Approaches to Digital Estate Planning in Microsoft Fabric

Digital estate planning in Microsoft Fabric requires an approach tailored to modern, SaaS-based, domain-oriented data platforms. The planning must account for both traditional infrastructure metrics and new architectural elements like capacities, workspaces, semantic models, and Lakehouse structures.

1. Domain-Driven (Workload-Based) Approach

This top-down approach focuses on the business and data product level. You evaluate each domain or business unit's responsibilities and analyze:

• Data sensitivity and compliance (using Microsoft Purview classification)
• Data flow complexity and dependencies (across Pipelines, Shortcuts, Notebooks)
• Expected usage patterns and capacity needs (e.g., refresh frequency, concurrency)
• Lifecycle expectations and semantic reuse across workspaces
• Access control requirements and tenant segmentation

This approach requires deep engagement with business stakeholders, domain owners, and platform teams. It is essential for prioritizing Fabric-native adoption scenarios (e.g., live analytics, governed self-service BI).

💡 Tip: This method is excellent for targeting high-value use cases but requires well-prepared interviews and dependency mapping. Use it to assess transformation potential before planning technical migration.

2. Asset-Centric Approach

This approach starts from the technical inventory—analyzing the foundational components that could map to Fabric workloads:

• Power BI datasets and reports (with usage metrics)
• SQL databases (schema, volume, performance)
• Data pipelines (control flow, orchestration, triggers)
• Azure Storage or on-prem sources (source system inventory)
• SSAS, SSIS, and legacy data models

Assets are assessed for compatibility, modernization potential, and migration blockers. Use this method for early licensing, capacity planning, and cost estimation.

💡 Tip: Use Fabric Capacity Metrics App and Power BI Tenant Activity Logs to extract initial usage and adoption baseline. This helps create early models for CU sizing and storage forecasting.

3. Iterative & Incremental Planning

The most effective planning approach for Fabric is incremental, aligned with product-based delivery:

• Initial baseline: Use the asset-driven method to quantify worst-case costs (storage, compute, license).
• Backlog creation: Work with domain owners and data architects to build a backlog of priority use cases.
• Release cycles: Prioritize small batches (5–10 data products) for pilot or wave-based delivery.
• Pre-implementation sizing: Reassess asset complexity and CU sizing just before implementation.

This method enables fast value generation, early stakeholder buy-in, and reduced risk through progressive elaboration.

💡 Tip: Document all assumptions (e.g., refresh frequency, user concurrency, capacity modes) and validate them with real Fabric telemetry during pilot phases.