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Enterprise Data Lifecycle Management System for Food Manufacturing & Retail
  1. case
  2. Enterprise Data Lifecycle Management System for Food Manufacturing & Retail

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Enterprise Data Lifecycle Management System for Food Manufacturing & Retail

yalantis
Food & Beverage
Retail

Challenges in Data-Driven Transformation

The organization faces significant barriers in adopting a data-driven culture due to fragmented data storage, outdated processing tools, and lack of centralized data governance. Key issues include siloed departmental data, slow report generation, limited visibility into data potential, and insufficient infrastructure to support advanced analytics or AI/ML initiatives.

About the Client

30+ year-old manufacturer and retailer with complex supply chain operations across multiple locations

Strategic Transformation Goals

  • Establish centralized data warehouse for unified data management
  • Implement scalable data lifecycle management (DLM) framework
  • Enhance business intelligence capabilities through modern analytics infrastructure
  • Enable real-time data access for sales, finance, and operations decision-making
  • Create foundation for future AI/ML implementation

Core System Requirements

  • Automated data migration from legacy systems to structured database
  • ETL pipeline development using Azure Data Factory
  • Python-based data validation and testing automation
  • Role-based access control (RBAC) with data encryption
  • SQL-based calculation engine for Power BI integration
  • Data archiving and versioning system

Technology Stack

Azure Data Factory
Amazon SQL Server
Python
Microsoft Power BI
Amazon Redshift

System Integrations

  • ERP systems (SAP/Oracle)
  • CRM platforms
  • Legacy financial systems
  • Supply chain management tools

Operational Requirements

  • Horizontal scalability for growing data volumes
  • 99.9% system availability
  • Data processing latency <2 seconds
  • Role-based security compliance (GDPR/SOX)
  • Automated disaster recovery

Business Transformation Outcomes

Implementation will enable real-time operational visibility, reduce reporting latency by 70%, and decrease manual data processing errors by 90%. The centralized data architecture will support 50% faster decision-making cycles, 30% reduction in data management costs, and create a foundation for predictive analytics that could yield $2M+ annual savings through optimized inventory and resource allocation.

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