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Leveraging Data Lakes and Warehouses for Business Intelligence in Media and Telecom

Author(s) Mahesh Mokale
Country United States
Abstract The media and telecommunications industries are undergoing a transformative evolution, driven by the convergence of technological advancements and shifting consumer behaviors. The rapid adoption of streaming services, 5G networks, and smart devices has led to an unprecedented surge in data generation. Each user interaction, from video streaming to mobile usage and network diagnostics, generates data that, when effectively harnessed, holds the potential to unlock significant business value. However, the sheer volume, complexity, and speed of data creation present formidable challenges for traditional data management systems. Data lakes and data warehouses have emerged as pivotal solutions for enabling robust Business Intelligence (BI) capabilities. A data lake serves as a vast reservoir capable of storing raw, unstructured, semi-structured, and structured data, offering businesses the flexibility to collect data from diverse sources without predefined schemas. In contrast, a data warehouse is a structured repository designed to store processed and organized data optimized for high-speed queries and analytical reporting. Together, these platforms create a holistic data ecosystem capable of supporting both exploratory and operational analytics. The successful integration of data lakes and warehouses empowers media and telecom companies to transition from reactive to proactive decision-making. By leveraging data-driven insights, these organizations can enhance customer experiences, optimize network performance, reduce operational costs, and unlock new revenue streams. This paper provides a comprehensive analysis of the strategic advantages of deploying data lakes and warehouses, outlines their integration methodologies, and examines their application in media and telecom business intelligence. Furthermore, it highlights the challenges faced in implementing these systems and offers insights into future trends that will shape the data management landscape in these industries.
Keywords Data lakes, Data warehouses, Business intelligence, Media, Telecom, Customer insights, Personalization, Network performance, Revenue optimization, Content monetization, Churn prediction, Real-time analytics, Data governance, Data quality, Cloud computing, Data integration, ETL, ELT, Predictive analytics, Artificial intelligence, Machine learning, Big data, Scalability, Security, Data privacy, Compliance, Cost management, Hybrid cloud, Data-driven decision-making
Field Engineering
Published In Volume 11, Issue 1, January-March 2020
Published On 2020-01-07
Cite This Leveraging Data Lakes and Warehouses for Business Intelligence in Media and Telecom - Mahesh Mokale - IJSAT Volume 11, Issue 1, January-March 2020. DOI 10.71097/IJSAT.v11.i1.2162
DOI https://doi.org/10.71097/IJSAT.v11.i1.2162
Short DOI https://doi.org/g8593n
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