Engineering Traceable Data Pipelines for Regulated Finance
Keywords:
Data Quality Governance, Data Lineage Management, Regulated Financial Data Platforms, Regulatory Compliance in Data Management, Accuracy Completeness Consistency and Timeliness (ACCT), Data Provenance Capture, Enterprise Data Governance Frameworks, Financial Services Data Architecture, Data Monitoring and Alerting, Data Quality Assessment and Remediation, Lineage Artefacts and Documentation, Roles and Responsibilities in Data Governance, Regulatory Data Operating Models, End-to-End Data Traceability, Metadata Management Systems, Compliance-Driven Data Engineering, Trustworthy Financial Data, Enterprise Data Stewardship, Governance of Analytical Data Pipelines, Provenance-Aware Data Platforms.Abstract
Data quality and data lineage are critical concerns for organizations mandated to comply with stringent regulatory regimes. This paper analyses the latest developments in the governance of data quality and data lineage within a regulated financial services organisation. It sets out the underlying regulatory context, describes the concepts employed in the business environment, summarizes how data quality is captured and monitored, examines the artefacts that record data lineage, reviews the roles and responsibilities of staff who implement the necessary processes, and maps areas where improvements are possible.
The internal organization and processes of regulated data platforms are shaped not only by the capabilities prescribed by their technical architecture but also by the regulatory regimes under which they operate. These mandates, in particular, require rigorous examination of four aspects of data quality — accuracy, completeness, consistency, and timeliness — and detailed documentation of how data arrives in its final form in the repository. Although data monitoring, alerting, assessment, and remediation are well established, provenance capture remains an area ripe for further investment.
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