Case study • Professional AV Industry • Data & Analytics Modernization

Unified Analytics Migration Enables Scalable Governance with Microsoft Fabric

We partnered with a global professional audiovisual organization to modernize its fragmented analytics ecosystem by migrating from Azure Synapse and Power BI to Microsoft Fabric. By consolidating ingestion, transformation, modeling, and reporting into a unified platform, we established a scalable analytics foundation designed for governance, KPI consistency, and future AI-driven insights.

Success Highlights

90+ reactive pipelines standardized through reusable ingestion patterns Unified reporting foundation across certification, events, membership, and training domains Centralized KPI governance using shared semantic models and certified datasets

Key Details

Industry: Professional Audiovisual / Membership Services Geography: United States

Platform: Microsoft Fabric (unified platform for ingestion, processing, modeling, and reporting)

Business Challenge

The organization managed large-scale data operations across multiple independent systems, creating fragmented analytics workflows and inconsistent reporting.

Fragmented Data Workflows: Analytics processes operated independently across programs, regions, and Salesforce environments, limiting unified visibility.

Inconsistent KPI Definitions: Departments calculated the same metrics differently, creating reporting inconsistencies and governance gaps.

Manual Reporting Cycles: Heavy reliance on disconnected Synapse notebooks, Dataflows, and Power BI workflows increased operational complexity.

Scalability & Governance Limitations: Growing data volumes across tens of thousands of tables lacked standardized architecture, lineage tracking, and stewardship controls.

Our Solution Approach

We designed and implemented a Microsoft Fabric-based analytics modernization strategy focused on scalability, governance, and reusable data engineering patterns.

1 · Discover

Assess Analytics Fragmentation & Governance Gaps

Analyzed Azure Synapse, Dataflows, and Power BI workflows to identify KPI inconsistencies, pipeline duplication, reporting bottlenecks, and governance limitations across business domains.

2 · Consolidate

Build Unified Fabric Lakehouse Architecture

Migrated selected workloads into Microsoft Fabric and implemented a Medallion (Bronze-Silver-Gold) architecture to standardize ingestion, refinement, and business-ready reporting datasets.

3 · Automate

Enable Metadata-Driven Ingestion & Transformation

Implemented YAML-driven ingestion and transformation workflows supporting incremental and full-load processing, deduplication logic, and reusable data engineering patterns across domains.

4 · Accelerate

Establish Governance & Scalable Reporting

Enabled semantic modeling, certified datasets, lineage visibility, Git-based deployments, and standardized KPI governance to support scalable enterprise analytics and future AI initiatives.

Technical Highlights

Microsoft Fabric Lakehouse implementation using Bronze-Silver-Gold medallion architecture Metadata-driven YAML ingestion framework for scalable transformation and deduplication logic
Centralized semantic modeling layer with standardized KPI definitions and DAX measures Cross-region Salesforce data consolidation across US and Europe environments

Fabric Pipelines & Dataflows Gen2 orchestration for incremental and full-load processing Governance controls including RBAC, lineage tracking, dataset certification, and Git deployments

// Python – config = load_yaml(dataset)
data = ingest_source(config.source)
refined = apply_transformations(data, config.rules) publish_to_gold_layer(refined)
update_semantic_model()

Business Outcomes

Established a scalable analytics foundation that improved reporting consistency, governance readiness, and long-term scalability.

90+

Pipelines Standardized & Modernized:
Replaced fragmented legacy workflows with reusable Microsoft Fabric ingestion and transformation patterns across enterprise reporting systems.

40–60%

Reduction in Engineering Effort:
Metadata-driven automation significantly reduced dataset-specific pipeline development and maintenance overhead.

50%

Faster Dataset Onboarding:
Reusable ingestion frameworks accelerated onboarding of new datasets and business domains into the analytics ecosystem.

Reduced manual reporting effort by up to 60% through workflow automation and standardized reporting pipelines Unified analytics across 4+ business domains and 2 regional Salesforce ecosystems Improved KPI consistency with centralized semantic models and governance controls Enhanced scalability and maintainability of enterprise analytics workflows

Looking to Modernize Enterprise Analytics?

Let’s help you unify fragmented data workflows, standardize KPIs, and build a scalable Microsoft Fabric foundation for enterprise reporting and AI-ready analytics.

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