Context and objectives

Following a significant internal reorganization, an international Consumer Packaged Goods (CPG) leader faced a critical challenge in its reporting capabilities for key business units. The existing reporting landscape was fragmented, consisting of over 40 distinct Power BI reports and more than 100 report pages scattered across multiple workspaces.

This data foundation was heavily reliant on manual processes and disparate data sources, including major third-party market data syndicators. It was prone to errors, difficult to maintain, and slow to adapt to business needs.

The reduced capacity of the internal data team exacerbated these issues, creating a bottleneck that delayed insights and increased operational risk.

The organization needed more than just updated reports; they required a complete re-engineering of their data foundation and ecosystem. The primary objective was to transition from a manual, disjointed reporting environment to a robust, automated data platform that would:

• ensure data consistency,

• improve time-to-market for new insights,

• provide a scalable foundation for future analytics initiatives.

Approach

Agilytic partnered with the client to implement a future-proof data platform, moving beyond simple report generation to build a sustainable engineering architecture.

🧱   Layered data architecture

The team deployed a Snowflake data warehouse using a "Medallion" architecture (Bronze, Silver, Gold). This approach standardized data ingestion and transformation, ensuring a single source of truth and simplifying debugging and lineage tracking.

🚚  Dual-track delivery model

To balance immediate business value with long-term stability, the project utilized a "Day 1 / Day 2" strategy to modernize the company’s data foundation.

"Day 1" focused on rapid prototyping and validation with business users, while "Day 2" prioritized industrialization, governance, and the establishment of rigorous CI/CD pipelines across development, test, and production environments.

🤖  Standardization and automation

We rationalized the existing data models, consolidating over 10 separate models into just two optimized schemas. Complex hierarchies from external data providers were flattened and standardized algorithmically to ensure consistency across different markets.

⚙️  Rigorous engineering practices

The team introduced data engineering best practices to the reporting workflow, including strict version control and automated data testing, ensuring that the new platform was not only accurate but also maintainable by the client’s internal teams post-handover.

Results

The project successfully transformed the client’s reporting capabilities from a resource-intensive burden into a streamlined, strategic asset.

⚡️ Operational efficiency

The consolidation of reports resulted in a drastic reduction in volume, shrinking over 100 report pages to approximately 30 highly relevant, interactive dashboards.

📦  Accelerated delivery

The new standardized architecture reduced report development cycles by 30%, enabling the business to react faster to market changes.

🦾  Scalability and innovation

By establishing a clean, governed data foundation, the client significantly reduced maintenance costs and is now leveraging this architecture to explore advanced use cases, including AI and Large Language Model applications.

🎯  Data quality and trust

Automated data flows eliminated manual discrepancies, restoring stakeholder confidence in key commercial performance metrics.