Key Highlights: What This Case Study Covers
- Best practices in enterprise data foundation implementation for CPG, including scalable data modeling and governance frameworks
- Implementation of cloud-based data pipelines and GenAI-assisted modeling tools to enable faster and standardized data processing
- How to build a future-ready, scalable data foundation that supports multiple functions and high-volume datasets with minimal manual intervention
- Real-world application of Azure and Databricks for automated workflows, end-to-end data governance, and data-driven decision-making
Client Overview
A global CPG giant operating in over 200 countries with a mature cloud strategy and a strong focus on digital transformation. Known for its large-scale operations and multi-brand portfolio, it serves a broad consumer base across retail, grocery, and foodservice channels worldwide.
The Ask
The client wanted to transform its disparate and siloed data landscape into a unified, trusted, and accessible source of truth to enable data-driven decision-making, improve business insights, and drive innovation.
Challenges
- Standardizing the diverse data sources for enterprise-wide use was a major challenge; the presence of manually maintained data made the standardization process extremely difficult.
- Legacy Teradata system limited scalability and cloud readiness for AI-driven use cases.
- The complex, multilayered Teradata architecture, with views created from different layers, significantly complicated data lineage tracking.
Our Solution: Enterprise Data Foundation Implementation
1. Data Architecture Modernization and Standardization
- Cloud Migration to Delta Lake: Transitioned from the legacy Teradata system to a modern cloud-based Delta Lake architecture (Bronze, Silver, Gold layers) to achieve scalability and AI/ML readiness.
- Established Clear Data Layers: Designed a logical data model covering entities, attributes, and relationships.
- Bronze: Ingested raw, disparate data (ERP, POS, IoT, etc.).
- Silver: This layer was the key to standardizing diverse and problematic manually maintained data. We leveraged the Microsoft CPG (Consumer Packaged Goods) data model template as a foundation. This industry-specific model was customized and tailored to meet the client’s specific business requirements and unique data needs. This approach accelerated the creation of a structured, query-ready format, establishing the “single source of truth.”
- Gold: Developed business-centric aggregates, dimensional models, and large analytic tables optimized for analytics and reporting.
- Improved Lineage: Incorporated metadata management directly into the logical model for improved data governance and to actively track data lineage across the new multilayer architecture.
2. Established Data Governance
Set up governance boards, defined data quality metrics, data security, and privacy processes, and implemented a comprehensive data dictionary and metadata management framework.
3. Enabled Concurrent Modeling via DevOps
Introduced DevOps processes to support multiple projects working simultaneously while maintaining standards and consistency.
4. Automated Dashboards and Reporting
Developed dashboards reflecting business inputs, with auto-generated reports circulated to relevant user groups.
5. Adoption and Review Processes
Conducted daily “data modeling office hours” and weekly governance reviews to ensure standards adherence and facilitate adoption.
6. Leveraged Accelerators and GenAI Tools
Used a custom toolkit to validate STTMs compliance and synchronize models in Synapse. Later, incorporated a GenAI-assisted data modeling accelerator to suggest tables and columns, improving accuracy and efficiency.
Impact Delivered
Architectural and Operational Efficiency Gains:
The deployment of the new architecture, centered on a modernized Delta Lake and a customized industry blueprint, delivered immediate, quantifiable value by resolving legacy constraints and enabling scalable analytics.
The core success hinged on taking the Microsoft CPG (Consumer Packaged Goods) data model template and customizing its entities and attributes to perfectly align with the client’s unique operational needs. This strategic approach achieved several critical outcomes:
- Accelerated Time-to-Value: The use of pre-defined schemas and ETL pipelines for common CPG scenarios (e.g., sales, procurement) drastically reduced development time, allowing the enterprise data foundation to be deployed faster.
- Future-Proof Unified View: Custom-tailored entities broke down legacy data silos, successfully integrating transactional, operational, and external data into a single source of truth, establishing future-proof integration capabilities.
- Scalable AI & Analytics: The standardized platform directly enables advanced use cases, including demand forecasting, trade promotion optimization, and sentiment analysis, supporting critical AI-driven initiatives.
Beyond the Architectural Benefits, We Realized Significant Process and Adoption Improvements:
- Foundation for Enterprise Applications: Multiple critical applications across the business—including Procurement, Supply Chain, Integrated Business Planning (IBP), and Financial Analytics—are now built directly on this robust, standardized enterprise data foundation.
- Modeling Efficiency Increase: Achieved a 10% increase in data modeling efficiency through the implementation of a dedicated modeling portal with integrated data steward approval workflows, ensuring rapid yet governed changes.
- Robust Data Ingestion: Implemented a reliable pipeline framework that supports the automated and resilient ingestion of data from all diverse sources, including ERP systems, POS data, and IoT sensors.
- Cost-Efficiency: The adoption of Delta Lake separates compute from storage and utilizes open formats, ensuring scalability while reducing vendor lock-in and the total cost of ownership.
Customizing the industry blueprint transformed raw data into a production-ready, AI-powered data platform, ensuring it can easily evolve with the company’s broader data strategy.
