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Data Governance and Hybrid Cloud Flexibility

| Moaz Mirza |

Data Governance

YouTube

Anchor

Spotify

Apple

Episode #284

Introduction

In episode 284 of our SAP on Azure video podcast we talk about Data Governance & Multi-cloud flexbility.

Find all the links mentioned here: https://www.saponazurepodcast.de/episode284

Reach out to us for any feedback / questions:

#Microsoft #SAP #Azure #SAPonAzure #Purview #Data #Governance #MultiCloud

Summary created by AI

  • Data Governance and Multi-Cloud Flexibility Overview:
  • Holger, Goran, and Moaz discussed the challenges and opportunities of data governance and multi-cloud flexibility, highlighting Moaz’s open-source modular solution for managing data assets across multiple cloud providers and compliance domains, with a focus on SAP and Microsoft environments.
    • Challenges of Data Silos: Moaz described the common issues faced by organizations, such as data silos and disconnected operations, using the example of a fictional data governance officer, Mark Spencer, at Contoso. These challenges stem from information overload and the evolution of technology, leading to difficulties in managing and controlling data across domains.
    • Opportunities in Data Governance: The team emphasized that the current data overload presents both challenges and opportunities, with the need for governance visibility and management across multiple data centers and cloud vendors. Moaz explained the importance of having a single pane of glass for compliance and management.
    • Modular Solution Approach: Moaz introduced his open-source repository, which offers a modular, cross-domain solution for data governance and hybrid cloud flexibility. The solution is likened to Lego blocks, allowing organizations to build and customize their governance framework based on their needs, covering multiple compliance domains and cloud providers.
    • Scope and Components: The solution spans 12 Azure services, about 40 solution components, and can scale across multiple cloud providers using Azure Arc. It supports ingestion from nine different data sources, including SAP S/4HANA, Azure SQL, and other cloud storage and compute resources, and is capable of handling millions of governed assets.
  • Agentic AI and Copilot Integration for Data Governance:
  • Moaz demonstrated how agentic AI, including Copilot agents and GPT-based LLMs, are integrated into the solution to automate data governance tasks, such as compliance checks, residency hydration, and enforcement actions, providing conversational interfaces for governance officers like Mark.
    • AI Agents and Automation: The solution includes two AI agents: a Fabric Data Agent and a Copilot Studio agent, which orchestrate and enforce compliance actions. These agents automate tasks such as auditing, reporting, and updating compliance information, reducing manual effort and improving efficiency.
    • Residency Hydration and Enforcement: Moaz explained the process of residency hydration, where the agent checks for missing or mismatched residency information in data products, validates actual resource regions, and offers automated updates to Purview metadata. The workflow involves Azure Functions, Power Automate flows, and REST API calls.
    • Confidential Computing Compliance: The team discussed how the solution identifies data products with personally identifiable information (PII) that are not running on confidential compute resources. The agent provides compliance scores, lists non-compliant products, and enables tagging and notification workflows for investigation and enforcement.
    • Technical Workflow and Integration: Moaz detailed the technical workflow, including ingestion of metadata into Purview, transformation in Fabric notebooks, mapping via data product ID tags, and dashboard hydration for compliance reporting. The integration pattern leverages Azure policies, tagging, and modular components for extensibility.
  • Implementation Guidance and Efficiency Gains:
  • Holger, Goran, and Moaz discussed practical steps for implementing the solution, emphasizing the importance of identifying pain points, setting up foundational infrastructure, and leveraging the GitHub starter kit to achieve significant efficiency gains in data governance across domains.
    • Getting Started Recommendations: Moaz recommended starting by identifying the major pain points among the five knowledge domains (e.g., patching, residency, confidential computing) and focusing implementation efforts accordingly. He advised ensuring foundational components like data sources, Azure landing zones, Arc onboarding, and policy enforcement are in place.
    • Modular and Team-Based Approach: The team highlighted the modular nature of the solution, which allows organizations to build incrementally and collaborate across analytics, ETL, and governance teams. The approach supports stacking components and integrating with existing pipelines.
    • Efficiency and Time Savings: Moaz shared research indicating that using the AI-driven template and starter kit can save 40-80% of overall effort compared to manual or traditional programming approaches, especially when dealing with large-scale data and multiple cloud providers.
    • Ease of Use and Accessibility: The solution is designed to be accessible, not requiring deep expertise in Python, Fabric, or Purview. Users can leverage templates, Copilot assistance, and click-through interfaces to implement governance workflows, making it suitable for pilot projects and scalable for enterprise use.

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