How Tiger Analytics and Microsoft Transformed Nature’s Path Organic Foods’ Data Estate

Executive Summary

Nature’s Path Organic Foods, a leading manufacturer of organic breakfast and snack foods distributed across more than 50 countries, partnered with Tiger Analytics to modernize a legacy business intelligence environment that had outgrown its ability to support the business. Built on aging SQL Server, SSIS, and SSRS technology, the previous platform left data fragmented across silos, slowed decision-making, and could not scale with the organization’s growth.
Tiger Analytics led a complete re-architecture – not a lift-and-shift – onto Microsoft Fabric, unifying ingestion, transformation, governance, and reporting under a single, metadata-driven platform. The result is a scalable, AI-ready foundation that has already accelerated data engineering timelines and re-established trust in the organization’s reporting through automated data quality controls – establishing the base on which reporting silos will be reduced as self-service adoption expands.

This case study outlines the challenge, the solution Tiger Analytics designed and delivered, and the measurable impact achieved in the first phase of the engagement — along with the roadmap for what comes next.

Customer Challenges

Nature’s Path Organic Foods recognized that its data and analytics landscape could no longer keep pace with the business. Several issues drove the need for change:

• Fragmented data silos: Customer, financial, and operational data lived in isolated systems, with reporting built independently across business units rather than from a shared source of truth.
• Legacy technology limitations: More than 10 legacy SSIS pipelines, SSRS-based reporting, and a traditional SQL Server warehouse constrained scalability and made even routine changes slow and costly.
• Manual operational bottlenecks: Without a centralized, automated framework, maintenance was labor-intensive and new development cycles were slow – a direct barrier to responding to changing business needs.
• Inconsistent data quality: The absence of an automated validation framework meant data quality issues surfaced downstream in reports, eroding business users’ trust in the numbers.

IT leadership and business unit stakeholders jointly sponsored the initiative, prioritizing a platform that would deliver faster time-to-value, stronger governance, and a foundation ready for AI-driven analytics.

Our Solution: Fabric-Native Data Modernization

Tiger Analytics designed and delivered an enterprise-scale modernization built on a Medallion Architecture (Bronze, Silver, Gold) within Microsoft Fabric. Rather than migrating legacy logic as-is, the team rebuilt the platform from the ground up around a metadata-driven framework purpose-built for scale, governance, and maintainability.

360-Degree Data Integration

The platform connects and unifies approximately 48 distinct data entities across the organization’s core systems – spanning ERP, supply chain planning, point-of-sale market data, and productivity-tool sources – into a single, centralized OneLake foundation. The platform currently supports more than 50 business users across finance, supply chain, and commercial teams, drawing on a curated reporting dataset of under 20 GB, with usage-volume baselines still being established as adoption grows.

The Tiger Weave Framework

At the core of the solution is Tiger Weave, Tiger Analytics’ proprietary metadata-driven accelerator. Rather than hand-building and maintaining pipelines for every source, Tiger Weave dynamically drives extraction, transformation, and load logic from configuration metadata – dramatically reducing the engineering effort required to onboard new sources or extend the platform.

A key differentiator is the framework’s context-aware automated recovery: if a pipeline run fails partway through, the system identifies precisely where execution stopped and resumes from that point on rerun, without reprocessing or duplicating data that already loaded successfully.

Advanced Data Quality Shield

Tiger Analytics implemented a rigorous validation layer using the open-source Great Expectations library. Incoming data is automatically checked against schema, nullability, and uniqueness rules before it reaches business reporting; records that fail validation are diverted to quarantine tables for review rather than silently corrupting downstream reports.

Workspace Isolation & Secrets Management

The platform is organized into physically segregated Development, Test, and Production workspaces, separating ETL compute and code artifacts from the consumption layer that business users access. Source credentials and connection secrets are managed centrally through Azure Key Vault rather than embedded in pipeline code. This phase of the engagement focused on this workspace-level isolation and secrets management; broader governance capabilities – such as row-level security, sensitivity labels, and Microsoft Purview integration – are identified as candidates for a future phase rather than delivered controls in this engagement.

Enhanced Visualization Suite

Tiger Analytics delivered three integrated Power BI report suites aligned to the business’s highest-priority domains: market and brand performance analytics, supply chain shortfall and out-of-stock tracking, and profitability and budget variance reporting.

Operationalized DevOps

The engagement professionalized release management with a three-tier Git branching strategy (feature → dev → test → main), Fabric Deployment Pipelines for promoting reporting and ETL artifacts, and Azure DevOps CI/CD automation for Fabric Warehouse schema (DACPAC) deployments – replacing ad hoc, manual promotion processes with a controlled, auditable release cadence.

Impact Delivered

In its first phase, the engagement delivered measurable improvements in reporting consolidation, processing speed, development velocity, and data trust.

50% Faster ETL Processing

Metadata-driven incremental load strategies and the Tiger Weave framework’s optimized extraction logic cut data ingestion and transformation timelines by roughly 50% relative to the legacy SSIS-based process – directly shortening the time between data landing and insight availability.

35-40% Reduction in Development Time

Because Tiger Weave drives pipeline behaviour from configuration rather than custom code per source, onboarding a new data source or extending an existing module no longer requires a platform redesign – cutting development time for new data integration work by roughly 35-40%.

Higher-Trust Analytics & Operational Resilience

Automated data quality validation meaningfully increased confidence in executive financial and operational reporting, while the platform’s context-aware recovery model ensures pipeline failures are resolved without manual rework or risk of duplicated data – reducing operational risk for the data engineering team. Early benchmarking on one of the platform’s most heavily used reports shows roughly a 50% reduction in end-to-end execution time; dashboard refresh times are now measured in seconds, though a direct before/after comparison isn’t meaningful since the legacy and new platforms use different underlying data models.

A Foundation for Cost Efficiency and AI Readiness

The Medallion architecture is designed to scale Azure resource consumption with actual workload demand rather than fixed legacy infrastructure overhead, positioning the platform for more efficient cost management as usage grows. Formal infrastructure cost comparisons between the legacy and Fabric environments are still being finalized and will be incorporated as that analysis is completed.

More immediately, the engagement established a clean, well-governed data foundation that is structurally ready for natural language querying, generative AI applications such as Microsoft Copilot, and predictive modelling – capabilities the legacy SSRS-based environment could not have supported.

Future Innovation Roadmap

Tiger Analytics and Nature’s Path Organic Foods continue to build on the platform’s foundation, with a roadmap aligned to Microsoft’s broader AI vision. Planned initiatives include:

• Integration of Azure OpenAI and Microsoft Copilot experiences for conversational, natural-language analytics
• Predictive demand forecasting and supply chain optimization, building on the supply-chain data foundation already in place
• Advanced machine learning models for operational decision support
• Expanded self-service analytics and conversational BI
• Adoption of industry-specific AI accelerators
• Continuous adoption of new Microsoft Fabric platform capabilities as they mature

Nature’s Path Organic Foods has indicated it would recommend Microsoft Fabric to peer organizations, particularly those already operating within the Microsoft ecosystem, citing the depth of native integration across the platform as a key advantage.