The convergence of data lakes and data warehouses into unified lakehouse architectures represents a paradigm shift in enterprise data management, enabling unprecedented capabilities for real-time business intelligence and risk monitoring. This systematic review synthesizes current research and industry practices on lakehouse implementation for enterprise BI, examining how these platforms address critical limitations of traditional architectures that create delays and data silos impeding executive decision-making. We analyze architectural components enabling rapid data processing, integration patterns with enterprise systems, and impacts on organizational agility and risk management effectiveness. The review covers technical foundations including streaming integration, governance frameworks, and ACID transaction capabilities, alongside organizational considerations such as change management, skills development, and implementation strategies. Findings indicate that lakehouse-enabled BI systems significantly enhance executive visibility into cross-domain organizational risks while reducing the complexity and operational costs associated with maintaining separate analytical and operational platforms. We identify critical success factors for implementation and outline research directions for federated learning, autonomous risk detection, and ethical governance frameworks.
Adebayo et al. (2026) studied this question.