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Case Study: Modernizing Data Warehousing in Azure Cloud for Global Packaging Company
Problem Statement: Legacy Data Silos Hindering Business Intelligence
A global packaging manufacturer struggled with fragmented data architecture across multiple Dynamics 365 systems. This outdated setup created severe barriers to data accessibility, trust, and performance:
Manual & Disjointed Access:
Business users relied on direct system queries for analytics, leading to high resource strain and inconsistent performance.
Business users relied on direct system queries for analytics, leading to high resource strain and inconsistent performance.
Unstandardized Reporting Models:
Inconsistent definitions and lack of modular standardization caused significant delays in reporting and extended development cycles.
Inconsistent definitions and lack of modular standardization caused significant delays in reporting and extended development cycles.
Disconnected Data Relationships:
Siloed datasets lacked relational mapping, limiting holistic business analysis and obstructing enterprise-level insights.
Siloed datasets lacked relational mapping, limiting holistic business analysis and obstructing enterprise-level insights.
Impact: Operational Drag & Data Trust Deficiency
The siloed and reactive data environment directly resulted in:
Delayed Decision-Making:
Lack of real-time dashboards and self-service BI limited executive visibility.
Lack of real-time dashboards and self-service BI limited executive visibility.
Increased IT Overhead:
Inefficient querying and data reconciliation efforts elevated operational costs by consuming high-value IT resources.
Inefficient querying and data reconciliation efforts elevated operational costs by consuming high-value IT resources.
Data Governance Risks:
Absence of a unified access and compliance framework led to inconsistent governance and vulnerability across data flows.
Absence of a unified access and compliance framework led to inconsistent governance and vulnerability across data flows.
Low Productivity:
Analysts and business users spent excessive time sourcing and cleaning data instead of extracting actionable intelligence.
Analysts and business users spent excessive time sourcing and cleaning data instead of extracting actionable intelligence.
Solution: Azure-Powered Enterprise Data Warehouse & Reporting Framework
To resolve these challenges, JBS engineered a scalable Enterprise Data Warehouse (EDW) on the Azure Cloud, enabling governed, integrated, and high-performance analytics:
ETL Automation with Azure Data Factory:
Unified structured and unstructured datasets from Dynamics 365 into a governed Azure Data Lake, enabling high-volume transformations.
Unified structured and unstructured datasets from Dynamics 365 into a governed Azure Data Lake, enabling high-volume transformations.
Staging & ODS Architecture:
Designed robust Staging (Stg) and Operational Data Store (ODS) layers to standardize and prep data before analytics modeling.
Designed robust Staging (Stg) and Operational Data Store (ODS) layers to standardize and prep data before analytics modeling.
Enterprise Data Modeling:
Built Facts & Dimensions aligning to business workflows, enabling trusted data marts and reusable reporting layers.
Built Facts & Dimensions aligning to business workflows, enabling trusted data marts and reusable reporting layers.
Data Governance Framework:
Enforced access controls, metadata management, and quality rules to ensure data lineage, compliance, and reliability.
Enforced access controls, metadata management, and quality rules to ensure data lineage, compliance, and reliability.
Power BI Enablement:
Created a suite of interactive dashboards delivering critical KPIs across finance, operations, and supply chain—driving faster, insight-led decision-making.
Created a suite of interactive dashboards delivering critical KPIs across finance, operations, and supply chain—driving faster, insight-led decision-making.
Quantified Business Outcomes
The transformation delivered measurable business gains:
5% Faster Reporting Cycles
Reduced reporting lag by automating data pipelines and harmonizing sources.
Reduced reporting lag by automating data pipelines and harmonizing sources.
15% Improvement in Decision Accuracy
Enhanced trust and consistency in data through model-driven reporting.
Enhanced trust and consistency in data through model-driven reporting.
10% Reduction in Operational Costs:
Cut overhead through centralized data governance and reduced system strain.
Cut overhead through centralized data governance and reduced system strain.
30% Boost in Data Quality
Standardized transformation logic improved accuracy and eliminated duplication
Standardized transformation logic improved accuracy and eliminated duplication
Single Source of Truth
Created unified data views across departments, improving cross-functional collaboration and analytics maturity
Created unified data views across departments, improving cross-functional collaboration and analytics maturity
Technology Stack
Azure Data Factory
Logic App
Azure Synapse
Azure VM
V-net
Azure Single DB
Power BI
ETL Pipelines & SQL Modeling
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