Confidential (Enterprise Manufacturing Company)
The Challenge
The client managed large volumes of data across ERP, MES, CRM, production systems, and legacy databases. Business teams relied on siloed data sources and manual reporting processes, resulting in inconsistent metrics, slow report generation, and limited visibility into operational performance. As data volumes continued to grow, the existing infrastructure struggled to support scalable analytics, making it difficult for leadership to access timely insights for production planning, inventory optimization, and business performance monitoring.
Our Solution
Databriva modernized the client's data platform by implementing a cloud-based architecture using Azure Data Lake, Azure Data Factory, Azure SQL Database, and Microsoft Power BI. Data from multiple enterprise systems was ingested into Azure Data Lake through automated ETL pipelines, where it was cleansed, standardized, and transformed into analytics-ready datasets. A centralized semantic model was developed to support enterprise reporting, while interactive Power BI dashboards provided real-time visibility into production efficiency, inventory levels, supply chain operations, financial performance, and executive KPIs. The solution incorporated role-based security, scheduled data refreshes, and scalable cloud infrastructure to support future business growth.
Results
The modern data platform established a single, trusted source of enterprise data, eliminating reporting silos and improving data consistency across departments. Automated data pipelines reduced manual data preparation efforts by more than 85%, while report refresh times decreased from several hours to minutes. Business leaders gained near real-time insights into operational and financial performance, enabling faster, data-driven decision-making. The Azure-based architecture also improved scalability, reduced infrastructure maintenance, and provided a strong foundation for advanced analytics, AI initiatives, and future cloud modernization projects.
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