Abstract
The fundamental significance of Big Data is in the possibility to enhance effectiveness and advancement for employees with regards to utilize a big volume of data, of various type. If Big Data is defined clearly and strongly characterized, banks can improve in their business, thence prompting to efficiency in various fields. The aim of this study is to identify what are factors that effect on Big Data Analytics skills and further propose a long-term development, self-efficacy and level of analytics as framework for a successful career in Big Data analytics in banking sector. This is because banking sector is one of the most services sector which are having big flow of data. Using quantitative approach to emphasize objective measurements and the statistical, numerical analysis survey in this research, 161 bankers were randomly selected from ten banks in Malaysia. The result of the study revealed that two independent variables significantly affect the successful of Big Data in banking sector which were long-term development and self-efficacy. On the other hand, the third variable (level of analytics) has fairly affect the success of Big Data through the skills indirectly.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Schermann, M., Hemsen, H., Buchmuller, C., Bitter, T., Krcmar, H., Markl, V., Hoeren, T.: Big data - an interdisciplinary opportunity for information systems research. Bus. Inf. Syst. Eng. 6(5), 261–266 (2014)
Wamba, S.F., Akter, S., Edwards, A., Chopin, G., Gnanzou, D.: How ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study. Int. J. Prod. Econ. 165, 234–246 (2015)
Nikam, A.V., Bhoite, S.D.: Leverage of big data analytics for banking sector. Indian J. Appl. Res. 5(8) (2016)
Sivarajah, U., Kamal, M.M., Irani, Z., Weerakkody, V.: Critical analysis of big data challenges and analytical methods. J. Bus. Res. 70, 263–286 (2016)
Kwon, O., Lee, N., Shin, B.: Data quality management, data usage experience and acquisition intention of big data analytics. Int. J. Inf. Manag. 34, 387–394 (2014)
Waller, M.A., Fawcett, S.E.: Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. J. Bus. Logistics 34(2), 77–84 (2015)
LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S., Kruschwitz, N.: Big data, analytics: and the path from insights to value. MIT Sloan Manag. Rev. 52(2), 21–31 (2014)
Shah, S., Horne, A., Capellá, J.: Good data won’t guarantee good decisions. Harvard Bus. Rev. 90(4), 23–25 (2014)
Provost, F., Fawcett, T.: Data science and its relationship to big data and data-driven decision making. Big Data 1(1), 51–59 (2013)
Wixom, B., Yen, B., Relich, M.: Maximizing value from business analytics. MIS Q. Executive 12(2), 37–49 (2013)
Halaweh, M., Massry, A.E.: Conceptual model for successful implementation of big data in organizations. J. Int. Technol. Inf. Manag. 24(2), 2 (2015)
Martindale, R.J.J., Collins, D., Wang, J.C.K., McNeill, M., Lee, K.S., Sproule, J., Westbury, T.: Development of the talent development environment questionnaire for sport. J. Sports Sci. 28(11), 1209–1221 (2010)
Bandura, A.: On the functional properties of perceived self-efficacy revisited. J. Manag. 38(1), 9–44 (2012)
Onyishi, I.E., Ogbodo, E.: The contributions of self-efficacy and perceived organizational support when taking charge at work. SA J. Ind. Psychol. 38(1), 1–11 (2012)
Grillo, M., Hackett, A.: What types of predictive analytics are being used in talent management organizations? Cornell University (2015). http://digitalcommons.ilr.cornell.edu/student/74. Accessed 8 Feb 2017
Craig, E., Hou, C., McCarthy, B.F.: The looming global analytics talent mismatch in banking (2013)
Bersin, J., O’Leonard, K., Wang-Audia, W.: High-impact talent analytics: Building a world-class HR measurement and analytics function. Bersin by Deloitte (2013)
Zaial, A., Rjdeh, S.M.: Information seeking behaviour among post graduate students. Tourism research and innovations (2012)
Becker, R., Doyle, D.: Sampling in SAS ® using PROC SURVEYSELECT. SAS Institute Inc., New york (2016)
Tanburn, R.: Practical Advice for Selecting Sample Sizes, The Donor Committee for Enterprise Development, May 2015. http://www.enterprise-development.org/wp-content/uploads/Practical_advice_for_selecting_sample_sizes_May2015.pdf. Accessed 27 Dec 2016
Marcus, A. (ed.): DUXU 2015. LNCS, vol. 9188. Springer, Cham (2015). doi:10.1007/978-3-319-20889-3
Ott, R.L., Longnecker, M.T.: An Introduction to Statistical Methods and Data Analysis. Nelson Education, Scarborough (2015)
Mercy, J.L.: Analytical report on, Luxembourge: Luxembourg: Publications Office of the European Union (2016)
Masson, M.E.: A tutorial on a practical Bayesian alternative to null-hypothesis significance testing. Behav. Res. Methods 43(3), 679–690 (2011)
Levine, T.R., Weber, R., Hullett, C., Park, H.S., Lindsey, L.L.M.: A critical assessment of null hypothesis significance testing in quantitative communication research. Hum. Commun. Res. 34(2), 171–187 (2008)
Knapp, C.W., McCluskey, S.M., Singh, B.K., Campbell, C.D., Hudson, G., Graham, D.W.: Antibiotic resistance gene abundances correlate with metal and geochemical conditions in archived Scottish soils. PLoS ONE 6(11), e27300 (2011)
Roshan, S., Madhumita, M.: Impact of training practices on employee productivity. Intersci. Manag. Rev. 2, 87–92 (2012)
Khawaja, J., Nadeem, B.A.: Training and development program and its benefits to employee and organization: a conceptual study. Eur. J. Bus. Manag. 5(2) (2013)
Virginie, M.: A study of the effect of national culture value and self-efficacy on organizational commitment in Haiti. Graduate Institute Human Resource Development (2010)
Masood, U.H., Rabia, K., Kashif, N.: The effects of personal characteristics on organizational commitment. through job satisfaction: an empirical study of Pakistan’s financial sector. Middle-east. J. Sci. Res. 16(7), 942–951 (2013)
Dixit, V., Bhati, M.: A study about employee commitment and its impact on sustained productivity in Indian auto-component Industry. Eur. J. Bus. Soc. Sci. 1(6), 34–51 (2012)
Salami, S.O.: Demographic and psychological factors predicting organizational commitment among industrial workers. Anthropologist 10(1), 31–38 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
AL-Hakimi, A.A.A. (2017). Big Data Skills Required for Successful Application Implementation in the Banking Sector. In: Mohamed Ali, M., Wahid, H., Mohd Subha, N., Sahlan, S., Md. Yunus, M., Wahap, A. (eds) Modeling, Design and Simulation of Systems. AsiaSim 2017. Communications in Computer and Information Science, vol 752. Springer, Singapore. https://doi.org/10.1007/978-981-10-6502-6_34
Download citation
DOI: https://doi.org/10.1007/978-981-10-6502-6_34
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6501-9
Online ISBN: 978-981-10-6502-6
eBook Packages: Computer ScienceComputer Science (R0)