Data Modernization Assessment
Assess whether your data estate can support trusted analytics, Microsoft Fabric, Power BI, and governed AI without carrying forward legacy complexity.
Is your data estate ready for Fabric and AI?
This assessment is for CIOs, data and analytics leaders, platform owners, architects, and business intelligence teams before a Fabric rollout, warehouse or lake migration, Power BI consolidation, AI initiative, major source-system change, or renewal of legacy platforms. It is useful when data moves through fragile pipelines, reports disagree, refreshes fail, ownership is unclear, capacity cost is rising, or teams cannot safely reuse data for analytics and AI.
We review prioritized source systems, databases, files, and APIs; ingestion and orchestration; Azure Data Factory and Fabric Data Factory; OneLake, lakehouse, and warehouse patterns; Azure SQL and selected Synapse workloads; semantic models, Power BI workspaces, and gateways; real-time requirements; security and identity; data quality, lineage, and ownership; Microsoft Purview and OneLake Catalog governance; capacity and operations; and Copilot in Fabric readiness where licensed and in scope.
You receive a Data Modernization Assessment Report, current-state architecture and data-flow map, source and pipeline inventory, platform-fit decision matrix, data quality and governance gap register, workload and migration waves, target-state recommendations, and a 90-day roadmap. Sample findings may include duplicated ingestion, unsupported gateways, brittle pipelines, unowned semantic models, inconsistent business definitions, excessive data copies, missing sensitivity controls, refresh bottlenecks, or Fabric capacity without workload and cost governance.
Our process
Turn data platform complexity into a modernization roadmap
We trace selected business data products from source through ingestion, transformation, storage, semantic modeling, reporting, access, lineage, and operations. Recommendations are tied to business outcomes, data quality, security, performance, cost, migration effort, and ownership rather than assuming every workload belongs on one platform.
Review current data estate
We inventory priority sources, databases, files, APIs, pipelines, storage, gateways, semantic models, reports, refresh schedules, capacities, owners, service expectations, and operational incidents. Inputs include architecture and data-flow diagrams, platform inventories, lineage evidence, pain points, usage and capacity data, and business priorities.
Current state
Assess Microsoft data platform fit
We compare Fabric Data Factory, Data Engineering, Data Warehouse, Real-Time Intelligence, OneLake, Power BI, Azure Data Factory, Azure SQL, selected Synapse patterns, and existing platforms against workload needs. Decisions consider capability, migration effort, integration, performance, resilience, licensing, capacity, and operating skills.
Platform fit
Identify governance and quality gaps
We sample data quality rules, business definitions, lineage, ownership, workspace roles, row- and object-level security, sensitivity labels, sharing, retention, gateway security, monitoring, and Purview or OneLake Catalog coverage. The review identifies where weak governance undermines trust, reuse, compliance, or AI readiness.
Governance
Build modernization roadmap
We sequence quick wins, pilots, platform foundations, migration waves, semantic-model changes, governance controls, cutover, and decommissioning. Each roadmap item includes business outcome, dependency, owner, risk, acceptance evidence, and the recommended target platform.
Roadmap
OUR WORK
Duration, inputs, and next steps
A focused assessment typically takes three to four weeks after scope and access are confirmed. Participants usually include an executive or data sponsor, data platform owner, data and solution architects, analytics and Power BI leads, security and governance representatives, source-system owners, and operations staff. Provide architecture and data-flow diagrams, source and pipeline inventories, workspace and semantic-model inventory, gateway and refresh history, capacity and cost data, data quality issues, security and classification standards, business priorities, and read-only access or agreed exports. Follow-on work may include a Microsoft Fabric pilot or migration, OneLake and medallion architecture, Data Factory modernization, Power BI semantic-model governance, Purview integration, Copilot readiness, or managed data-platform operations.
