United Industries Limited – Enterprise Data Warehouse & Analytics Dashboards including AI Pulse Project

The primary goal of this project was to design and implement an enterprise-grade Data Warehouse and Analytics Ecosystem that provides a single source of truth for all critical business functions at United Industries Limited (UIL). The project aimed to transform disconnected, siloed data into unified, actionable intelligence for faster and more informed decision-making across eleven core departments.

Historically, UIL faced challenges in aggregating data from multiple systems SAP, SAGE, HRMS, IoT, and Secondary Sales System. Reporting was manual, time-consuming, and often inconsistent across departments. The project’s strategic goal was to establish a modern cloud-based data architecture using AWS Glue, Snowflake, and Tableau, enabling real-time visibility into operational, financial, and strategic KPIs.

Specific objectives included:

• Building a centralized data lake capable of integrating diverse data sources (SAP HANA, MySQL, SQL Server, and Microsoft 365).

• Automating ETL pipelines to ensure data accuracy, timeliness, and consistency.

• Developing department-specific dashboards (Production, Warehouse, Quality, Engineering, Accounts & Finance, HR/Admin, Sales & Marketing, Audit, Procurement, MIS, and Management).

• Enabling data-driven culture through self-service analytics and transparency.

• Enhancing data security, governance, and compliance using AWS native tools and DevOps best practices.

• Setting up scalable infrastructure for future predictive analytics and AI integration.

The project’s ultimate goal was to empower UIL’s leadership and operational teams with real-time analytics and insights, driving efficiency, cost reduction, and performance improvement across the enterprise.

The project successfully delivered measurable business and operational benefits:

• Unified Data Access: A single source of truth for all key departments.

• Time Efficiency: Reporting cycle reduced from days to minutes.

• Enhanced Decision-Making: Department heads can track performance and trends via interactive dashboards.

• Operational Transparency: Leadership gained end-to-end visibility across the production, finance, and sales chain.

• Improved Data Governance: Automated ETL and role-based access improved data security and integrity.

• Enabled AI & Predictive Analytics: AI and ML analytics

Data Ingestion & Processing: AWS Glue, DBT, S3

Data Warehouse: Snowflake

Visualization & BI: Tableau Online

AI/ML: Tableau Pulse and tableau einstein

AI/ML Extension (Optional): DataBricks, AWS SageMaker

Collaboration & DevOps: ClickUp, Microsoft Teams, Slack

Data Sources: SAP HANA, SAGE ERP, MySQL, SQL Server, HR Cloud, Vendi (Secondary Sales), IoT & Energy Monitoring systems, Microsoft 365

Implementing an enterprise-grade data warehouse across eleven departments posed significant challenges:

• Data Complexity: Integration of heterogeneous systems (SAP, SAGE, IoT) with inconsistent schemas required extensive data mapping and validation.

• Change Management: Shifting departmental users from Excel-based reporting to interactive dashboards required training and behavioral adaptation.

• Infrastructure Configuration: Configuring AWS, Snowflake, and Tableau under UIL’s accounts with shared responsibility for cost and security demanded strong coordination.

• Data Quality Assurance: Ensuring accuracy across 250+ tables and 1 million+ rows required multi-level validation.

• Limited Local Expertise: Given the novelty of cloud-native analytics in Pakistan’s manufacturing sector, significant effort was invested in knowledge transfer.

Despite these challenges, the project was executed successfully within planned timelines through disciplined milestone tracking, on-site collaboration, and agile communication frameworks.

The “United Industries Limited Data Warehouse and Dashboards Project” was initiated to modernize UIL’s data management and analytics capabilities. the project implemented a scalable data architecture using AWS Glue for extraction, Snowflake for warehousing, and Tableau Online for visualization.

Eleven departments, including Sales, Finance, HR, and Production, were equipped with customized dashboards integrating data from over 250 tables and 1 million+ rows. A secure DevOps environment was established for data recovery, governance, and continuous validation.

The project adopted an agile milestone-based approach, ensuring early value delivery and refinement through user acceptance testing (UAT). The architecture supports both structured and unstructured data and is future-ready for AI-driven predictive analytics.

By integrating multiple ERP and operational systems into a unified data lake, UIL achieved real-time insights, reduced manual reporting efforts, and improved business decision speed and accuracy.

The project was primarily executed in Pakistan, with major operations based in Faisalabad (United Industries Limited HQ) and Lahore . On-site activities such as requirement gathering and user acceptance testing were conducted in Faisalabad, while data modeling and architecture design were carried out virtually and in Lahore. The cloud infrastructure was hosted globally on AWS and Snowflake, ensuring scalability and resilience beyond geographic boundaries.