Enterprise Data Lifecycle Management System for Manufacturing Business

The client faces limited visibility into the full potential of their data due to the absence of a comprehensive data processing system and siloed data across departments. Outdated data handling tools impair timely insights, affecting service quality and operational efficiency. Manual data management hinders scalability and impedes advanced analytics capabilities, including AI/ML integration.

A large-scale manufacturing entity operating a complex, high-volume supply chain across multiple locations, aiming to leverage data for operational efficiency and strategic decision-making.

• Establish a unified data storage standard by migrating manual data files to a centralized database system.
• Create a scalable data warehouse architecture supporting increased data volumes and diverse data sources.
• Implement automated data ingestion, validation, and processing pipelines using modern data integration tools.
• Enhance reporting accuracy and speed by shifting calculations to the database and optimizing data refresh processes.
• Secure sensitive data with role-based access controls and encryption to comply with data privacy requirements.
• Lay a foundation for advanced analytics and AI/ML capabilities through a stable and well-structured data core.
• Enable faster, real-time data access and improved process visibility across multiple departments.

• Data migration subsystem to transfer raw data from manual files into structured databases.
• Design and implementation of a scalable data schema and warehouse on a cloud-based SQL platform.
• Automated ETL (extract, transform, load) pipelines utilizing modern data orchestration tools (e.g., Azure Data Factory).
• Automation of data validation, cleaning, and testing through scripting (e.g., Python).
• Role-based access control (RBAC) and encryption for sensitive data security.
• Data archiving and versioning mechanisms for historical comparisons and integrity checks.
• Optimized reporting layer with SQL-based calculations and automatic data refresh scheduling.

• Existing operational and legacy data sources for migration.
• Business intelligence tool integration (e.g., Power BI or similar) for reporting.
• Security and identity management systems for access control.

• Scalability to support increasing data volumes with minimal performance degradation.
• High availability and disaster recovery to ensure continuous data access.
• Data processing and report generation should deliver responses within seconds, supporting real-time analytics.
• Robust security measures, including encryption and RBAC, to protect sensitive enterprise data.

The implementation of a comprehensive data lifecycle management system is expected to enhance overall data accuracy and process visibility, leading to faster and more informed decision-making. Targeted outcomes include improved operational efficiency, reduction in manual data handling errors, and the establishment of a scalable data foundation enabling advanced analytics and AI integration, thereby future-proofing the organization’s data strategy.