Transition from Power BI to Microsoft Fabric, guided by specialists in retail, convenience chain, fuel, and energy data ecosystems.
Talk to our expertsStop waiting 60–75 minutes for overnight reports. Fabric's Medallion Lakehouse eliminates the resource contention that locks reporting during peak hours, so store managers open to live sales, shrink, and transaction data, and pricing teams work from fuel margin figures that reflect what's happening right now, not last night.
One governed version of item-360, store-360, promotion performance, and mix-n-match analytics built on a single source of truth every team draws from. No more reconciling three versions of the same figure. No more waiting on IT for a promotional uplift extract. Self-service on data you can trust.
Migrating to Fabric moves your data into a governed, unified Lakehouse on OneLake. That architecture is what makes downstream AI initiatives viable: Copilot in Power BI, merchandise demand forecasting, fuel throughput prediction, and loyalty personalization all depend on clean, trusted, centrally governed data to produce reliable outputs.
Refresh delays and report contention in retail and fuel environments are rarely a Power BI problem. They are an architecture problem. Source systems like PDI Enterprise, PDI FP, Excentus, PriceAdvantage, Fiserv, COMDATA, and UKG were never designed to feed a single BI layer at scale. Tightly coupled datasets create contention when multiple users query simultaneously. Departments pulling from different sources produce conflicting figures by design.
Our migration fixes this at the data layer, not the report layer. We move your data into a governed Medallion Lakehouse on OneLake, structured around the entities your business runs on: Store 360, Item 360, Site 360, Customer 360, and Fuel Performance. Your existing reports repoint to this foundation rather than being rebuilt.
Datasets, reports, dashboards, and Premium features mapped to Fabric's Lakehouse, warehouse, and dataflow architecture with full functionality preserved.
Workspace permissions, row-level security, and compliance policies reconfigured within Fabric's Purview framework. Your security model moves with your data.
DAX measures, custom visuals, Power Query dataflows, and ETL pipelines migrated with data integrity and performance validated at every step.
Fabric compute, storage, and query performance optimized for the concurrency and transaction volumes typical in multi-site retail and fuel environments.
Teams trained before cutover. Dashboard continuity maintained so end users experience improvement, not disruption.
PHASE 1
Weeks 1–2
Connect your data to Fabric without touching a single report.
Your data flows into Fabric. Your team notices nothing yet, by design.
PHASE 2
Weeks 3–4
Fix the architecture. Keep the reports.
Refresh times drop from 60–75 minutes to near-real-time. Same reports, faster and consistent.
PHASE 3
Weeks 5–6
One version of every number, for every team.
Category, finance, and operations all draw from the same governed data. IT extract requests stop.
PHASE 4
Weeks 7–8
Reconciled, enabled, and ready for what comes next.
A governed Lakehouse your business trusts, with Copilot active and the foundation ready for demand forecasting, loyalty personalization, and fuel prediction.
PHASE 1
Weeks 1–2
Connect your data to Fabric without touching a single report.
Your data flows into Fabric. Your team notices nothing yet, by design.
PHASE 2
Weeks 3–4
Fix the architecture. Keep the reports.
Refresh times drop from 60–75 minutes to near-real-time. Same reports, faster and consistent.
PHASE 3
Weeks 5–6
One version of every number, for every team.
Category, finance, and operations all draw from the same governed data. IT extract requests stop.
PHASE 4
Weeks 7–8
Reconciled, enabled, and ready for what comes next.
A governed Lakehouse your business trusts, with Copilot active and the foundation ready for demand forecasting, loyalty personalization, and fuel prediction.
PHASE 1
Weeks 1–2
Connect your data to Fabric without touching a single report.
Your data flows into Fabric. Your team notices nothing yet, by design.
PHASE 2
Weeks 3–4
Fix the architecture. Keep the reports.
Refresh times drop from 60–75 minutes to near-real-time. Same reports, faster and consistent.
PHASE 3
Weeks 5–6
One version of every number, for every team.
Category, finance, and operations all draw from the same governed data. IT extract requests stop.
PHASE 4
Weeks 7–8
Reconciled, enabled, and ready for what comes next.
A governed Lakehouse your business trusts, with Copilot active and the foundation ready for demand forecasting, loyalty personalization, and fuel prediction.
PHASE 1
Weeks 1–2
Connect your data to Fabric without touching a single report.
Your data flows into Fabric. Your team notices nothing yet, by design.
PHASE 2
Weeks 3–4
Fix the architecture. Keep the reports.
Refresh times drop from 60–75 minutes to near-real-time. Same reports, faster and consistent.
PHASE 3
Weeks 5–6
One version of every number, for every team.
Category, finance, and operations all draw from the same governed data. IT extract requests stop.
PHASE 4
Weeks 7–8
Reconciled, enabled, and ready for what comes next.
A governed Lakehouse your business trusts, with Copilot active and the foundation ready for demand forecasting, loyalty personalization, and fuel prediction.
A leading U.S. fuel and convenience retailer migrated from Power BI to Microsoft Fabric, implementing a Medallion Lakehouse architecture and integrating PDI FP data. Existing Power BI reports were repointed with minimal rework, creating a governed data foundation for unified store, product, and customer analytics while enabling AI-ready insights.
Read the full storyReduced reporting time by enabling teams to analyze promotions and item performance in near real time.
Consolidated multiple systems into a single platform, eliminating silos and improving data accessibility.
Established a governed data foundation to ensure accuracy, consistency, and readiness for AI use cases.
