Building SME Resilience through Agile Data Architecture: Integrating Data Vault 2.0 and Business Intelligence for Competitive Advantage

Abstract

Purpose: In the wake of global disruptions such as the COVID-19 pandemic, Small and Medium-sized Enterprises (SMEs) face heightened pressure to digitize operations to maintain competitive advantage. This study explores the intersection of advanced data modeling—specifically Data Vault 2.0—and modern Business Intelligence (BI) tools to create a resilient infrastructure for decision-making.

Design/Methodology/Approach: The research utilizes a systematic integrative review, synthesizing literature on Crisis Management, Knowledge Management Systems (KMS), and the TAM-TOE (Technology Acceptance Model and Technology-Organization-Environment) adoption framework. It further analyzes the technical specifications of BI platforms (Tableau, Power BI) and data warehousing methodologies to propose a comprehensive adoption framework.

Findings: The analysis suggests that traditional data warehousing techniques (Star Schemas) often lack the agility required for modern SMEs. The implementation of Data Vault modeling, when coupled with governed BI strategies, significantly correlates with improved Intellectual Capital and operational flexibility. Furthermore, the integration of AI within these BI ecosystems is modulated by organizational readiness and environmental pressures.

Originality/Value: This paper bridges the gap between technical data engineering concepts (ETL, Data Modeling) and strategic management theories (Intellectual Capital, Crisis Resilience), offering a practical roadmap for SMEs to leverage data as a strategic asset rather than a byproduct of operations.