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Operationalizing and automating Data Governance.

Sergi Nadal1, Petar Jovanovic1, Besim Bilalli1

  • 1Database Technologies and Information Management Group, Universitat Politècnica de Catalunya - BarcelonaTech, Barcelona, Spain.

Journal of Big Data
|December 19, 2022
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Summary
This summary is machine-generated.

This study introduces a framework to automate data governance for organizations. It enhances data management by systematically integrating diverse data sources, enabling data-driven strategies.

Keywords:
Big DataData GovernanceData IntegrationMetadata

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Area of Science:

  • Data Science
  • Information Management
  • Computational Biology

Background:

  • Organizations face challenges in managing data lifecycles from multiple, evolving sources.
  • Manual or ad-hoc data governance hinders the transformation of raw data into actionable insights.
  • Semi-structured data from numerous sources complicates effective data management.

Purpose of the Study:

  • To develop a framework for operationalizing and automating data governance.
  • To enable systematic data ingestion, transformation, and integration from heterogeneous sources.
  • To facilitate the adoption of data-driven strategies through automated data management.

Main Methods:

  • Proposed a zoned data lake architecture for systematic data handling.
  • Developed data governance processes for ingestion, transformation, and integration.
  • Created metadata artifacts to automate data governance processes.

Main Results:

  • Demonstrated a cohesive framework for operationalizing and automating data governance.
  • Showcased the framework's utility in a real-world use case with the World Health Organization for Neglected Tropical Diseases data.
  • Enabled systematic data management from heterogeneous sources for business users.

Conclusions:

  • The proposed framework effectively operationalizes and automates data governance.
  • Automated data governance facilitates organizations in adopting data-driven strategies.
  • The approach addresses challenges in managing continuously evolving, semi-structured data from multiple sources.