About Client:
A renowned global hotel group with an extensive portfolio of luxury resorts and business hotels, managing millions of guest interactions each year across multiple destinations.
The company operates a complex digital ecosystem supporting reservations, guest services, loyalty programs, and operational analytics. As its data-driven initiatives continued to expand, the organization required stronger governance capabilities and modern data lineage tools within its Azure environment to maintain data quality, transparency, and trust across its analytics platform.
Background:
The client had already adopted Microsoft Azure as its core data platform. Data from Property Management Systems, Central Reservation Systems, CRM platforms, and digital engagement tools flowed into Azure Data Lake, Azure Synapse Analytics, and Power BI dashboards.
Azure Data Factory and Databricks were used to orchestrate and transform data for reporting, operational analytics, and guest experience optimization. This cloud-first architecture provided the scalability and flexibility required to support the organization’s growing analytics needs.
However, as data pipelines, sources, and analytical use cases expanded, one critical governance capability struggled to keep pace: data lineage. Teams lacked clear end-to-end visibility into how data moved across the environment. Without an effective Azure data lineage solution, tracing where datasets originated, how they were transformed, and where they were ultimately consumed became increasingly difficult.
Challenge:
Lack of Data Visibility: Teams could not easily trace data back to its source or understand how it had been transformed, making it harder to validate and trust business metrics.
Slow Impact Analysis: Identifying which datasets, downstream processes, or reports would be affected by source changes or pipeline failures could take days.
Compliance Risk: Limited visibility into how personal and sensitive information moved through the data ecosystem created challenges in supporting GDPR and CCPA compliance requirements.
Low Data Literacy: Business users often lacked clear definitions and context around datasets and metrics, increasing the risk of inconsistent interpretation and usage.
Manual Metadata Management: Data definitions and documentation were distributed across PDFs, spreadsheets, and multiple systems, making metadata difficult to maintain and keep consistent.
Complex Troubleshooting: Investigating report discrepancies required extensive coordination between data engineering, analytics, and business teams, slowing root cause analysis and resolution.
These challenges highlighted the need for an enterprise-grade governance framework using modern Azure data lineage capabilities to automatically discover assets, capture lineage, and improve transparency across the organization’s data ecosystem.
Solution:
To address these challenges, Supply Medium implemented a comprehensive data lineage and governance framework using Azure Purview, integrated with the client’s existing Azure data architecture.
The implementation established Azure Purview as the organization’s centralized data governance and lineage solution, enabling automated data discovery, metadata management, classification, lineage tracking, and governance across the broader data estate.
Key Steps:
Centralized Data Catalog: Azure Purview was configured to scan and register data assets across Azure and connected on-premises systems, creating a centralized source of truth for technical metadata and data discovery.
Automated Lineage Tracking: Integrations with Azure Data Factory, Databricks, and Synapse Analytics enabled automated lineage capture across pipelines, transformations, and analytical workloads. Custom connectors were implemented where required to support more complex processing jobs.
Business Glossary & Ownership: A centralized business glossary connected business-friendly terminology with underlying technical data assets. Ownership and stewardship responsibilities were assigned to strengthen governance accountability.
Data Classification & Compliance: Purview’s automated classification capabilities were used to identify sensitive information, including PII and payment-related data, helping teams apply appropriate privacy, security, and governance controls.
Self-Service for Business Users: Business stakeholders gained the ability to search for datasets, review definitions, understand ownership, and visually explore lineage without relying on IT teams for routine data discovery.
End-to-End Visibility with Power BI: Integration with Power BI extended lineage visibility from upstream data sources and transformations through to reports and dashboards, simplifying impact analysis and troubleshooting.
Outcome:
Improved Data Trust: End-to-end visibility from source systems through transformations to Power BI dashboards gave stakeholders greater confidence in the accuracy and reliability of business data.
60% Faster Issue Resolution: Visual lineage graphs accelerated root cause analysis, helping teams identify upstream and downstream dependencies faster and significantly reduce troubleshooting time.
Increased Data Literacy: Clear business definitions, ownership information, and visual lineage helped business users better understand datasets and use analytics more confidently.
Stronger Compliance & Auditability: Transparent views of sensitive and personal data flows improved traceability and simplified support for GDPR and CCPA compliance requirements.
Faster Impact Analysis: Teams could quickly identify which pipelines, datasets, and reports were affected by system changes, reducing risk and improving change management.
Accelerated Innovation: Clear ownership, trusted metadata, and improved lineage visibility enabled teams to develop and deploy new analytics and data solutions with greater speed and confidence.