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FMCG Wine Enterprise – Power BI & Redshift Optimisation

Sector/s: FMCG (Fast Moving Consumer GOods)
Technologies Used: Power BI, AWS Redshift, SQL Management Studio, Power Query, DAX Studio
Key Services: Data Management & Optimisation

A major grower, distributor, and wholesaler in the wine industry, our client manages operations across viticulture, production, logistics, national distribution, and wholesale. With a highly seasonal and geographically dispersed business model, they required a reporting solution that could deliver fast, reliable insights across teams to support decision-making in supply chain planning, yield tracking, and performance monitoring. The client was experiencing significant delays in their Power BI reporting ecosystem with load times regularly exceeding 60 minutes, limiting access to critical data across the organisation. Data Sagacity was engaged to assess, restructure, and optimise their reporting framework, reducing load times, improving data usability, and establishing a foundation for scalable, AI-ready analytics.

Problem

The organisation’s reporting ecosystem, powered by Power BI, had become a major bottleneck. Key issues included:

  • Extreme report load times of 60+ minutes in Power BI Desktop and 30–40 minutes in Power BI Service.
  • Widespread user delays across a nationwide workforce, leading to hundreds of hours lost weekly.
  • Overloaded Power Query logic, with complex transformation steps handled inefficiently at the BI layer.
  • Unstructured data model using a disconnected galaxy schema, hindering AI readiness and semantic clarity.
  • Internal capacity stretched thin – as the volume of reports grew, the ability of internal teams to maintain, validate, and optimise them diminished. Reports that started as proofs of concept became production-critical but lacked the dedicated resourcing to evolve. This created a fragmented reporting environment with inconsistent standards and rising maintenance overhead.

Solution

Following discovery sessions and model walkthroughs, Data Sagacity implemented a targeted rebuild and performance strategy:

  • Offloaded Power Query transformations into AWS Redshift, using SQL views to pre-process and shape the data closer to the source.
  • Redesigned the Power BI semantic model, replacing the fragmented schema with a combination of snowflake and denormalised tables.
  • Removed redundancy and cleaned metadata, reducing visual clutter and improving business usability.
  • Configured incremental refreshes to reduce full load pressure and enable faster data cycling.
  • Prepared the model for Microsoft Copilot, ensuring field naming, relationships, and metadata were AI-quarriable.
  • Collaborated with internal engineering teams across vineyards, supply, and distribution for validation and alignment with domain logic.

Benefits

The impact of the new reporting framework was immediate:

  • Refresh optimisation:

    Initial Refresh Optimised Refresh Improvement
    Power BI Desktop 70min 8min 89%
    Power BI Service 40min 3min 92%
  • Improved access and adoption across operational teams – boosting insight availability.
  • AI readiness enabled, unlocking future self-service with natural language querying.
  • Improved data governance and structure, reducing model maintenance and technical debt.
  • Faster decision cycles, supporting seasonal agility and operational performance.
  • Established repeatable optimisation practices – developed a scalable approach to transforming Power BI reports from proof-of-value assets into production-grade, enterprise-ready solutions. This included guidance on performance tuning, model design, metadata structuring, and incremental refresh strategies.

Additional Benefits
In addition to performance and model improvements, this engagement delivered broader operational value across the client’s analytics environment:

  • Data Quality Uplift: As part of the migration and optimisation process, we performed targeted data validation and profiling. This surfaced inconsistencies and improved trust in key reporting outputs across teams.
  • Pilot for Internal Best Practices: Our team was successfully onboarded into a newly introduced development environment that had not previously been used by external partners. In doing so, we became a working pilot for the client’s internal governance and operating procedures – helping to validate, refine, and align their best practices for engaging external consultancies.
  • Strengthened Governance & Collaboration: By following internal protocols while also establishing new, applicable techniques, we supported the evolution of their documentation, version control, and collaboration workflows – delivering value beyond the immediate technical solution.

Conclusion

This project delivered a step change in reporting performance and usability across a complex enterprise ecosystem. By offloading Power Query logic to AWS Redshift, redesigning the semantic model, and embedding AI-ready design principles. The client now benefits from a 92% reduction in report refresh time, and significantly improved reliability in accessing business critical insights. This has drastically reduced wait times across the business, enabling faster, more confident decision-making and shortening the path from insight to action. Crucially, the engagement also strengthened internal data governance. As the first external team to operate within their new development environment, we validated internal onboarding processes, refined operational best practices, and helped shape future standards for working with partners. This made the engagement a success not only in technical delivery – but also in strategic alignment and internal capability uplift.

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