DATAFICATION AS A NECESSARY STEP IN THE PROCESSING OF BIG DATA IN DECISION-MAKING TASKS OF BUSINESS
DOI:
https://doi.org/10.12955/pns.v2.156Keywords:
datafication, logistics, classification, DSS, big dataAbstract
The digital transformation of business in the light of opportunities and focusing on the challenges posed by the introduction of Big Data in enterprises allows for a more accurate reflection of the internal and external environmental stimuli. Intuition ceases to be present in the decision-making process, and decision-making becomes strictly data-based. Thus, the precondition for data-based decision-making is relevant data in digital form, resulting from data processing. Datafication is the process by which subjects, objects and procedures are transformed into digital data. Only after data collection can other natural steps occur to acquire knowledge to improve the company's results if we move in the industry's functioning context. The task of finding a set of attributes (selecting attributes from a set of available attributes) so that a suitable alternative can be determined in its decision-making is analogous to the task of classification. Decision trees are suitable for solving such a task. We verified the proposed method in the case of logistics tasks. The analysis subject was tasks from logistics and 80 well-described quantitative methods used in logistics to solve them. The result of the analysis is a matrix (table), in which the rows contain the values of individual attributes defining a specific logistic task. The columns contain the values of the given attribute for different tasks. We used Incremental Wrapper Subset Selection IWSS package Weka 3.8.4 to select attributes. The resulting classification model is suitable for use in DSS. The analysis of logistics tasks and the subsequent design of a classification model made it possible to reveal the contours of the relationship between the characteristics of a logistics problem explicitly expressed through a set of attributes and the classes of methods used to solve them.
References
Ahmim, A., Maglaras, L., Ferrag, M. A., Derdour, M., & Janicke, H. (2019, 29-31 May 2019). A Novel Hierarchical Intrusion Detection System Based on Decision Tree and Rules-Based Models. Paper presented at the 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS).
Athamena, B., & Houhamdi, Z. (2018). Model for decision-making process with big data. Journal of Theoretical and Applied Information Technology, 96, 5951-5961.
Bermejo, P. (2020). IWSS: Incremental Wrapper Subset Selection (Version 1.0.0): WEKA.
Brezina, I. (2003). Kvantitatívne metódy v logistike [Quantitative methods in logistics]. Bratislava, Slovak Republic: Vydavateľstvo EKONÓM.
Daas, D., Hurkmans, T., Overbeek, S., & Bouwman, H. (2013). Developing a decision support system for business model design. Electron. Mark., 23(3), 251– 265.
Eriksson, Y. (2019). Digitalization of society: what challenges will users meet? HBiD - Human Behaviour in Design, Proceedings of the 2nd SIG conference, 125-127. DOI: 10.18726/2019_2
Fernández-Rovira, C., Álvarez Valdés, J., Molleví, G., & Nicolas-Sans, R. (2021). The digital transformation of business. Towards the datafication of the relationship with customers. Technological Forecasting and Social Change, 162. doi:10.1016/j.techfore.2020.120339
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.
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
Jia, L., Hall, D., & Song, J. (2015). The Conceptualization of Data-driven Decision Making Capability. Paper presented at the Twenty-first Americas Conference on Information Systems, , Puerto Rico.
Jones, M. (2019). What we talk about when we talk about (big) data. Journal of Strategic Information Systems, 28(1), 3-16. doi:10.1016/j.jsis.2018.10.005
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
Li, M., Xu, H., & Deng, Y. (2019). Evidential Decision Tree Based on Belief Entropy. Entropy, 21(9), 897.
Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think: Houghton Mifflin Harcourt.
Mejias, U. A., & Couldry, N. (2019). Datafication. Internet Policy Review, 8(4). doi:10.14763/2019.4.1428
Merendino, A., Dibb, S., Meadows, M., Quinn, L., Wilson, D., Simkin, L., & Canhoto, A. (2018). Big Data, Big Decisions: The Impact of Big Data on Board Level Decision-Making. Journal of Business Research, 93, 67-78. doi:10.1016/j.jbusres.2018.08.029
Musik, C., & Bogner, A. (2019). Book title: Digitalization & society. Österreichische Zeitschrift für Soziologie, 44(1), 1-14. doi:10.1007/s11614-019-00344-5
Myles, A. J., Feudale, R. N., Liu, Y., Woody, N. A., & Brown, S. D. (2004). An introduction to decision tree modeling. Journal of Chemometrics: A Journal of the Chemometrics Society, 18(6), 275-285.
Power, D. J. (2002). Decision support systems: concepts and resources for managers: Greenwood Publishing Group.
Singhal, S., & Jena, M. (2013). A study on WEKA tool for data pre-processing, classification and clustering. International Journal of Innovative technology and exploring engineering (IJItee), 2(6), 250-253.
Song, Y.-Y., & Ying, L. (2015). Decision tree methods: applications for classification and prediction. Shanghai archives of psychiatry, 27(2), 130.
Southerton, C. (2020). Datafication. In L. A. Schintler & C. L. McNeely (Eds.), Encyclopedia of Big Data (pp. 1-4). Cham: Springer International Publishing.
Srivastava, S. (2014). Weka: a tool for data pre-processing, classification, ensemble, clustering and association rule mining. International Journal of Computer Applications, 88(10), 26 - 29.
Travica, B. (2017). Big Data Aspects and Decision Making. Paper presented at the Sixth European Academic Research Conference on Global Business, Economics, Finance and Social Sciences, 1-3, July 2017, Italy
Yousuf, H., & Zainal, A. (2020). Quantitative Approach in Enhancing Decision Making Through Big Data as An Advanced Technology. Advances in Science Technology and Engineering Systems Journal, 5, 109-116. doi:10.25046/aj050515
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