Published November 3, 2025

From Multiple POS Systems to 1 Source of Truth: Building Great Harvest’s Unified Data Foundation with Quantiiv.

When NewSpring Capital acquired Great Harvest in 2024, they inherited a unique challenge: over one hundred franchisees running on multiple point of sale systems. Answering a simple question like “What are my sales?” proved a bit difficult to say the least.

Each POS system had its own schema, export cadence, and data semantics. But the real complexity wasn’t just disparate systems. It was multiple systems multiplied by independent implementations. A franchisee on one POS system didn’t roll up to a corporate account with standardized menu structures. They had their own account, their own menu build, their own naming conventions, their own export schedule. Thirty locations on the same POS system meant thirty separate data sources, each speaking a slightly different dialect of the same language.

Leadership had trouble answering high level questions in a quick manner with confidence:

What are same-store sales trends across the system?
Which product categories are growing, and where?
How do we benchmark performance when every store speaks a different data language?

This is the challenge we wrote about in our previous post on POS-agnostic infrastructure. But Great Harvest became the proving ground for turning that theory into operational reality.

The Real Problem: Data Fragmentation as Organizational Debt

The technical symptoms were obvious — mismatched schemas, inconsistent naming conventions, incompatible export formats. But the organizational cost was deeper:

Loss of analytical velocity: Every question required manual reconciliation. Insights arrived too late to be actionable.
Erosion of trust: Reconciling against different systems created unnecessary overhead and was error prone given the manual nature of the work.
Strategic impacts: Without system-wide visibility, corporate leadership couldn’t identify patterns, allocate resources effectively, or measure initiative impact.

Great Harvest needed more than reporting. They needed data infrastructure that could absorb heterogeneity without sacrificing consistency — a canonical layer that could unify an arbitrary number of different operational realities into one analytical truth.

The Architecture: Building a Universal Data Backbone

We designed Great Harvest’s solution around three core principles:

1. POS-Agnostic Ingestion & Normalization

Every transaction, from every system, flows into a unified data warehouse where it’s transformed into a common schema.

Multi-protocol ingestion: API feeds, SFTP drops, webhook streams, and manual exports — all handled through a single orchestration layer.
Schema mapping & reconciliation: Each POS system’s data structure is mapped to a canonical model that preserves system-specific nuance while enabling cross-platform analysis.
Incremental sync with validation: Pipelines run continuously, detecting schema drift, missing data, or anomalies before they propagate downstream.

This isn’t ETL as middleware. It’s data infrastructure as a product — robust, self-healing, and designed for the long term.

2. Canonical Menu & Product Taxonomy

The hardest part of multi-POS unification isn’t moving data — it’s semantic reconciliation.

“Honey Whole Wheat” in one system, “HWW Bread” in another, and “Product_447” in the legacy system — all the same item, invisible to cross-system analysis.

We are building a centralized product catalog that:

Maps every item across every POS to a single, authoritative taxonomy
Handles SKU drift, menu changes, and regional variations
Enables product-level analytics that were previously impossible

Now Great Harvest can answer questions like “How is sourdough performing system-wide?” without manual category tagging, store-by-store exports, or POS rebuilds.

Curious about our methodology for this? Read more about it here.

3. Franchise-Level Data Governance

Great Harvest franchisees operate with autonomy — and their data infrastructure needed to respect that.

Permissioned access: Each bakery sees their own performance while contributing to aggregate analytics.
POS flexibility: Franchisees can switch systems without losing historical continuity.
Trust through transparency: Every metric is traceable back to its source transaction.

The result is a federated data model that balances independence with enterprise-grade visibility.

From Infrastructure to Insight: The Quantiiv Console

Once the data foundation was unified, we leaned on the Quantiiv Console — a purpose-built interface for operating on top of that consolidated layer.

The Console isn’t a generic BI tool layered on top of messy data. It’s the analytical surface of a unified data product, designed specifically for multi-unit restaurant operators.

Real-time, cross-system visibility: transaction counts, product mix, average ticket, menu analytics, and more.
Historical continuity: Franchisees that switch POS systems maintain year-over-year trend analysis without data loss.
Performance benchmarking: Compare individual stores against each other, system averages, and product analytics.

The Console is where the Great Harvest team interacts with their data warehouse to understand trends and stories.

ROGER: Natural Language Access to Unified Data

With the data foundation in place, Great Harvest leadership can now ask questions in plain English through ROGER, Quantiiv’s email-based AI analyst.

Instead of logging into dashboards or writing SQL, executives simply email questions like “How did sourdough perform last month across Montana stores?” and receive analysis back in their inbox — complete with relevant analysis, comparisons, and insights drawn directly from the unified data warehouse.

ROGER transforms the consolidated infrastructure into an always-available analyst, making the data as accessible as sending an email.

Automating the Narrative: From Pull to Push

Traditional reporting architectures are pull-based: someone has a question, logs into a dashboard, and hunts for an answer.

We inverted that model.

Great Harvest now runs on push-based, narrative automation:

Weekly synthesis: Every week, the system generates and distributes a structured digest highlighting what changed — top movers, anomalies, trends — across all bakeries in the warehouse.
Contextualized alerts: Instead of raw deltas, stakeholders receive insights like “Sourdough sales declined 5% WoW in Midwest regions, likely due to seasonal shifts — Montana and Colorado stores bucked the trend.”
Proactive delivery: Reports arrive in inboxes like clockwork. Leadership doesn’t go looking for the story of the business — the business tells its own story.

This is a fundamental shift: from data as a resource you query to data as a system that communicates.

Reinventing the Monthly Business Review

The Quantiiv team worked with Great Harvest to set up and manage a business cadence, working with leadership to lead a monthly business review operating on a live, unified dataset.

What used to require a month of lead time to aggregate data has now been consolidated to a few sql queries and can be put together by the Quantiiv team inside of a day. The time is now spent on what to do next instead of gathering the data.

The MBR has evolved from a backward-looking report into a live operating rhythm — powered by infrastructure that allows for real-time insights.

The Outcome: A Modern Data Organization

In just a couple of months, Great Harvest transformed from fragmented, siloed reporting to an integrated, self-narrating data system:

Multiple POS systems unified into a single, canonical data layer
Automated weekly storytelling with push-based narrative delivery
Monthly reviews driven by real-time, trusted data via the Quantiiv Console and Data Warehouse.
Franchise flexibility preserved — no vendor lock-in. The system works for you, not the other way around.

Great Harvest now operates with the speed, clarity, and confidence of a company built on modern data infrastructure — because they are.

Infrastructure as Competitive Advantage

At Quantiiv, we believe every restaurant brand should be able to ask a question — any question — and get a consistent answer, instantly, regardless of what system generated the data.

Great Harvest’s transformation demonstrates what becomes possible when data infrastructure is treated as a first-class product rather than a reporting afterthought.

The payoff isn’t dashboards. It’s organizational clarity, decision velocity, and structural confidence.

If your brand is managing multiple POS systems and struggling to unify your business story, the problem isn’t complexity — it’s foundation. Fix the infrastructure, unlock the business.

If this sounds familiar, let’s talk. We love a good challenge. Get in touch with us here.