Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM11159
Volume:
5 |
12 |
Dec 2021
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
In the current technological landscape marked by the widespread adoption of 5G networks, the proliferation of Internet of Things (IoT) devices, and the ever-growing need for real-time data processing, telecommunications enterprises are encountering unprecedented challenges in managing and scaling their vast volumes of data. The digital age has brought about a transformative surge in the volume, variety, and velocity of data, compelling telecom companies to reevaluate and enhance their data management strategies to remain competitive. This surge is not limited to just an increase in the number of connected devices but also encompasses the complexity of data types—ranging from structured and unstructured customer data to high-frequency network performance metrics, sensor-generated data, and real-time analytics. The sheer scale and speed at which data is generated in the telecom industry make traditional data management frameworks insufficient. Without robust, scalable strategies in place, enterprises risk inefficiencies in operations, delays in decision-making, and missed opportunities for revenue growth and innovation. Therefore, adopting a forward-looking approach to data management is no longer optional but an absolute necessity. This paper delves into a range of innovative approaches to scalable data management, highlighting how cutting-edge technologies such as cloud computing, edge computing, process automation, and artificial intelligence (AI)-driven analytics can provide telecom companies with the tools they need to thrive in this data-driven era. Cloud adoption offers unmatched scalability and flexibility, while edge computing enables low-latency processing by bringing computational power closer to the data source. Additionally, automation simplifies complex workflows, and AI-powered analytics unlock actionable insights that can drive operational efficiency, enhance customer satisfaction, and create new revenue streams. By exploring these strategies, this paper aims to provide actionable insights and a roadmap for telecom companies to not only optimize their existing data infrastructure but also align their operations with emerging industry trends, ensuring long-term growth, resilience, and competitiveness in an increasingly interconnected world.
KEYWORDS
Publication Date
12/26/2021
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
2
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...