Summary
Managing data from different sources can be complicated, especially when making sure the data is reliable, well-documented, and easy to reproduce later. The authors noticed that existing approaches often handle these needs separately, making data projects harder to manage. They created a new model called UnespDataLens-RM that combines all these important parts—like governance, quality checks, and tracing data origins—into one clear framework. This model helps teams design better data pipelines from the start that are easier to govern, audit, and reproduce. It also provides guidelines and criteria that others can use to build and evaluate their own data processes.
Data engineeringData governanceData qualityData provenanceData traceabilityData pipelineReproducibilityDesign Science Research
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.