Abstract

Financial analysis for listed companies increasingly depends on timely, trustworthy, and multi-dimensional data. Between 2013 and 2023, advances in data warehousing (DW) and online analytical processing (OLAP)—including columnar storage, cloud-elastic warehouses, and big-data OLAP engines—fundamentally reshaped how analysts prepare, explore, and govern financial information. This paper synthesizes post-2013 scholarship and engineering literature, proposes a reference architecture that integrates enterprise resource planning (ERP) data with iXBRL filings and market data, and demonstrates how OLAP cubes support ratio analysis, benchmarking, scenario planning, and regulatory reporting. Comparative analysis shows where MOLAP/ROLAP/HOLAP and modern cloud warehouses each excel; we also discuss performance, cost, and governance trade-offs. We illustrate the approach with a worked example for a generic listed firm (“ABC Ltd.”) and report expected gains in latency, auditability, and analytical breadth.

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