Cloud-Driven Business Intelligence Modernization for Scalable Enterprise Data Integration and Decision Support
Keywords:
Cloud-Driven Business Intelligence,Enterprise Data Integration,Scalable Analytics Architecture,Decision Support Systems,Cloud Data Platforms,Data Warehousing Modernization,Business Intelligence Modernization,Real-Time Data Analytics,Data Governance and Integration,Enterprise Decision Intelligence.Abstract
Business Intelligence (BI) modernization establishes cloud-driven infrastructures from both employee and enterprise perspectives. Cloud automation structures modernize how BI users accelerate their access to integrated data, how integration reflects data consumers' changing needs, how cost-efficient BI adoption scales with workloads, and how BI-related risks are managed and reduced. Moreover, modernization is not an end goal per se; it supports organizational change and responds to business strategy over time. Key objectives include providing reliable business cases that directly link BI modernization with tangible business drivers, outcomes, and benefits, examining challenges associated with scaling BI-related investments and risks, and capturing architectural patterns used for BI modernization in cloud environments. Rapid and high-volume business changes force organizations to make decisions faster and reflect these decisions in their BI systems. Organizations also expect rapid adaptability of BI systems to changing new needs. Manual processes for uploading data and integrating BI systems accelerate BI-related investments and risks. Cloud services are frequently seen as a way to reduce these problems. However, BI-related investments and risk reduction often scale faster than the use of investment-reducing cloud services. The data pipelines connecting the upper BI reporting and analytics environments are often not auto-scalable. The underlying resource provisioning and cost-efficient measurement of big data analytics pipelines can be poorly structured. Guidelines and automation supporting the prediction of future analytics needs are often absent or not documented in these environments. A formal structure is needed to capture these issues in a scalable way.
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