Business Intelligence Systems for Data-Driven Decision Making: Cloud-Based Analytics Frameworks for Executive Intelligence and Operational Monitoring
TL;DR
According to the findings, cloud-native BI greatly enhances the speed of decision-making, enables uninterrupted monitoring of performance, and democratizes access to analytics, while also referring to such critical issues as gaps in data governance, information overload, lack of skills, and the lack of trust due to inconsistent data definitions and bad design of dashboards.
Abstract
The fast-changing business intelligence (BI) has revolutionized the way organizations gather, analyze and process data as a decision-making tool. Conventionally, BI systems were described as periodical, retrospective reporting that depended on centralized IT capabilities and past on-premise systems. These systems were useful when it came to simple monitoring of performance, but in most cases, they did not hold up to the increased need of timely and actionable information. The advent of cloud computing, big data applications and self-service analytics has redefined the face of BI fundamentally, and has made the intelligence accessible, scalable and real time across organizational levels. This article presents a descriptive narrative literature review focusing on the use of cloud-based BI platforms to change executive intelligence and operational analytics with specific regard to Microsoft Power BI. Based on peer-reviewed academic literature, industry publications, and technical documentation on platforms published between 2021 and 2025, the review summarizes the latest trends, advantages, and challenges related to the adoption of modern BI. According to the findings, cloud-native BI greatly enhances the speed of decision-making, enables uninterrupted monitoring of performance, and democratizes access to analytics. However, the review also refers to such critical issues as gaps in data governance, information overload, lack of skills, and the lack of trust due to inconsistent data definitions and bad design of dashboards. The research also outlines the new trends in augmented analytics, AI-based governance, and the increasing significance of data literacy as the key complements to technological investment. Comprehensively, the paper is a valuable addition to the body of BI literature as it combines technical, managerial, and human-focused views to provide practical guidance on how organizations pursue the use of cloud-based BI as a strategic decision-support capability in dynamic business settings.