Raw scraped data is messy. We make it analysis‑ready.
Every retailer names things differently — “Colour” vs “Color”, centimetres vs inches, EUR vs USD. Our normalization pipelines clean, standardise, and unify product data from any source, mapping every attribute to your internal schema, converting units, and merging duplicates. The result: a single, trustworthy product feed that drops directly into your BI tools, PIM, or pricing engine.
rules:
- map: "Colour" → "color"
- unit_convert: "weight_oz" → "weight_g" (*28.35)
- currency: "EUR→USD" (daily rate)
- size_normalize: "US‑letter" → "EU‑numeric"
output: "snowflake.products_normalized"
4,200 records this hour
142 source fields → 38 canonical
Normalization pipelines running for
Stop cleaning product data by hand
Product normalization is the final, essential step that turns raw web‑scraped data into a business asset. It automatically resolves the inconsistencies that come from collecting product information from multiple retailers — unifying attribute names, converting measurement units, normalising prices to a single currency, and standardising date and status formats. Instead of spending hours in spreadsheets, your team gets a clean, consistent dataset delivered on schedule.
We build a custom rule engine mapped to your internal taxonomy. Once live, every new product record passes through these rules before landing in your warehouse — guaranteeing that every “colour” is spelled “color”, every weight is in grams, and every date is in ISO 8601.
- Attribute name mapping to your canonical schema
- Unit conversion (imperial ↔ metric, apparel sizes)
- Currency normalisation with configurable rate sources
- Date, status, and category standardisation
- Duplicate detection and merge with configurable survivorship
Why “just export to CSV” leaves you with a data mess
Raw scraped data is full of hidden inconsistencies that break dashboards, corrupt repricing rules, and waste analyst hours.
Every retailer speaks a different dialect
One calls it “Colour”, another “Color/Finish”, a third uses “Farbe”. Without a mapping layer, you can’t filter, compare, or merge product records across sources.
Units are all over the place
A US site lists a laptop weight as 4.2 lbs; a German site lists the same model as 1.9 kg. Your comparison dashboard doesn’t know they’re the same — until you normalise to a single unit.
Duplicates inflate your catalog size
The same product may appear under slightly different titles or SKUs from different retailers. Without deduplication and merging, your competitive analysis is based on inflated counts and wrong averages.
A rule engine that cleans as it collects
Normalization happens in‑flight — data is transformed the moment it’s extracted, before it ever reaches your warehouse.
Canonical attribute mapping
We map every source field to your internal taxonomy — “Colour” becomes “color”, “Material” becomes “fabric”, and so on. The mapping document is reviewed and signed off by your team.
Unit & currency conversion
Define your target units (metric, imperial) and base currency once. Every incoming value is automatically converted — weight, dimensions, volume, and price.
Regional size normalisation
Convert apparel and footwear sizes between US, UK, EU, and JP standards using configurable tables — so “M” and “38” land in the same column.
Duplicate detection & merging
Identify and merge records that represent the same product, using configurable survivorship rules (e.g., keep the most complete attributes, average the prices).
{
"source": "retailer‑a",
"transformations": [
"Colour → color",
"Weight (oz) → weight_g (x28.35)",
"Price EUR → USD (x1.09)"
],
"status": "normalized — ready for warehouse"
}
From raw extract to a spotless, standardised dataset
Schema & rule scoping
We review your internal taxonomy and the raw fields from each target retailer, then build a mapping document with unit and currency preferences.
Normalization rule engine build
Our engineers codify the rules into an in‑flight transformation pipeline. Every record is cleaned the moment it’s extracted.
Sample output & validation
A normalized sample dataset is delivered in your schema. You validate field mappings, unit conversions, and duplicate handling.
Production feed & ongoing maintenance
Normalized data flows on your schedule. We monitor for new source fields and update rules proactively — your schema stays consistent forever.
Deliverables for every normalization engagement
Attribute mapping & rule specification
A comprehensive document mapping every source field to your canonical attributes, with unit, currency, and size conversion rules — approved by your data governance team.
Normalized sample dataset
A representative sample of products, fully normalized, delivered in your schema. You verify that every transformation is correct before full‑scale rollout.
Production normalization pipeline + runbook
All incoming data is normalized in real time. A runbook documents the mapping, conversion rules, and how to add new attributes or retailers.
Monthly schema health report
Summary of any new source attributes detected, rule updates applied, and overall data quality metrics — shared proactively with your team.
Flexible plans for every normalization scope
One‑Time Normalization
Best for a single batch of raw data that needs cleaning and standardisation before analysis.
- Fixed scope & price
- Full attribute mapping & conversion
- Single delivery of clean data
Managed Normalization Feed
Ongoing normalization of all incoming scraped data — we maintain the rules, you enjoy clean data forever.
- Daily or weekly normalized delivery
- Automatic rule updates for new fields
- 99.9% field consistency SLA
Enterprise Data Governance
For organisations with complex taxonomies, multi‑source data, and strict governance requirements.
- Unlimited sources & attributes
- Custom transformation logic
- SSO, audit logs, quarterly reviews
Every team that feeds scraped data into downstream systems
Product Information Management
Feed your PIM with clean, pre‑normalized competitor data that matches your internal attribute structure exactly.
Business Intelligence & Analytics
Eliminate hours of spreadsheet cleanup — normalized data plugs directly into Tableau, Looker, or Power BI.
Dynamic Pricing
Ensure your repricer compares apples to apples — every competitor price is in your base currency, with tax treatment consistent.
Multi‑Retailer Assortment Analysis
Compare category breadth, pricing, and availability across retailers using a single, standardised data model.
Product normalization vs. manual data cleaning
| Capability | Managed Normalization | Manual Spreadsheet Cleaning | In‑House Scripts |
|---|---|---|---|
| Attribute name mapping | |||
| Unit & currency conversion | |||
| Regional size normalisation | |||
| In‑flight, automated cleaning | |||
| Ongoing rule maintenance | Included | Your team | Your team |
| Time to analysis‑ready data | 2–3 weeks | Weeks per batch | Months to build |
“Our analysts stopped cleaning data and started using it”
"We were spending 15 hours a week manually converting sizes, currencies, and colour names. Now every record lands in Snowflake already in our format. Our analysts haven't touched a spreadsheet in months."
"The attribute mapping document they produced became our de facto data dictionary. We now enforce the same standards across our own internal systems — it's had a ripple effect on data quality everywhere."
"The duplicate merging alone saved us from a major pricing error. We had the same product listed under three slightly different titles across retailers — their normalization pipeline merged them, and we avoided repricing against ourselves."
Normalized data lands exactly where your teams expect it
Schema‑aligned, clean, and ready to query — no manual formatting, no import wizards.
Get a scoped quote for your normalization project
Share a sample of your raw data and your target schema — we’ll return a normalized sample and a fixed‑price estimate within two business days.
Common questions about product normalization
Your raw product data, transformed into a spotless, analysis‑ready dataset
Send us a sample of your scraped data and your target schema. We’ll return a normalized sample within two business days — no commitment, no sales pitch.
Most normalization pipelines deliver the first clean dataset within 2–3 weeks of kickoff.
