DATA WAREHOUSING AND TEXT ANALYTICS AS INSTRUMENTS OF CULTURAL KNOWLEDGE MANAGEMENT: IMPLICATIONS FOR DIGITAL PRESERVATION AND SOCIETAL DECISION-MAKING

Abstract

The rapid proliferation of digitized cultural artifacts, archival records, and institutional knowledge repositories presents both opportunities and challenges for contemporary information management. This paper investigates the convergence of data warehousing architectures and text analytics methodologies as a dual-instrument framework for the systematic organization, retrieval, and interpretation of large-scale cultural and societal datasets. Drawing on principles from database management, distributed data systems, and natural language processing, we propose a conceptual model that enables cultural institutions, educational bodies, and public organizations to transform unstructured textual heritage into structured, queryable, and analytically actionable knowledge assets. We further examine how decision-support capabilities embedded within such architectures can inform policy formulation, resource allocation, and the preservation of intangible cultural knowledge in an increasingly data-driven society. The implications of this framework extend beyond archival science into domains of digital humanities, public governance, and enterprise knowledge management, establishing a methodological bridge between computational data science and cultural stewardship.