Beyond technology: Management challenges in the Big Data era

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Eduardo de Rezende Francisco
http://orcid.org/0000-0001-8895-2089
José Luiz Kugler
http://orcid.org/0000-0003-1625-7807
Soong Moon Kang
http://orcid.org/0000-0003-1605-601X
Ricardo Silva
http://orcid.org/0000-0002-6502-9563
Peter Alexander Whigham
http://orcid.org/0000-0002-8221-6248

Abstract

The ability of organizations to produce, collect, manage, analyze, and transform data has increased rapidly over the past decade (Delen & Zolbanin, 2018). This has resulted in significant new challenges regarding how data can be leveraged for improving business decisions and how this new scenario changes business processes and operations (Vidgen, Shaw, & Grant, 2017). The widespread adoption of advanced analytical methods (e.g., machine learning) has attracted significant interest (Gupta, Deokar, Iyer, Sharda, & Schrader, 2018; Vassakis, Petrakis, & Kopanakis, 2018) particularly because the required data storage and methods can be accessed remotely through web-based interfaces such as cloud services. This has resulted in an increased belief that businesses must actively engage with this technology to remain competitive. However, this Red Queen scenario comes at a cost as collecting, curating, and managing large datasets requires expertise and dedicated staff, often consuming resources that do not contribute to core business activities. Consider the fact that there is an increasing role for data scientists and data engineers, among others, within organizations (Davenport & Patil, 2012). Roles such as Chief Data Officer (CDO) and Chief Analytics Officer (CAO)
are now commonplace within most organizations.

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How to Cite
FRANCISCO, E. de R.; KUGLER, J. L.; KANG, S. M.; SILVA, R.; WHIGHAM, P. A. Beyond technology: Management challenges in the Big Data era. RAE - Revista de Administracao de Empresas , [S. l.], v. 59, n. 6, p. 375–378, 2019. DOI: 10.1590/S0034-759020190603. Disponível em: https://periodicos.fgv.br/rae/article/view/80770. Acesso em: 27 feb. 2024.
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References

Ahmed, E., Yaqoob, I., Hashem, I. A. T., Khan, I., Ahmed, A. I. A., Imran, M., & Vasilakos, A. V. (2017). The role of big data analytics in internet of things. Computer Networks, 129(Part 2), 459-471. doi:10.1016/j. comnet.2017.06.013

Cabrera-Sánchez, J-P., & Ramos, A. F. V. (2019). Factors affecting the adoption of big data analytics in companies. RAE-Revista de Administração de Empresas, 59(6), xxx-xxx. doi:

Davenport, T. H., & Patil, D. J. (2012). Data scientist: The sexiest job of the 21st century. Harvard Business Review, 90(10), 70-76

Delen, D., & Zolbanin, H. M. (2018). The analytics paradigm in business research. Journal of Business Research, 90, 186-195. doi:10.1016/j. jbusres.2018.05.013

Demchenko, Y., Laat, C. de, & Membrey, P. (2014). Defining architecture components of the big data ecosystem. International Conference on Collaboration Technologies and Systems (CTS) (pp. 104-112). doi:10.1109/CTS.2014.6867550

Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897-904. doi: 10.1016/j.jbusres.2015.07.001

Gupta, A., Deokar, A., Iyer, L., Sharda, R., & Schrader, D. (2018). Big data & analytics for societal impact: Recent research and trends. Information Systems Frontiers, 20(2), 185-194. doi: 10.1007/s10796- 018-9846-7

Insardi, A., & Lorenzo, R. (2019). Measuring accessibility: A big data perspective on Uber service waiting times. RAE-Revista de Administração de Empresas, 59(6), xxx-xxx. doi:

Loebbecke, C., & Picot, A. (2015). Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. Journal of Strategic Information Systems, 24(3), 149-157. doi: 10.1016/j.jsis.2015.08.002

Maçada, A. C. G., Brinkhues, R. A., & Freitas, J. C. da S., Junior. (2019). Information management capability and big data strategy implementation. RAE-Revista de Administração de Empresas, 59(6), xxx-xxx. doi:

Newell, S., & Marabelli, M. (2015). Strategic opportunities (and challenges) of algorithmic decision-making: A call for action on the long-term societal effects of “datification”. Journal of Strategic Information Systems, 24(1), 3-14. doi: 10.1016/j.jsis.2015.02.001

Page, S. E. (2018). The model thinker: What you need to know to make data work for you. New York, USA: Hachette Book Group.

Queiroz, M. M., & Farias, S. C. (2019). Intention to adopt big data in supply chain management: A Brazilian perspective. RAE-Revista de Administração de Empresas, 59(6), xxx-xxx. doi:

Vassakis, K., Petrakis, E., & Kopanakis, I. (2018). Big data analytics: Applications, prospects and challenges. In G. Skourletopoulos, G. Mastorakis, C. Mavromoustakis, C. Dobre, & E. Pallis (Eds.), Mobile big data (Vol. 10, pp. 3-20). doi:10.1007/978-3-319-67925-9_1

Vidgen, R., Shaw, S., & Grant, D. B. (2017). Management challenges in creating value from business analytics. European Journal of Operational Research, 261(2), 626-639. doi:10.1016/j. ejor.2017.02.023

Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How “big data” can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234-246. doi:10.1016/j.ijpe.2014.12.031