Year: 2025
Type: article
Abstract: In the fast-paced era of financial analytics, real-time insights are now the top priority for organizations to remain agile, transparent, and strategically oriented. Although sophisticated data analytics tools are now readily available, there is still a research gap in the integration of real-time data processing with enterprise-level financial planning systems. The current research aims to bridge this gap by investigating the architectural design and implementation of real-time financial insights using SAP Analytics Cloud (SAC). With the growing complexity of financial operations and data silos between departments, the traditional batch-processing models are incapable of delivering the immediacy required for informed decision-making. SAP Analytics Cloud, with its in-built machine learning, predictive analytics, and seamless integration with SAP S/4HANA and other data sources, offers a revolutionary solution to the visualization and interpretation of financial information. This study suggests an improved architecture that takes advantage of SAC's real-time connectivity, smart capabilities, and cross-functional integration to facilitate dynamic financial governance and forecasting. By developing an architecture that accommodates live data models, combined analytics, and collaborative dashboards, this study provides a framework for addressing latency, fragmentation, and interpretability issues associated with financial reporting. The research approach of this study involves scenario-based architectural modeling, performance assessment, and a case-based validation technique to guarantee the effectiveness of the system. The findings of this study are anticipated to enhance the theoretical and practical knowledge of real-time financial insight systems and offer a guiding foundation for organizations that require the enhancement of their financial analytics platform using SAC. This study bridges a significant gap in existing literature by combining real-time analytics capabilities with enterprise financial intelligence frameworks.
Authors: Karan Shah, Dr Anand Singh
Institution: Johns Hopkins University
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4414829323
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