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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM11159

Scalable Data Management Strategies for Telecommunications Enterprises

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM11159

Volume:

5 |

12 |

Dec 2021

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM11159

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.

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MM

Mahesh Mokale

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Abstract

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

Scalable Data Management Telecommunications Enterprises 5G Networks Internet of Things (IoT) Cloud Computing Edge Computing AI-Driven Analytics DataOps Methodologies Data Federation Real-Time Data Processing Blockchain for Data Security Automation in Data Management Hyper-Personalization Regulatory Compliance DOI: 10.55041/IJSREM11159

Cite This Article

Mahesh Mokale, "Scalable Data Management Strategies for Telecommunications Enterprises," International Journal of Scientific Research in Engineering and Management, vol. 5, no. 12, Dec 2021. https://doi.org/10.55041/IJSREM11159

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12/26/2021

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Review Rounds: 2

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