A Research Review: Ai and Data Science Applications in the Telecom Industry
DOI:
https://doi.org/10.17697/ibmrd/2022/v11i2/172620Keywords:
Data Science, Artificial Intelligence, Skill, Telecom, Customer ServiceAbstract
This research is about the study of the telecom sector is becoming more digital, as is the case with other sectors in the modern economy. In the modern telecommunications sector, digital transmission of voice conversations is standard, data services often outnumber voice services in significance and income, and an increasing percentage of the underlying infrastructure is now digital and software-driven. In addition, customer service and back-office processes in the telecom industry are experiencing digital transformation. The importance of data science and artificial intelligence to the industry continues to grow. Massive volumes of data are produced by the network operations, customer service activities, and infrastructure operations of telecommunications businesses. That's why there's so much focus on data science in the telecommunications industry today. With the help of data science and AI, telecom operators may be able to better understand their data and use it to do things like make their networks more reliable, save money on operating costs, and improve customer service. The use of AI in the network industry might help cut down on setup and maintenance fees, boost efficiency, provide better assistance for customers, and pave the way for the creation of brand new offerings. This document catalogs and evaluates the many AI applications that already exist and are planned for the near future. As a result of studying the existing literature's worth of applications, Due to the exponential growth of data being produced by the sector, artificial intelligence (AI) and data scientists are in high demand in the telecom business. In terms of big data expansion, Analytics Insight reports that the telecom and IT sectors account for 33% of the market. According to the forecast, expenditure on big data in telecom would increase from $59 billion in 2019 to over $105 billion in 2023. Additionally, by 2023, more than four million positions will be vacant in the industry, requiring the expertise of skilled candidates.Downloads
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AI in Telecom Operations:Opportunities & ObstaclesAUTHOR: JAMES CRAWSHAW, SENIOR ANALYST, HEAVY READINGHEAVY READING | SEPTEMBER 2018 | AI IN TELECOM OPERATIONS
Godey, B., Manthiou, A., Pederzoli, D., Rokka, J.,Aiello, G., Donvito, R., & Singh, R. (2016).Socialmediamarketingeffortsofluxurybrands:Influenceonbrandequityandconsumer behavior. Journalofbusinessresearch, 69(12),5833-5841. DOI: https://doi.org/10.1016/j.jbusres.2016.04.181
Gupta,S.,Agarwa,A.K.,&Chauhan,A.K.(2018). Social media and its impact on consumersbuying behavior with special reference to apparelindustryinBareillyregion. SMARTJournalofBusinessManagementStudies,14(2),17-23. DOI: https://doi.org/10.5958/2321-2012.2018.00013.1
Liu,Q.,Zhang,X.,Huang,S.,Zhang,L.,&Zhao, 5. Y. (2020). Exploring consumers’ buying behaviorin a large online promotion activity: The role of psychological distance and involvement. Journal of theoretical and applied electronic commerce research, 15(1),66-80. DOI: https://doi.org/10.4067/S0718-18762020000100106
Varghese, S., Nandhini M. (2020). The Influenceof Demographic Factorsonthe Customers of Online-Shopping with Special Reference to Kochi City. Journal of Adv Research in Dynamical &ControlSystems,12(1-SpecialIssue). DOI: https://doi.org/10.5373/JARDCS/V12SP1/20201137
https://hevodata.com/learn/artificial-intelligence-in-data-science/
https://cacm.acm.org/magazines/2021/11/256400-there-is-no-ai-without-data/fulltext
Abraham, R., Schneider, J., and Brocke, J.v. Data governance: A conceptual framework, structured review, and research agenda. Intern. J. of Information Management 49 (2019), 424–438. DOI: https://doi.org/10.1016/j.ijinfomgt.2019.07.008
Ballard, C., Compert, C., Jesionowski, T., Milman, I., Plants, B., Rosen, B., and Smith, H. Information governance principles and practices for a big data landscape. IBM (2014).
Ballou, D.P. and Tayi, G.K. Enhancing data quality in data warehouse environments. Communications of the ACM 42, 1 (1999), 73–78. DOI: https://doi.org/10.1145/291469.291471
Cao, L. Data science: A comprehensive overview. ACM Computing Surveys 50, 3 (2017), 1–42. DOI: https://doi.org/10.1145/3076253
Chu, X., Ilyas, I.F., Krishnan, S., and Wang, J. Data cleaning: Overview and emerging challenges. In Proceedings of the Intern. Conf. on Management of Data (SIGMOD), ACM, New York (2016), 2201–2206. DOI: https://doi.org/10.1145/2882903.2912574
Cui, Y., Kara, S., and Chan, K.C. Manufacturing big data ecosystem: A systematic literature review. Robotics and Computer Integrated Manufacturing 62 (2020) Article 101861. DOI: https://doi.org/10.1016/j.rcim.2019.101861
Daraio, C., Lenzerini, M., Leporelli, C., Naggar, P., Bonaccorsi, A., and Bartolucci, A. The advantages of an ontology-based data management approach: Openness, interoperability and data quality. Scientometrics 108, 1 (2016), 441–455. DOI: https://doi.org/10.1007/s11192-016-1913-6
