Illustrative example — not a real client engagement. This case study is a placeholder used to demonstrate structure and depth while real, permissioned case studies are gathered.
- Client
- Confidential — retail sector
- Role
- Data & Solution Architect
- Duration
- 4 months
The Challenge
Store-level sales and inventory reporting lived in a mix of spreadsheets, a legacy data warehouse, and point Power BI reports with no shared semantic layer — so different teams routinely presented different numbers for the same metric in leadership meetings.
The Approach
Designed a Microsoft Fabric lakehouse as the single landing and transformation layer for point-of-sale, inventory, and finance extracts, with a governed set of Power BI semantic models sitting on top as the one source of truth for core metrics (sales, margin, stock cover).
Integration Approach
Used Fabric data pipelines to ingest from the existing POS and ERP exports on a nightly schedule, with dataflows handling the transformation logic that had previously lived in ungoverned spreadsheets. Row-level security in the semantic model let regional managers see only their own stores without duplicating models per region.
Implementation Challenges
Reconciling historical definitions of "sales" and "margin" across teams took longer than the technical build — several metrics had silently drifted in meaning over time. Establishing a short data-definition sign-off process with stakeholders before building the model prevented rework later.
The Outcome
Illustrative outcome: in a project like this, moving to one governed semantic layer typically eliminates the "whose numbers are right" conversation in leadership reporting and meaningfully cuts the manual spreadsheet work previously needed to produce weekly reports.
Lessons Learned
Resolve metric definitions with the business before modeling, not during it. A single well-governed semantic model with row-level security scales better across regions than maintaining near-duplicate reports per region.
Technologies
Microsoft FabricPower BIFabric Data PipelinesDataflows Gen2Azure Data Lake Storage
This is an illustrative example — not a real client engagement. It is included to show the structure, depth, and type of architecture work involved in a project like this, while real case studies are gathered and published.