About the Client
Supply Medium is a technology-driven organization specializing in modern data, cloud, and analytics solutions. With a growing client base and expanding digital operations, the company relies on scalable data platforms to deliver reliable insights and support business growth.
Background
As Supply Medium expanded its operations and data ecosystem, its legacy on-premises data systems began showing limitations. Data from marketing, finance, sales, and operational platforms existed in separate silos, connected through fragile ETL processes that frequently failed. Reports were slow, inconsistent, and often lacked a complete business view.
This meant teams couldn’t gain end-to-end visibility into business performance, understand how campaigns influenced customer engagement, or analyze operational trends in real time. Instead, they relied on static, outdated reports that limited agility and data-driven decision-making.
Challenge
The limitations of the legacy on-premises environment became increasingly apparent:
- Data Silos: Disparate systems made it difficult to achieve a unified view of business operations.
- Unreliable ETL: Manual data pipelines frequently failed, delaying reporting and insights.
- No Real-time Analytics: Teams couldn’t respond quickly to operational changes or customer behavior.
- Scalability Issues: Existing infrastructure struggled to support growing data volumes and increasing workloads.
- High Costs: Maintaining legacy systems required significant time, resources, and specialized expertise.
Solution
Supply Medium partnered with our team to modernize its data ecosystem on Microsoft Azure, creating a unified cloud platform capable of processing large-scale, real-time data workloads.
Key Components Included
Unified Data Ingestion (ELT)
- Azure Data Factory replaced legacy ETL processes, ingesting data from multiple business systems into a centralized platform.
Central Data Lake
- Azure Data Lake Storage Gen2 became the single source of truth, providing scalable and secure storage for structured and unstructured data.
Metadata Management
- Azure Purview automatically discovered, classified, and cataloged enterprise data, enabling governance, lineage tracking, and easier data discovery.
Modern Data Warehouse
- Azure Synapse Analytics transformed raw data into curated warehouse layers and optimized data marts for high-performance analytics.
Department-Specific Data Marts
- Dedicated data marts for Marketing, Finance, Sales, and Operations provided clean, business-ready datasets tailored to each team’s reporting needs.
Real-time Analytics
- Azure Databricks and Azure Stream Analytics processed live data streams, enabling near real-time monitoring and faster business decisions.
Interactive Dashboards
- Power BI dashboards connected directly to Azure Synapse Analytics, delivering real-time insights and self-service reporting for stakeholders.
Outcome
- A unified Azure platform created a single source of truth, eliminating data silos across the organization.
- Real-time insights enabled faster decision-making, improved operational efficiency, and better business performance.
- Automated ELT pipelines reduced manual reporting effort by 70%, cutting reporting time from days to hours.
- Self-service analytics through Azure Synapse Analytics and Power BI improved productivity while minimizing dependency on manual reporting.
- The cloud architecture scaled seamlessly to support growing business demands and over 2PB of data stored in Azure Data Lake.
- Azure Databricks enabled advanced analytics, predictive modeling, personalized insights, and new opportunities for business innovation.