From Fragmented data to safe, scalable value Architecture.

Current state and proposed target

A global enterprise needs to enable AI across Engineering, HR, and Sales with consistent security, governance, and adoption.

Current state

Data silos across business units and regions.
Security gaps and inconsistent permissions.
No unified governance or lineage tracking.
Local AI pilots without an enterprise strategy.

Proposed target

Microsoft Purview: Discover, classify, and catalog data; track ownership and supported lineage.
Azure Landing Zones and Microsoft Entra: Establish infrastructure guardrails and consistent identity and access controls.

The target is a governed data estate, not an immediate migration of every source. Business domains retain responsibility for their data under shared policies.

Priority Requirements and Blockers

Address access controls, data accountability, and operating capacity before expanding AI use cases.

Non-functional requirements

Security and identity / Critical: Use Entra ID, RBAC, data protection, and Landing Zone policies to enforce consistent access.
Data governance and quality / Critical: Use Purview for cataloging, classification, and lineage. Assign owners and define data-quality checks.
Scalability and reliability / High: Use Fabric for governed integration, with monitoring and processing capacity appropriate to workload growth.

Anticipated blockers and mitigation

Fragmented data ownership: Unclear accountability reduces data quality. Assign domain owners and data stewards.
Inconsistent security controls: Unauthorized access creates regulatory exposure. Establish shared security baselines and access policies.
Organizational resistance: Limited adoption leaves AI initiatives disconnected. Use federated governance and shared success metrics.

Architectural trade-offs and decisions

Centralize policy, retain domain ownership, and integrate data selectively.

Centralized governance vs. business agility

Central controls improve consistency but can slow innovation.

Decision: Define policies centrally; delegate data ownership and implementation to business domains.

Data consolidation vs. data federation

Moving every source increases migration cost, complexity, and disruption.

Decision: Use Fabric to integrate priority data without requiring immediate migration of all sources.

Speed of adoption vs. enterprise readiness

Rapid pilots can create disconnected solutions and governance gaps.

Decision: Establish minimum security and governance controls, then expand through measurable use cases.

Integration depends on source capabilities, permissions, connectivity, and regulatory restrictions. Validate these before choosing migration or federation for each dataset.

Initiatives and Milestones

Connect the platform implementation to data ownership, operating responsibilities, and AI adoption.

01

Establish the secure foundation

Apply Azure Landing Zone guardrails and Entra access controls. Configure Purview discovery, classification, and lineage for supported sources.

Milestone: Security baseline approved; pilot sources cataloged and access controls tested.

02

Build the governed data foundation

Use Fabric to integrate priority domains. Assign owners and provide datasets with documented quality and access requirements.

Milestone: A priority dataset is available through an approved integration with ownership and lineage documented.

03

Establish the AI operating model

Define responsibilities across security, data owners, platform operations, and business teams. Agree how use cases are approved and evaluated.

Milestone: Responsibilities, approval process, and shared adoption metrics agreed.

Start ownership and operating-model work alongside the secure foundation. Expand data integration and AI adoption after the pilot meets the agreed criteria.

First two weeks: one use case and one secured integration

Limit the pilot to accessible sources. Confirm permissions, connectivity, and platform availability before implementation.

Period	Action	Deliverable
Week 1	Approve one priority AI use case. Assess data sources, sensitivity, ownership, and access requirements.	Data inventory, ownership map, and baseline KPIs.
Week 2	Implement a limited, secured integration using Fabric and Purview. Validate technical feasibility.	Working data integration with documented access controls, classification, and lineage.

Proposed success criteria

One business use case prioritized and approved.
Every pilot dataset has an identified owner.
Every pilot access path tested against defined authorization requirements.
End-to-end lineage documented for the pilot flow, including unsupported automated segments.
Initial data-quality and operational metrics established.

Assess data readiness before expanding. The pilot is a data integration, not a production AI deployment.