OMX Innovation · Deck 16 · Product Data Enhancement (PDX → Snowflake)
PDX takes all product data — ranged or not — and we round un-ranged SKUs through Snowflake as the staging layer.
This deck is the work to get every SKU we sell, quote, or might one day stock into PDX with the quality our search, quote, switching and IBP layers depend on.
01 / 06
Why now

The wedge.

02 / 06
What this covers

What's in scope.

01
PDX as the master
ranged or not, all product data lives there
02
Snowflake staging for un-ranged
when a SKU is quoted, requested, or scraped via Plex-CI, it lands in Snowflake first; promoted into PDX when it qualifies
03
AI-assisted enrichment
descriptions, attributes, dimensions, GTINs, image-presence checks, taxonomic classification — generated/validated by AI then human-reviewed
04
Data-quality scorecard
completeness, accuracy, consistency, age — per SKU, per category, per supplier
05
Spec sheet capture
supplier loop reduction; pull from supplier portals when possible, OCR when not
06
Image normalisation
catalog hero, alt views, scale references — standardised format
03 / 06
The problem

What's broken.

01
Off-range = unmanaged
when quoting team finds a SKU outside the ranged catalog, it's a multi-day supplier loop
02
Spec-sheet hunt
quoting/customer-service rep manually searches supplier websites for product information
03
Inconsistent attributes
same product might be described 3 different ways across PDX, Pronto, web, supplier feeds
04
Image gaps
many SKUs have no hero, alt views, or scale references
05
No data-quality SLA
no scorecard, no remediation backlog, no clear ownership
06
Snowflake as accidental graveyard
un-ranged SKUs sit there because nobody promotes them up
04 / 06
The benefits

The value story.

Lever
Mechanism
Sizing
Quoting speed
Spec sheets + attributes immediately available
Quote turnaround 3-5 days → <1hr (Deck 04 benchmark)
Search relevance
Better attributes = better search results
Conversion lift in Deck 08
Switching enablement
Cross-references to competitor SKUs
Deck 09 funnel works
AI enrichment cost
$0.01-0.10 per SKU enrichment vs $5-50 manual
Tens of thousands of SKUs × $5+ savings = material
Margin lift
Better data = better pricing decisions in PPSS/CI
Indirect; supports Deck 02 / Deck 13
05 / 06
The ask + roadmap

What we need.

Now
Problem vector grid (4)
: Off-range-pain / Spec-sheet-hunt / Image-gaps / Attribute-inconsistency
P2
PDX as the centre — round-trip diagram
: Ranged SKU → PDX. Un-ranged → Snowflake → enrichment → promote → PDX.
P3
AI-enrichment pipeline
: Supplier feed → AI propose → human-validate → PDX commit. Per-SKU economics.
P4
Data-quality scorecard
: completeness × accuracy × consistency × age — visual heat map by category
P5
Before/after a SKU record
: today's sparse data vs the enhanced data
Audience
Primary: Chief Commercial Officer + Master Data lead + Chief Digital Officer. Secondary: Customer Service (quoting team) + Sales (off-range frequent flyers). Tertiary: Buying — data quality drives pricing decisions.
References
  • Memory: PDX as the master, Snowflake as un-ranged round-trip (Jeff verbal 2026-06-28)
  • Memory: Deck 05 Global Catalog research — off-range margin leak $1.8M-$4.8M annual; PIM tools $100k-$300k/yr packaged
  • Memory: OMX dbt models at lens/Current/libraries/dbt/models/presented/ — existing structured product data
  • Memory: Plex-CI as competitor SKU data source — feeds the cross-reference layer
  • Memory: FDL REVIEW_OMX_dbt v2.0 standards — landing → ODS/STAGING → DW → PRESENTED; this deck's data flows fit that pattern
06 / 06
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