Authors: Srujana Parepalli
Abstract: By March 2024, enterprise intelligence initiatives increasingly depended on the reliability of master data to support analytics, operational reporting, customer engagement, and automated decision systems. Organizations consolidated data from numerous operational sources, including transactional systems, customer platforms, supplier feeds, and third party reference datasets. These sources frequently represented the same real world entities using inconsistent identifiers, formats, and semantic conventions. As data volumes and integration velocity increased, traditional rule based record matching and manual standardization processes struggled to maintain accuracy, coverage, and timeliness at enterprise scale. AI assisted record matching emerged as a practical response to these limitations by augmenting deterministic matching logic with probabilistic similarity scoring, contextual inference, and adaptive learning. Rather than replacing existing master data management controls, AI techniques were increasingly applied to improve candidate matching, resolve ambiguous records, and normalize attributes across heterogeneous inputs. These approaches enabled enterprises to detect duplicates, align entity representations, and maintain consistent master views while reducing manual stewardship effort. However, the introduction of AI into master data workflows also introduced governance challenges related to explainability, confidence thresholds, override accountability, and downstream trust in standardized outputs. This paper examines AI assisted record matching and standardization for enterprise master data as of March 2024, focusing on architectural patterns, matching workflows, confidence management, and governance controls. The discussion frames AI as an augmentation layer within controlled master data pipelines, emphasizing operational accuracy, traceability, and stewardship alignment. The paper positions AI assisted matching as a foundational capability for enterprise intelligence systems that require consistent, auditable, and scalable entity resolution across rapidly evolving data landscapes.
DOI: https://doi.org/10.5281/zenodo.18640329
