About the Client
The client is a leading global gaming company with a portfolio of blockbuster titles enjoyed by millions of active players worldwide. Operating at significant scale, the company relies on data across player activity, marketing, finance, sales, and game performance to continuously improve experiences and drive sustainable growth.
Background
As the company expanded its game portfolio and global player base, its legacy on-premises data infrastructure struggled to keep pace with growing data volumes and increasingly complex analytics requirements.
Data from marketing, finance, sales, and in-game telemetry remained distributed across disconnected systems. Fragile ETL processes were used to bring this information together, but frequent failures resulted in delayed, inconsistent, and incomplete reporting.
Without a unified view of the player journey, teams found it difficult to understand how marketing campaigns influenced engagement, how new features affected in-game spending, or how player behavior changed in real time. Reliance on static and outdated reports further limited the company’s ability to respond quickly and innovate in a fast-moving gaming market.
The Challenge
The limitations of the legacy on-premises environment created several critical challenges:
Data Silos: Disconnected systems prevented teams from building a complete and consistent view of players and overall business performance.
Unreliable ETL Processes: Legacy and manual data pipelines frequently failed, creating reporting delays and increasing operational overhead.
Limited Real-Time Analytics: Teams lacked immediate visibility into player behavior, game telemetry, and campaign performance, making rapid decision-making difficult.
Scalability Constraints: Existing infrastructure struggled to efficiently process the growing volume of data generated by millions of daily active users.
High Operational Costs: Maintaining aging infrastructure required significant time, resources, and specialized technical expertise.
To overcome these limitations, the client partnered with Supply Medium to modernize its data ecosystem on Microsoft Azure and establish a scalable foundation for real-time analytics.
The Solution
Supply Medium designed and implemented a unified, cloud-based data platform on Azure capable of processing large-scale, real-time data while improving reliability, governance, accessibility, and analytics performance.
The modern architecture brought previously fragmented data sources into a centralized ecosystem and provided teams with faster access to trusted, actionable insights.
Unified Data Ingestion
Azure Data Factory replaced fragile legacy ETL processes with automated ELT pipelines, ingesting data from multiple business and gaming sources into a centralized cloud environment.
This improved pipeline reliability while reducing the manual effort required to manage data movement.
Centralized Data Lake
Azure Data Lake Storage Gen2 was implemented as a scalable central repository for raw data, establishing a reliable single source of truth across the organization.
The architecture provided the flexibility required to support growing data volumes and a wide range of analytics workloads.
Data Governance & Metadata Management
Microsoft Purview was used to automatically discover, classify, and catalog data across the environment.
This strengthened governance, improved data lineage visibility, and made trusted datasets easier for analysts and business teams to discover and understand.
Modern Data Warehouse
Azure Synapse Analytics transformed raw data into curated warehouse layers and optimized data models designed for high-performance analytics and reporting.
This enabled teams to query large datasets efficiently while maintaining consistent data definitions across the organization.
Department-Specific Data Marts
Dedicated data marts were created for teams including Marketing, Product, and Finance, giving each department access to clean, relevant, and actionable datasets tailored to its reporting and analytical requirements.
Real-Time Analytics
Azure Databricks and Azure Stream Analytics enabled real-time processing of live gaming telemetry.
Developers and product teams gained immediate visibility into player activity and game performance, allowing them to identify trends, detect issues, and respond to behavioral changes more quickly.
Interactive Business Intelligence
Power BI dashboards connected to the modern Azure data environment, giving business leaders and operational teams access to interactive, up-to-date insights.
Self-service analytics reduced reliance on static reporting and enabled stakeholders to explore performance data more efficiently.
The Outcome
The modern Azure data ecosystem transformed how the company collected, processed, analyzed, and acted on data across its global gaming operations.
A centralized source of truth eliminated key data silos and provided teams with a more complete view of the player journey.
Real-time analytics enabled faster responses to player behavior, campaign performance, and operational issues, contributing to stronger player engagement, improved retention, and more effective marketing decisions.
Automated ELT pipelines reduced manual reporting effort by 70%, while reporting cycles that previously took days were reduced to hours.
The platform scaled effectively to support millions of daily active users and approximately 2 PB of data within Azure Data Lake.
With Azure Databricks and the broader modern data architecture in place, the client also established a foundation for predictive analytics, personalized player experiences, advanced behavioral modeling, and new revenue opportunities.