Universal Data Models for Enterprise Multi-System Analytics: A Scalable Framework for Cross-Domain Data Consolidation
Author(s): Pratiksha Kar
Publication #: 2506018
Date of Publication: 13.06.2025
Country: USA
Pages: 1-12
Published In: Volume 11 Issue 3 June-2025
DOI: https://doi.org/10.5281/zenodo.15658010
Abstract
This paper presents a Universal Data Model (UDM) framework for consolidating disparate enterprise systems into a single, high-performance analytics platform. By organizing data into three schema zones—Migration Performance Metrics, Revenue & Credit Metrics, and Partner ROI Metrics—and implementing Apache Airflow–driven ETL pipelines that range- partition and hash-distribute fact tables, UDM enables partition pruning and data locality to minimize I/O and network shuffles. High-cardinality lookup attributes are denormalized, and pre- aggregated materialized views support common queries, yielding up to 97% query latency reduction and over 60% ETL load time savings in a production-scale, 18-month dataset covering more than 2 billion rows. Rigorous governance—schema isolation for PII, least-privilege access controls, automated lineage tracking, and assertion-based validation—ensures data security and consis- tency, reducing metric discrepancies from 5% to 0.2%. Business results include a 95% reduction in executive reporting effort, a 60% decrease in analyst preparation time, and sustained sub- second responsiveness during a 25% data volume spike. Finally, the paper outlines future work in automated schema matching, near-real-time ingestion, adaptive partitioning, and extension to other enterprise domains.
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