UnespDataLens-RM: A Reference Model for Analytical Data Engineering with Governance, Quality, Provenance, and Reproducibility

Ronaldo Celso Messias Correia; Douglas Francisquini Toledo; Camila Tolin Santos da Silva

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arXiv · 2609.08184

UnespDataLens-RM: A Reference Model for Analytical Data Engineering with Governance, Quality, Provenance, and Reproducibility

Ronaldo Celso Messias Correia·Douglas Francisquini Toledo·Camila Tolin Santos da Silva

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Abstract

The growing reliance on data in analytical processes and evidence-based decision-making has reinforced the importance of Data Engineering in building pipelines capable of integrating, transforming, validating, and delivering data from heterogeneous sources. However, the reliability of analytical assets depends not only on data processing capabilities but also on mechanisms for governance, quality assurance, provenance, traceability, versioning, and reproducibility throughout their lifecycle. These responsibilities are commonly addressed by different models, frameworks, and operational practices, resulting in methodological fragmentation across the analytical data lifecycle. To address this gap, this article proposes UnespDataLens-RM, a technology-independent reference model that integrates technical-operational processes and cross-cutting capabilities within a unified structure for Analytical Data Engineering. The model aims to support the specification, organization, and evolution of analytical pipelines by incorporating governance, quality, provenance, traceability, and reproducibility from the design stage. Developed following the Design Science Research approach, UnespDataLens-RM comprises eight technical-operational modules, eight cross-cutting modules, complementary dimensions, and a formalized set of artifacts, metrics, and validation criteria. The resulting specification offers a conceptual and methodological framework for future instantiations and empirical evaluations of analytical pipelines designed to be more governable, documented, traceable, auditable, and reproducible.

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Ronaldo Celso Messias Correia, Douglas Francisquini Toledo, Camila Tolin Santos da Silva. 2026-09-08. UnespDataLens-RM: A Reference Model for Analytical Data Engineering with Governance, Quality, Provenance, and Reproducibility. https://arxiv.org/abs/2609.08184

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