Self‑Serving Data Marts Orchestrated by Auto ML-Governed Pipelines

Self-serving data marts orchestrated through AutoML-governed pipelines mark a significant advancement in enterprise analytics democratization. This architectural approach creates domain-specific information repositories with automated data preparation, feature engineering, and model development functions accessible to business users without deep technical knowledge. The governance framework applies automated quality controls, lineage tracking, and access management, ensuring data integrity throughout analytical processes. Integration with existing data warehouse systems maintains centralized governance while enabling distributed analytical capabilities, addressing specific business needs. Implementation factors include metadata standardization, processing resource allocation, and organizational change management supporting effective usage. Technical elements comprise automated data profiling, dynamic transformation creation, and continuous quality monitoring throughout pipeline operation. The orchestration layer manages complex workflows while implementing appropriate error handling and recovery mechanisms. Enterprises implementing these frameworks report significant enhancements in analytical responsiveness, resource utilization effectiveness, and business coordination compared to conventional centralized models. This balanced approach resolves conflicting requirements between governance standardization and analytical adaptability, establishing durable foundations for growing self-service functions while preserving appropriate supervision across increasingly intricate data landscapes.

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The authors declare no conflict of interest.

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The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

This work did not receive any external funding.