Accelerating Retail & CPG Data Modernization with Snowflake’s Openflow and Tredence’s Sancus on AI Data Cloud

Retail and Consumer Packaged Goods (CPG) organizations operate in one of the most data-intensive and operationally complex environments. Every day, millions of product, vendor, inventory, and supply chain transactions flow across internal systems and external trading partners. Most enterprises still struggle with inconsistent, incomplete, and inaccurately structured product and supply chain data.

This fragmented data foundation leads to chronic operational challenges:
• Productivity loss due to manual data correction
• Inaccurate reporting and delayed business insights
• Inventory imbalances and stockouts
• Inconsistent product experiences across markets, channels, and systems

The modern data stack, with Snowflake at its core, now makes it possible to eliminate these issues at scale.

This blog presents a reference architecture and approach for Retail/CPG clients to ingest & parse GS1/EDI data using Snowflake’s Openflow, transform and aggregate data using Snowflake native features and harmonize product information using Tredence’s Cortex-enabled Data Harmonization tool, Sancus.

1. The Data Challenge in Retail & CPG: Why Standards Alone Are Not Enough

Different Retail & CPG enterprises opt for different ways to interoperate and exchange data across the complex vendor, partner and channel network. Some common ways are:

•         GS1 Global Data Synchronization Network (GDSN)
•         EDI messages (810, 850, 855, 856, 940, 945, etc.)
•         Vendor product catalogs
•         Marketplace product feeds
•         Internal PLM/PIM/ERP systems

Standardization is only as good as the quality of data. Even though these datasets carry standardized structures, variations are common due to:

•         Missing or inconsistent product attributes
•         Varying taxonomies across regions or channels
•         Vendors providing data in different formats or file structures
•         Ambiguity in product hierarchies (e.g., flavor variants, bundles, regional SKUs)
•         Lack of persistency or quality scoring in legacy ingestion platforms

The outcome is a fragmented product data ecosystem that slows down analytics, drives up supply chain costs, and creates inconsistencies in the customer experience.

2. Snowflake Openflow: A Modern Ingestion Framework for Structured & Unstructured Data

Snowflake Openflow running natively in Snowpark Container Services(SPCS) or Bring Your Own Cloud (BYO),brings a declarative, scalable, and low-code way to ingest structured,  semi-structured & unstructured data into the Snowflake Data Cloud. It supports:

•         Event-driven ingestion from cloud storage
•         Native support for JSON, XML, CSV, Parquet, Avro
•         Schema inference and evolution
•         End-to-end pipeline orchestration within Snowflake
•         Zero-infrastructure ingestion (no servers or third-party schedulers)

Fig 1: Connect and Ingest Data with Snowflake Openflow

This image shows the overall Snowflake Openflow roadmap of connectors and how it can help bring a variety of data from different data sources.

Because Openflow supports flexible transformations during ingestion, Retail/CPG enterprises can normalize formats, enforce quality rules, and store raw & conformed versions with minimal code.

 3. Parsing GS1 / EDI Data at Scale: From Vendor Files to Analytical Gold

GS1 files are one of the most common datasets landing into large Retail/CPG organizations. Here are some of the key GS1 standards and their definitions:

Fig 2: GS1 Data Ingestion & Parsing Architecture

This diagram explains the overall data ingestion & parsing architecture for GS1 files. It would typically be a four-step process:

1. Ingest GS1 data from Sources – This sources of data could be anything from an internal POS system to the ERP system, or an API call to a SaaS tool, or some vendor provided file upload.
2. Routing & Parsing - ‘GS1 Auto Detect Router’ automatically detects GS1 data type and routes to an appropriate parser:
1. ‘Parse GS1 AI String’ – Parses 150+ Application Identifiers from barcode scans into structured JSON
2. ‘Resolve GS1 Digital Link’ – Extracts Application Identifiers from GS1 Digital Link URIs (QR codes, smart packaging)
3. ‘Validate And Flatten EPCIS’ – Validates EPCIS 2.0 JSON events and flatten into 3 normalized tables (events, EPCs, parties)
3. Ingest Parsed Records into Tables – Now that we have data extracted from the GS1 files, we store it in the Raw layer (Bronze)
4. Filter & Process – Continue to filter for required fields & send it further downstream for processing until it reaches a Transformed state (Silver layer)

GS1 files often arrive in XML formats. Openflow can parse GDSN XML and extract Product Master data into normalized JSON structures.

•         Extract product attributes
•         Infer hierarchical structures
•         Convert GS1 attribute sets into Snowflake variant or relational structures
•         Capture lineage from raw XML → parsed layer → harmonized data model

Here are some sample Input & Output examples of the GS1 parsers running on Openflow:

Similarly, EDI documents require segment/element parsing (ISA, GS, ST, etc.) that can be achieved using Openflow.

The outcome is a fully conformed data foundation that connects orders, shipments, receipts, product attributes, and vendor records.

4. Aggregation & Transformation: Building Retail/CPG-Ready Data Models

Once data is ingested in the Raw layer, Snowflake enables high-performance transformations using Snowpark for Python, Dynamic Tables for incremental processing, DBT in Snowflake & Snowpark Container Services.

