Turn the messy data you have into data you can actually use.
We dedup, normalize, validate, and enrich your customer, product, and order data — the duplicates, the bad addresses, the three spellings of the same company. Usually a one-time cleanup, plus the rules that stop the mess coming back. So your reports finally match reality.
Your reports aren't wrong. The data underneath them is.
You can build the most careful dashboard in the world on top of a customer table that lists the same company four times and ships invoices to addresses that don't exist — and it will be confidently, precisely wrong. The fix isn't another report. It's the data itself: deduplicated so each customer counts once, normalized so the same thing looks the same everywhere, validated so bad records get caught, and enriched so the gaps get filled. Do that once properly, leave behind rules that keep it clean, and the numbers stop lying to you.
The data is fighting you.
Each symptom has the same root — and the same fix underneath.
- 01
The same customer appears three times under slightly different names
Deduplication that merges the matches and leaves the genuinely different records alone.
- 02
Invoices and mail bounce because the addresses are wrong
Address validation and standardization against a real postal source.
- 03
A total is off and it turns out a field was blank or malformed
Validation rules that quarantine bad records before they reach a report.
- 04
One product shows up under four different spellings
Normalization that maps everything back to a single canonical value.
- 05
You cleaned it last year and it's already messy again
Entry-point rules and monitoring that stop the mess from creeping back.
From a mess you can't trust to data you can.
The unglamorous work that makes every downstream report honest.
Data audit & scorecard
A measured baseline of duplicates, gaps, and validation failures.
Cleaning & dedup rules
Matching logic that merges the same record without over-merging.
Normalization
Names, addresses, phones, and products into one consistent shape.
Validation pipeline
Bad records caught and quarantined before they reach a report.
Enrichment integrations
Missing addresses, firmographics, and contacts filled from trusted sources.
Quality monitoring
Trend lines that flag the day the data starts slipping back.
Messy in, trustworthy out.
- Audit01
- Dedup02
- Normalize03
- Validate04
- Enrich05
- Monitor06
Each step builds on the last: you can't dedup what you haven't normalized, and there's no point enriching records you haven't validated. The final step — monitoring — is what turns a one-time cleanup into data that stays clean instead of drifting straight back to where it started.
Find the duplicates, prove the damage.
Before we change a row, we measure what's wrong: how many duplicate customers, how many bad addresses, how many blank fields. Then we build matching rules that merge the records that are genuinely the same — weighing name, email, phone, and address together — while leaving the borderline cases for review instead of guessing.
- A scorecard that quantifies the mess
- Multi-signal matching, not single-field guesses
- Auto-merge the clear cases, flag the rest
- Tuned against your real data, with you
Make the same thing look the same.
A customer split across three spellings counts as three customers, and your totals are wrong because of it. Normalization standardizes the formats — phones, addresses, dates, casing — and maps names and products back to a single canonical value, so every record that should be one thing actually is.
- Phone, date, and address standardization
- Canonical names and product mappings
- Consistent casing, codes, and whitespace
- The groundwork that makes dedup work
Catch the bad, fill the gaps.
Validation rules check every record against reality — required fields present, emails and phones well-formed, addresses verified, values in range — and quarantine what fails instead of loading it silently. Then enrichment fills the holes: addresses, geocodes, and firmographics pulled from sources that actually fit your data.
- Required-field, format, and range checks
- Addresses verified against a postal source
- Bad records quarantined, not buried
- Enrichment from sources worth paying for
Clean once. Stay clean.
A one-time cleanup decays the moment new records start flowing in. We move the same dedup, normalization, and validation rules to the point of entry, and keep the quality scorecard running — so if duplicate rates or validation failures start creeping back, you see it on a trend line and we fix the source, not the symptom.
- Rules applied at the point of entry
- An ongoing quality scorecard
- Alerts when the metrics start slipping
- Regression caught early, not next year
Cleaned once — and kept clean.
Audit & scorecard
We measure the state of the data — duplicates, gaps, bad addresses, format chaos — so the cleanup is driven by numbers, not hunches.
Agree the rules
We design the matching, normalization, and validation rules with you and tune them against real records before touching anything at scale.
Clean the existing data
Dedup, normalize, validate, and enrich the data you already have, in priority order, against a baseline we can compare to.
Add the safeguards
We move the rules to the point of entry and stand up quality monitoring, so the mess doesn't quietly return.
Document & hand over
The scorecard, the rules, and a walkthrough — a clean dataset your team can keep clean without us in the room.
The data has become the problem.
The customer table has duplicates and bad addresses
Mail bounces, the same account gets contacted twice, and your customer count is quietly inflated.
Reports are wrong because the data is wrong
No dashboard can fix a total built from records that count one customer as three.
You're about to migrate or merge systems
Moving dirty data just relocates the problem; merging two systems multiplies the duplicates.
Proven data tools, used with discipline.
The things teams ask before a cleanup.
Make the bad data usable again.
Tell us which table you don't trust — the duplicate customers, the bouncing addresses, the products spelled four ways. We'll audit it, clean it, validate it, and enrich it, then leave behind the rules that keep it clean — so your reports finally agree with reality.