné rozhodnutie o akceptácii technológie veledát, čo pomáha manažérom objektívne posúdiť pripravenosť podniku na implementáciu veledát.Reference
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Ramezani, J., & Nasrollahi, M. (2020). A Model to Evaluate the Organizational Readiness for Big Data Adoption. International Journal of Computers Communications & Control, 15. doi:10.15837/ijccc.2020.3.3874
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Shahzad, K. (2018). Analysis of Influencing Factors of Big Data Adoption in Chinese Enterprises Using DANP Technique. Sustainability, 10. doi:10.3390/su10113956
Soon, K. W. K., Lee, C. A., & Boursier, P. (2016). A chronology of big data adoption: Review of literature. I J A B E R, 14(1), 521-544.
Sun, S., Cegielski, C. G., Jia, L., & Hall, D. J. (2018). Understanding the factors affecting the organizational adoption of big data. Journal of Computer Information Systems, 58(3), 193-203.
Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K. (1990). Processes of technological innovation: Lexington books.
Warmerdam, M., & Bredveld, P. (2003). A holistic approach to delivering the value of IT: business service management. IDC White Paper.
Weiner, B. J., Amick, H., & Lee, S.-Y. D. (2008). Conceptualization and measurement of organizational readiness for change: a review of the literature in health services research and other fields. Medical care research and review, 65(4), 379-436.
Athamena, B., & Houhamdi, Z. (2018). Model for decision-making process with big data. Journal of Theoretical and Applied Information Technology, 96, 5951-5961.
Baig, M. I., Shuib, L., & Yadegaridehkordi, E. (2019). Big data adoption: State of the art and research challenges. Information Processing & Management, 56(6). doi:10.1016/j.ipm.2019.102095
Beyer, M. A., & Laney, D. (2012). The importance of ‘big data’: a definition. Stamford, CT: Gartner, 2014-2018.
Bremser, C. (2018). Starting points for big data adoption. Paper presented at the Twenty-Sixth European Conference on Information Systems (ECIS2018), Portsmouth, UK.
Bremser, C., Piller, G., & Rothlauf, F. (2017). Strategies and Influencing Factors for Big Data Exploration. Paper presented at the AMCIS, 23rd American Conference on Information Systems, Boston, MA, USA.
Columbus, L. (2017). IBM predicts demand for data scientists will soar 28% by 2020. IBM White Paper.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
Diniz, E., Luvizan, S., Cassitas Hino, M., & Ferreira, P. (2018). Unveiling the Big Data Adoption in Banks: Strategizing the Implementation of a New Technology. In Digital Technology and Organizational Change (pp. 149-162). Cham: Springer-Verlag
Gangwar, H. (2018). Understanding the Determinants of Big Data Adoption in India: An Analysis of the Manufacturing and Services Sectors. Information Resources Management Journal 31( 4).
Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS quarterly, 213-236.
Günther, W. A., Mehrizi, M. H. R., Huysman, M., & Feldberg, F. (2017). Debating big data: A literature review on realizing value from big data. The Journal of Strategic Information Systems, 26(3), 191-209.
Haddad, A., Ameen, A., & Mukred, M. (2018). The Impact of Intention of Use on the Success of Big Data Adoption via Organization Readiness Factor. International Journal of Management and Human Science (IJMHS), 2(1), 43-51.
Han, Q., Liang, S., & Zhang, H. (2015). Mobile cloud sensing, big data, and 5G networks make an intelligent and smart world. IEEE Network, 29(2), 40-45.
Holt, D. T., Helfrich, C. D., Hall, C. G., & Weiner, B. J. (2010). Are you ready? How health professionals can comprehensively conceptualize readiness for change. Journal of general internal medicine, 25(1), 50-55.
Jeble, S., Kumari, S., & Patil, Y. (2018). Role of Big Data in Decision Making. Operations and Supply Chain Management: An International Journal, 11, 36. doi:10.31387/oscm0300198
Jeyaraj, A., Rottman, J. W., & Lacity, M. C. (2006). A review of the predictors, linkages, and biases in IT innovation adoption research. Journal of information technology, 21(1), 1-23.
Kamarulzaman, M. S., Hassan, N. H., Drus, S. M., & Ismail, S. A. (2019). A Review on Factors for Big Data Adoption towards Industry 4.0. Open International Journal of Informatics (OIJI), 7(2), 200-207.
Kart, L. (2015). Big Data Industry Insights. Stamford: Gartner.
Kościelniak, H., & Puto, A. (2015). BIG DATA in Decision Making Processes of Enterprises. Procedia Computer Science, 65, 1052-1058. doi:10.1016/j.procs.2015.09.053
Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META group research note, 6(70), 1.
Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think: Houghton Mifflin Harcourt.
Nasrollahi, M., Ramezani, J., & Sadraei, M. (2020). The Impact of Big Data Adoption on SMEs Performance. doi:10.21203/rs.3.rs-66047/v1
Rakovská, E., & Hudec, M. (2019). A Three–Level Aggregation Model for Evaluating Software Usability by Fuzzy Logic. International Journal of Applied Mathematics and Computer Science, 29(3), 489-501.
Ramezani, J., & Nasrollahi, M. (2020). A Model to Evaluate the Organizational Readiness for Big Data Adoption. International Journal of Computers Communications & Control, 15. doi:10.15837/ijccc.2020.3.3874
Rogers, E. M. (1995). Lessons for guidelines from the diffusion of innovations. The Joint Commission journal on quality improvement, 21(7), 324-328.
Saggi, M. K., & Jain, S. (2018). A survey towards an integration of big data analytics to big insights for value-creation. Information Processing & Management, 54(5), 758-790. doi:https://doi.org/10.1016/j.ipm.2018.01.010
Shahzad, K. (2018). Analysis of Influencing Factors of Big Data Adoption in Chinese Enterprises Using DANP Technique. Sustainability, 10. doi:10.3390/su10113956
Soon, K. W. K., Lee, C. A., & Boursier, P. (2016). A chronology of big data adoption: Review of literature. I J A B E R, 14(1), 521-544.
Sun, S., Cegielski, C. G., Jia, L., & Hall, D. J. (2018). Understanding the factors affecting the organizational adoption of big data. Journal of Computer Information Systems, 58(3), 193-203.
Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K. (1990). Processes of technological innovation: Lexington books.
Warmerdam, M., & Bredveld, P. (2003). A holistic approach to delivering the value of IT: business service management. IDC White Paper.
Weiner, B. J., Amick, H., & Lee, S.-Y. D. (2008). Conceptualization and measurement of organizational readiness for change: a review of the literature in health services research and other fields. Medical care research and review, 65(4), 379-436.
