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
  • Unified Analytics Migration Enables – Scalable Governance with Microsoft Fabric
  • 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_config(dataset)
raw_data = ingest_source(config.source, config.load_type)
refined_data = apply_rules(raw_data, config.transformations)
curated_data = merge_and_dedupe(refined_data, config.primary_keys)
publish_to_gold_layer(curated_data)
refresh_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.
Speak with an expert