Designing Robust Data Architectures for Big Data Analytics: Best Practices for Data Security and Business Intelligence.
DOI:
https://doi.org/10.56472/ICCSAIML25-154Keywords:
Big Data Analytics, Data Architecture, Data Security, Business Intelligence, Best Practices, Data Integration, Data Storage, Data Processing, Scalability, Organizational AlignmentAbstract
Designing robust data architectures is crucial for effective big data analytics, ensuring secure data management and enabling actionable business intelligence (BI). This paper explores best practices in constructing data architectures that not only safeguard data but also enhance BI capabilities. We examine key components such as data integration, storage, processing, and analytics, emphasizing the importance of scalability, flexibility, and security. Additionally, the paper discusses strategies for aligning data architectures with organizational goals to drive informed decision-making and maintain a competitive edge
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