Raw data speaks a dozen different languages. We make it fluent.
Every source names things differently — “Colour” vs “Color”, centimetres vs inches, EUR vs USD. Our normalization pipelines automatically map, convert, and standardise every field to your exact schema, so all your data speaks the same language. No more manual spreadsheet wrestling before analysis.
rules:
- map_field: "Colour" → "color"
- convert_units: "weight_oz" → "weight_g" (*28.35)
- currency: "EUR→USD" (daily rate)
- date_format: "MM/DD/YYYY" → "ISO 8601"
output: "snowflake.public.normalized_data"
42 source → 38 canonical
EUR, GBP, JPY → USD
Trusted data from leading platforms
One standard, every source, zero manual work
Data normalization is the automated process of transforming raw, multi‑source data into a single, consistent format. It maps source field names to your canonical schema, converts units and currencies, standardises date and number formats, and aligns value lists — so every record that lands in your warehouse is immediately comparable and analysis‑ready, regardless of where it came from.
Our normalization engine is custom‑configured to your exact standards. Once live, it runs in‑flight — data is transformed the moment it's extracted, before it ever reaches your downstream systems. And when sources change, our team updates the rules without any work from you.
- Field name mapping to your canonical attribute names
- Unit conversion (imperial ↔ metric, apparel sizes, etc.)
- Currency normalization with configurable exchange rates
- Date, phone, and number format standardisation
- Value list alignment (e.g., “M” → “Medium”)
Why inconsistent data breaks your analytics
When every source uses its own dialect, your dashboards become a guessing game — and your team spends more time cleaning than analysing.
Every source speaks a different language
One site calls it “Colour”, another “Color/Finish”, a third “Farbe”. Without a mapping layer, you can't compare, filter, or merge records across sources.
Units and currencies are all over the place
A US listing shows weight in pounds; a German one uses kilograms. Prices appear in local currencies with no common baseline. Without normalization, your “average price” is meaningless.
Formats break automated workflows
Dates in MM/DD/YYYY vs DD/MM/YYYY, phone numbers with or without country codes, numbers with commas as decimal separators — these inconsistencies break ETL pipelines and cause data import failures.
A normalization engine that speaks your data language
Every feature below is configured to your standards — once, and then it runs forever.
Canonical schema mapping
We map every source field to your internal names, data types, and allowed values — “Colour” becomes “color”, “Size (US)” maps to your sizing standard, and statuses like “in_stock” become “Available”.
Unit & currency conversion
Define your target units (metric, imperial) and base currency once. Every incoming value is automatically converted — weights, dimensions, volumes, and prices.
Format & value standardisation
Dates to ISO 8601, phone numbers to E.164, numbers with consistent decimal separators, and categorical values to your predefined lists — all transformed automatically.
Self‑healing rules
When a source changes field names or formats, our monitoring detects the drift and our team updates the mapping — typically before you notice a gap in your data.
{
"source": "retailer‑a",
"transformations": [
"Colour → color",
"Weight (oz) → weight_g (×28.35)",
"Price EUR → USD (×1.09)"
],
"status": "normalized — ready for warehouse"
}
From raw extract to a perfectly normalised dataset
Source audit & schema alignment
We profile your raw data from each source, identify inconsistencies, and design a mapping to your canonical schema — with your team's sign‑off.
Normalization engine configuration
Our engineers codify the mapping, unit conversions, currency rules, and format transformations into an automated pipeline.
Sample validation & review
A test batch is normalized and delivered alongside a quality report — you verify that every field is correctly mapped and converted before production.
Production deployment & monitoring
Normalization runs in‑flight on every new data load. We monitor for source‑side changes and adapt rules without interrupting your data flow.
Deliverables for every data normalization project
Normalization rulebook & schema map
A document mapping every source field to your canonical attributes, with unit, currency, and format conversion rules — approved by your data governance team.
Sample normalized dataset
A batch of raw data processed through the normalization pipeline, delivered in your schema for accuracy and completeness review.
Production normalization pipeline + runbook
Automated normalization live on your data feed. A runbook documents the rules, conversion logic, and procedures for adding new sources.
Monthly data consistency report
Summary of field mapping accuracy, conversion volumes, and any source‑side changes handled — delivered proactively to your team.
Flexible plans for every normalization scope
One‑Time Normalize
Best for a single batch of raw data that needs to be standardised before a one‑off analysis or migration.
- Fixed scope & price
- Full schema mapping & conversion
- Single delivery of normalized data
Managed Normalization Feed
Ongoing normalization of all incoming data — we maintain the rules, your data stays consistently standardised forever.
- Daily or per‑batch normalization
- Automatic rule updates for new fields
- 99.9% field consistency SLA
Enterprise Data Standardization
For complex multi‑source environments, custom transformation logic, and dedicated data engineers.
- Unlimited sources & custom rules
- Dedicated normalization team
- SSO, audit logs, quarterly reviews
Every team that feeds scraped data into downstream systems
Business Intelligence & Analytics
Ensure every KPI is based on consistent units, currencies, and field names — no more “why does this number look wrong?” moments.
Dynamic Pricing
Feed your repricer with prices in your base currency, with tax treatment consistent — so comparisons are always apples‑to‑apples.
Product Information Management
Ingest competitor product data already mapped to your internal attribute taxonomy — plug directly into your PIM without manual reformatting.
Financial & Market Research
Standardise alternative datasets — economic indicators, stock data, public filings — so your models train on consistent, comparable inputs.
Data Normalization vs. manual spreadsheet transformations
| Capability | Managed Data Normalization | Manual Excel Cleanup | In‑House Scripts |
|---|---|---|---|
| Canonical schema mapping | |||
| Unit & currency conversion | |||
| Format & value standardisation | |||
| Automated, per‑batch execution | |||
| Self‑healing when sources change | |||
| Time to normalize a 100K‑record dataset | Hours (automated) | Days to weeks | Days of scripting |
“Our analysts finally stopped wrangling field names and started analysing data”
"We pull data from 12 different retailer APIs — every single one uses different field names and units. ScraperScoop's normalization pipeline mapped them all to our internal schema. Our team hasn't touched a spreadsheet in months."
"The currency conversion alone saved us hours per week. All prices now land in USD with historical exchange rates applied — our finance team finally trusts the competitive pricing reports."
"When one of our source sites redesigned and changed all its field names, they updated the mapping within 24 hours. We didn't even notice a gap in the data. That kind of proactive maintenance is invaluable."
Normalized data lands exactly where your teams work
Schema‑aligned, consistent, and ready to query — no manual formatting required.
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 Data Normalization
Your raw, inconsistent data — transformed into a spotless, standardised 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.
