From Dashboards to Decisions: A Microsoft Fabric and Copilot-Enabled Enterprise Analytics Governance Framework for Saudi Vision 2030

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@article{1722961,
      author = {Choudhry Bilal Mazhar Hussain},
      title = {From Dashboards to Decisions: A Microsoft Fabric and Copilot-Enabled Enterprise Analytics Governance Framework for Saudi Vision 2030},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {1023-1033},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722961.pdf},
      abstract = {Enterprises are moving from descriptive dashboards toward governed, AI-assisted decision environments. Microsoft Fabric unifies data engineering, warehousing, real-time analytics and business intelligence on OneLake, while Microsoft Copilot introduces natural-language decision support across the analytics lifecycle. Yet unified data and generative AI do not automatically produce trustworthy decisions. This paper develops and validates by case application a design-science framework that connects Fabric and Copilot with Saudi Arabia's National Data Management Office (NDMO), National Data Index (NDI), Personal Data Protection Law (PDPL), and Vision 2030 priorities. The framework contributes to a five-layer architecture, a governance-to-decision maturity model, a Copilot-in-the-loop workflow, a capability-to-control mapping, and decision-ready KPIs. Practice-based validation is provided through an ARASCO enterprise analytics case involving existing Power BI, staging SQL database, and governance-dashboard contexts. The central argument is that enterprise value is created not by dashboards or generative AI alone, but by governing the path from question to validated decision through lineage, sensitivity labeling, role-based access, human decision rights and audit evidence.},
      keywords = {Data Governance; Decision Support; Enterprise Analytics; Microsoft Copilot; Microsoft Fabric; Responsible AI},
      month = {September},
      doi = {https://doi.org/10.64388/IREV10I3-1722961}
  }