Authors: Paul Hall, George Cox, Edward Howard, Chaitanya Srinivas, Niharika
Abstract: The increasing complexity of enterprise data ecosystems has created significant challenges in maintaining accurate, transparent, and continuously updated data lineage across distributed databases, cloud platforms, data warehouses, data lakes, APIs, and analytical systems. Traditional data lineage approaches primarily depend on manually defined mappings, static metadata repositories, and rule-based tracking mechanisms, which can become difficult to maintain as enterprise data environments evolve rapidly. This research proposes an Intelligent Knowledge Graph Framework for Predictive Enterprise Data Lineage that integrates knowledge graph technologies, artificial intelligence, machine learning, metadata management, and automated lineage discovery to provide a scalable and intelligent approach to enterprise data traceability. The proposed framework represents data assets, business entities, transformations, dependencies, processes, and relationships as interconnected knowledge graph entities, enabling comprehensive representation and semantic analysis of enterprise data flows. Machine learning and predictive analytics mechanisms are incorporated to identify hidden dependencies, predict potential lineage changes, detect anomalous transformation patterns, and assess the impact of upstream modifications on downstream data assets. The framework further integrates automated metadata extraction, schema analysis, provenance tracking, and lineage inference to reduce manual lineage maintenance and improve data transparency. A continuous feedback mechanism enables the framework to update lineage relationships as new data sources, transformations, and business processes are introduced. The proposed approach can enhance enterprise data governance, impact analysis, regulatory compliance, data quality management, and decision-making by providing an intelligent and predictive view of data movement and dependencies. The effectiveness of the framework can be evaluated using metrics such as lineage discovery accuracy, relationship prediction accuracy, anomaly-detection precision, lineage completeness, metadata coverage, impact-analysis accuracy, and reduction in manual lineage management effort. The framework provides a foundation for developing adaptive, explainable, and intelligent data lineage capabilities for modern enterprise data platforms.
DOI: http://doi.org/10.5281/zenodo.23075967
