Welcome to Francis Academic Press

Academic Journal of Business & Management, 2023, 5(4); doi: 10.25236/AJBM.2023.050408.

Decision-making System Model of Modern Enterprise Management Based on Big Data


Li Jun1, Dong Jinyu2

Corresponding Author:
Li Jun

1Shizhen College of Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China

2Yunnan Industry and Commerce College, Kunming, Yunnan, China


The current era of big data has brought many opportunities for enterprise management, and the use of big data can guide modern enterprise management decisions. Based on this, the decision-making system model of modern enterprise management with big data is studied, with a data visualization system proposed. Based on the development status of the information-based supervision system of the process plant and the actual application requirements of the enterprise, the key technologies of the information-based supervision system of the process plant in view of the contradiction between the two are studied. The practice shows that the experiment has improved the technical level and supervisory ability of the information-based supervision system of the process plant, which can guide the decision-making of the production enterprise.


big data; enterprise management; decision-making

Cite This Paper

Li Jun, Dong Jinyu. Decision-making System Model of Modern Enterprise Management Based on Big Data. Academic Journal of Business & Management (2023) Vol. 5, Issue 4: 43-50. https://doi.org/10.25236/AJBM.2023.050408.


[1] Höchtl J, Parycek P, Schöllhammer R. Big data in the policy cycle: Policy decision making in the digital era. Journal of Organizational Computing & Electronic Commerce, 2016, 26(1-2), pp, 147-169.

[2] Kobayashi K, Kaito K. Big data-based deterioration prediction models and infrastructure management: towards assetmetrics. Structure & Infrastructure Engineering, 2016, 13(1), pp, 84-93.

[3] Lagos C, Carrasco R, Fuertes G, et al. Big Data on Decision Making in Energetic Management of Copper Mining. International Journal of Computers Communications & Control, 2016, 12(1), pp, 61-75.

[4] He W, Wang F K, Akula V. Managing extracted knowledge from big social media data for business decision making. Journal of Knowledge Management, 2017, 21(2), pp, 275-294.

[5] Wu D, Birge J R. Risk Intelligence in Big Data Era: A Review and Introduction to Special Issue. IEEE Transactions on Cybernetics, 2017, 46(8), pp, 1718-1720.

[6] Horita F E A, Mendiondo E M, Mendiondo E M, et al. Bridging the gap between decision-making and emerging big data sources. Decision Support Systems, 2017, 97(C), pp, 12-22.

[7] Lu Q, Li Z, Zhang W, et al. Autonomic deployment decision making for big data analytics applications in the cloud. Soft Computing, 2017, 21(16), pp, 4501-4512.

[8] Laat P B D. Big data and algorithmic decision-making: can transparency restore accountability? Acm Sigcas Computers & Society, 2017, 47(3), pp, 39-53.

[9] Daniel E O. Artificial Intelligence and Big Data. IEEE Intelligent Systems, 2013, 28(2), pp, 96-99.

[10] McCarthy, John. Generality in artificial intelligence. Resonance, 2014, 19(3), pp, 283-296.

[11] Imran M, Castillo C, Ji L. AIDR: artificial intelligence for disaster response. International Conference on World Wide Web. ACM, 2014, pp, 159-162.

[12] Hovy E, Navigli R, Ponzetto S P. Collaboratively built semi-structured content and Artificial Intelligence: The story so far. Artificial Intelligence, 2013, 194(Complete), pp, 2-27.

[13] Yampolskiy R V. Artificial Intelligence Safety Engineering: Why Machine Ethics Is a Wrong Approach. Philosophy and Theory of Artificial Intelligence. Springer Berlin Heidelberg, 2013, pp, 389-396.

[14] Moravík M, Schmid M, Burch N. DeepStack: Expert-level artificial intelligence in heads-up no-limit poker. Science, 2017, 356(6337), pp, 508.

[15] Parkes D C, Wellman M P. Economic reasoning and artificial intelligence. Science, 2015, 349(6245), pp, 267.

[16] Rigas E S, Ramchurn S D, Bassiliades N. Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(4), pp, 1619-1635.

[17] Glauner P, Boechat A, Dolberg L. The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey. International Journal of Computational Intelligence Systems, 2017, 10(1), pp, 760-775.

[18] Jennifer Hill, W. Randolph Ford, Ingrid G. Farreras. Real conversations with artificial intelligence: A comparison between human–human online conversations and human–chatbot conversations. Computers in Human Behavior, 2015, 49, pp, 245-250.

[19] Bundy A. Preparing for the future of Artificial Intelligence. Ai & Society, 2017, 32(2), pp, 1-3.

[20] Cismondi F, Celi LA, Fialho AS. Reducing unnecessary lab testing in the ICU with artificial intelligence. International Journal of Medical Informatics, 2013, 82(5), pp, 345-358.