Core transformations for CPG include:

•        Product Master Aggregation
  •        Consolidate GS1 item master, EDI item references, and internal ERP/PLM  records
  •        Align global, regional and market-specific product variations
  •        Generate a “360 Product Record” with lineage
•        Supply Chain Document Linkage - Link EDI events to build:
  •    PO lifecycle visibility
  •    Fill rate and OTIF analytics
  •    Inventory availability snapshots
  •    Warehouse throughput metrics
•        Data Quality & Conformance Rules
  •    Standardize units of measure (UOM conversions)
  •    Normalize brand/category/taxonomy attributes
  •    Deduplicate vendor product entries
  •    Validate missing dimensions, weights, or regulatory attributes

5. Sancus: AI-Driven Data Harmonization

Even after data ingestion and Snowflake transformations, Retail/CPG companies still face:

•         Duplicate SKUs
•         Conflicting attribute values
•         Inconsistent taxonomy across regions
•         Missing or inaccurate product attributes
•         Marketing vs supply chain attribute mismatches

Sancus fills these gaps with:

1. Entity Resolution & Duplicate Removal
   AI similarity models detect duplicates across data, vendor catalogs, marketplace feeds and internal product masters.
2. Attribute Completion & Error Correction
   Sancus leverages Snowflake Cortex LLM functions (e.g., COMPLETE, TRANSLATE) to intelligently parse ambiguous descriptions, while Snowpark ML handles deterministic entity resolution such as dimensions and allows users to accept or reject the matched records.
3. Product Taxonomy Mapping
   Auto-classification into specific pre-defined data structures and product match using extracted attributes.
4. Golden Record Generation
   Automated consolidation of multiple versions into a single, trusted “golden record” per product.
5. Continuous Learning
   As new data arrives, the system learns patterns and improves match accuracy
6. Multi-Language Support
   Cortex AI Functions support multiple languages across Customer & Product entities

Fig 3: Sancus Solution Overview

Fig 4: Entity Extraction Process

Fig 5: Product Match Process Example

Figure 3, 4 & 5 explain the working of Sancus from Attribute Extraction to Harmonization and User Validation, ultimately leading to a Golden Record or Product Master.

 6. Reference Architecture: End-to-End Retail/CPG Data Foundation

Openflow + Sancus = A Closed-Loop Data Enhancement System

Openflow ingests → Snowflake transforms → Sancus harmonizes → Snowflake consumption layers deliver analytics, APIs, and data sharing.

A typical solution architecture would include:

1. Openflow Ingestion Layer
   Raw GS1/GDSN, EDI, vendor feeds, marketplace catalogs.
2. Raw & Parsed Storage Layer
   XML/EDI variants, parsed relational structures.
3. Transformation Layer
   Dynamic Tables, Snowpark, rule-based validation.
4. Sancus Data Harmonization Layer
   Golden record creation & taxonomy mapping.
5. Conformed Data Models
   Product master, vendor master, PO/ASN/Invoice models, supply chain visibility datasets.
6. Consumption Layer
• BI dashboards (inventory, availability, demand, compliance)
• Supply chain KPIs (OTIF, fill rate)
• Data sharing with vendors
• API services to downstream systems
• AI/ML models for Predictive and Prescriptive Analytics

Fig 6: Reference Architecture

7. Business Value for Retail & CPG Organizations

Operational Productivity

•         Improve product matching accuracy up to 100%, thereby reducing 40–60% of manual data cleanup effort
•    Automated harmonization process accelerates item onboarding and vendor integration time by 3X
•    Harmonize additional customer data like Addresses using geolocation API’s
•    Work with data across various languages reducing manual/system translation overheads

Inventory Optimization

•         Improve forecast accuracy
•         Reduce phantom inventory and stockouts
•         Enable unified SKU visibility across channels

Regulatory & Compliance Readiness

•         Ensure GS1 compliance
•         Enforce global and regional labelling requirements
•         Maintain audit trails and lineage of product changes

Enhanced Customer Experience

•         Accurate, enriched product content
•         Consistent digital shelf across channels
•         Lower return rates due to accurate descriptions/specs

Strategic Advantage

•         A scalable data foundation for AI, personalization, demand planning, and omnichannel optimization

 8. Why Partner with Our Snowflake Data & AI Services Team

•         As a Snowflake ELITE partner and Partner of the Year 2025, Tredence brings best-in-class Snowflake engineering & AI/ML expertise
•         Tredence has been consistently named ‘Leader’ by top analyst groups (like ISG, Forrester & Gartner) for our industry domain knowledge across Retail, CPG, Supply Chain & eCommerce
•         We have proven accelerators (like Sancus) for GS1/EDI ingestion, parsing and conformance that work seamlessly with Snowflake
•         Our delivery teams accelerate time-to-market with more than 100 unique accelerators that reduce implementation timelines by 40–50%

This allows Retail/CPG clients to achieve faster time-to-value, lower total cost of ownership, and higher business confidence in their data.

Conclusion

Snowflake’s Openflow and Tredence’s Sancus, enable Retail/CPG organizations to build a unified, trusted, and intelligent product and supply chain data foundation. By automating ingestion, transformation and harmonization, enterprises can significantly reduce data errors, accelerate operational workflows, and unlock advanced analytics and AI use cases.

This is the future of product and supply chain data management – higly automated, AI-enhanced, domain-aware, and built entirely on Snowflake.

AUTHOR - FOLLOW
Sumit Bhatia
Snowflake Field CTO, Tredence