Enterprise Data Warehouse Modernization and BI System Overhaul for Supply Chain Analytics

The client manages a rapidly growing volume of supply chain and order management data, with over 1.5 million orders annually. Their existing analytics system relies on a third-party BI platform that exhibits latency (12-24 hours for data refreshes), inefficient report usability, limited user customization, and excessive load on the SQL server. These limitations hinder timely decision-making and operational agility, especially as demand forecasting and resource planning require near real-time data insights.

A large-scale logistics and supply chain management enterprise operating globally with extensive order and inventory data, seeking to enhance data analytics capabilities and operational decision-making.

• Implement a scalable data warehouse to aggregate and store order, inventory, and logistics data from multiple sources.
• Establish an efficient ETL process to enable near real-time data updates (within one hour) for over 1,000 users, minimizing infrastructure costs.
• Refactor and migrate over 150 complex reports into an intuitive, user-friendly BI platform with interactive dashboards and visual analytics tools.
• Facilitate self-service analytics capabilities for business analysts and operational managers.
• Integrate data security and access controls, including row-level security, to ensure proper data governance based on user roles and regions.
• Automate data quality checks and notifications to proactively address discrepancies.
• Unify system management within a single technology ecosystem for seamless maintenance and updates.
• Provide modern, dynamic dashboards that support diagnostic, predictive, and prescriptive analytics to optimize supply chain performance.

• Design of an enterprise-grade data warehouse to centralize order, inventory, and logistics data.
• Development of an incremental and scheduled ETL process using cloud-based data integration tools (e.g., Azure Data Factory) for hourly updates.
• Migration of existing reports and dashboards into an interactive BI environment with filters, drill-down capabilities, and visualizations.
• Implementation of role-based access controls and row-level security to tailor data visibility for diverse user groups.
• Packaging of reports into categorized, easy-to-navigate apps within the BI platform to improve user experience.
• Deployment of automated data quality assurance, including discrepancy detection and alert notifications.
• Development of dynamic, visual dashboards displaying KPIs such as delivery peaks, supply chain fluctuations, and resource utilization.
• Integration of predictive analytics models for demand forecasting and capacity planning.

• OLTP systems for real-time transaction data extraction
• Existing reporting repositories and legacy data sources
• User authentication and authorization systems
• Notification and alerting services for data quality monitoring

• System should support a data refresh cycle of no more than 1 hour for over 1,000 concurrent users
• Dataset optimized to minimize storage costs, aiming for datasets under 10 GB where feasible
• Role-based security and row-level data access controls
• High availability and disaster recovery readiness
• Secure data transmission and storage compliant with organizational policies

The new analytics platform aims to deliver near real-time data insights with an update delay of less than one hour, enabling faster and more informed decision-making. It anticipates a significant reduction in reporting latency, improved user autonomy in analytics, and enhanced operational efficiency in supply chain management. The solution is expected to support thousands of active users, optimize resource allocation, and increase overall supply chain responsiveness and resilience.