Raw scraped data is messy. We make it spotless.
Stop wasting hours in spreadsheets. Our managed data cleaning pipelines automatically deduplicate, normalise, fill in gaps, and map inconsistent data into your exact schema — so every record that lands in your warehouse is analysis‑ready.
rules:
- dedup: "canonical_url + title_fuzzy"
- normalise: "phone → E.164"
- fill_missing: "price → median_of_category"
- schema_map: "Colour → color"
output: "snowflake.public.clean_data"
18% of raw records
42 source fields → 38 canonical
Trusted data from leading platforms
Clean data is the foundation of every trusted dashboard
Data cleaning is the essential but often overlooked step that transforms raw web‑scraped feeds into reliable, business‑ready datasets. Our managed service eliminates duplicates, normalises inconsistent values, fills in missing fields, and enforces your custom schema — all automatically, on every new batch of data. No more manual Excel wrestling before your analysts can start working.
We configure a rule engine specific to your data sources and internal standards. Once deployed, it operates in‑flight — data is cleaned the moment it’s extracted, before it ever reaches your warehouse. Ongoing maintenance means if a source changes its field names or format, our team updates the rules, not yours.
- Duplicate detection via URL, content hash, or fuzzy matching
- Field normalisation: dates, phones, currencies, units
- Missing value imputation with configurable strategies
- Attribute mapping to your internal taxonomy and allowed values
- Delivered clean, validated, and ready for analysis
Why “just export to CSV” isn't the end of the story
Raw scraped data is full of hidden inconsistencies that break dashboards, corrupt models, and waste hours of analyst time.
Duplicates inflate your metrics
The same product scraped from three retailers under slightly different titles creates triple‑counted revenue figures. Automated fuzzy deduplication collapses them into a single, truthful record.
Inconsistent formats block automation
One source uses “Colour”, another uses “Color/Finish”. Manual mapping breaks every time a new retailer is added. Our rule engine maps them to your canonical schema automatically.
Missing fields create blind spots
15% of product records missing a weight or dimension makes competitive analysis impossible. Our pipelines fill gaps using configurable strategies — so you never have to filter out incomplete rows again.
An automated cleaning pipeline that runs on every record
Every feature below is configured once and applied continuously — no human intervention needed.
Intelligent deduplication
Combine exact matching on unique IDs with fuzzy title and content similarity to remove duplicates — even when the same product appears under different names.
Canonical schema mapping
Map any source field to your internal names, units, and value lists. “Colour” becomes “color”; inches convert to centimetres; “M” maps to “Medium”.
Smart missing‑value handling
Drop rows with too many gaps, fill missing values with category medians, or flag them for review — you define the logic per field and per source.
Validation & quarantine
Every record is checked against your schema. Valid data flows into the main table; questionable records are quarantined for review — with a clear report of what was flagged and why.
{
"batch_id": "2026-07-22T08:00Z",
"records_raw": 12400,
"duplicates_removed": 2230,
"records_cleaned": 10170,
"quarantined": 12
}
From raw extract to a trusted, clean dataset
Source audit & rule design
We profile your raw data to identify inconsistencies, duplicates, and gaps — then design a cleaning rulebook mapped to your internal standards.
Pipeline configuration
Our engineers configure the automated cleaning engine, including dedup logic, normalisation functions, and schema mappings.
Sample validation
A test batch is cleaned and delivered alongside a quality report — you verify accuracy, coverage, and that the output matches your expectations.
Production deployment & monitoring
The pipeline runs automatically on every new data load. We monitor cleaning stats and adapt rules when source formats change.
Deliverables for every data cleaning engagement
Cleaning rulebook & schema alignment
A document defining every dedup rule, normalisation function, and field mapping — reviewed and approved by your data governance team.
Sample cleaned dataset
A batch of raw data processed through the cleaning pipeline, delivered with a quality report showing duplicates removed, fields normalised, and any quarantined records.
Production cleaning pipeline + runbook
Automated cleaning live on your data feed. A runbook documents the rules, quality thresholds, and how to request changes.
Monthly data quality report
Summary of dedup rates, field completeness, quarantined records, and any source‑side changes handled — delivered proactively.
Flexible plans for any cleaning scope
One‑Time Cleanse
Best for a single batch of raw data that needs cleaning and standardisation before a one‑off analysis or migration.
- Fixed scope & price
- Dedup, normalise, schema‑map
- Single delivery of clean data
Managed Cleaning Feed
Ongoing cleaning of every new data batch — we maintain the rules, you enjoy consistently clean data.
- Automatic per‑batch cleaning
- Rule maintenance & adaptation
- 99.9% schema compliance SLA
Enterprise Data Quality
For complex multi‑source environments, custom quality rules, and dedicated data quality engineers.
- Unlimited sources & rules
- Custom validation & enrichment
- SSO, audit logs, quarterly reviews
Every team that relies on web data for decisions
E‑commerce & Retail
Ensure competitor prices, product attributes, and stock data are deduplicated and consistently formatted — before they hit your repricer.
Finance & Investment
Clean alternative datasets — news feeds, public filings, market data — so your models train on accurate, complete information.
Real Estate
Normalise property listings from multiple portals — unify price formats, area units, and amenity descriptions into a single view.
Business Intelligence
Feed your dashboards with clean, trusted data — no more “why does this number look wrong?” questions in Monday meetings.
Data cleaning vs. manual spreadsheet wrangling
| Capability | Managed Data Cleaning | Manual Excel Cleaning | In‑House Scripts |
|---|---|---|---|
| Fuzzy deduplication | |||
| Schema & unit normalisation | |||
| Missing value imputation | |||
| Automated, per‑batch execution | |||
| Ongoing rule maintenance | Included | Your team | Your team |
| Time to clean a 100K‑record dataset | Hours (automated) | Days to weeks | Days of scripting |
“Our analysts finally trust the numbers — and they stopped cleaning data manually”
"We had 500,000 raw product records from 8 retailers — full of duplicates and inconsistent attributes. ScraperScoop's cleaning pipeline deduplicated 23% and mapped everything to our taxonomy. Our product team now works with a single source of truth."
"The missing‑value imputation is what sold us. We had gaps in pricing data that were making our competitive analysis unusable. Now those gaps are filled with category‑median estimates — and we know exactly which records were imputed."
"We used to have one analyst spending 15 hours a week manually cleaning scraped data in Excel. The managed cleaning pipeline now does it automatically every night — and that analyst is finally doing real analysis."
Clean data lands directly in your existing stack
Schema‑mapped, validated, and ready to query — no manual import steps required.
Get a fixed‑price quote for your data cleaning project
Share a sample of your raw data and your target schema — we'll return a cleaned sample and a fixed‑price estimate within two business days.
Common questions about Data Cleaning
Your raw data, transformed into a spotless, analysis‑ready dataset
Send us a sample of your scraped data and your target schema. We'll return a cleaned sample within two business days — no commitment, no sales pitch.
Most cleaning pipelines deliver the first clean dataset within 2–3 weeks of kickoff.
