How data refinement works
One number. One truth.
Every record is normalized, every duplicate resolved, every field scored for confidence. No more arguing over which dashboard is right.
Which dashboard is actually right when two teams report different numbers?
- The same metric is defined differently across two tools
- Duplicate records inflate counts without anyone noticing
- A bad feed corrupts a report before anyone catches it
- Fixing a data issue means redoing the same cleanup by hand
- One taxonomy and metric definition inherited by every source
- Duplicates are matched and resolved before they reach a report
- Every record carries a trust score you can see
- Rows that fail are held with the reason, never silently dropped
From a million raw rows to an addressable audience
Quality work runs in the open, so you know exactly what survived and why.
- What was held back, and why, shown rather than hidden in a log
- Feeds that keep failing are flagged
- Addressable counts match what activation actually receives
Three ways your data becomes trustworthy
Each capability ships enabled. Nothing here is a services engagement.
ONE GRAMMAR FOR ALL YOUR DATA
Map it once. Every future import follows the rules.
- Field names, date formats and currency codes harmonize into one schema
- No manual reformatting when a new source arrives
DUPLICATES RESOLVED AUTOMATICALLY
One person. One record. Always.
- Duplicates across sources matched and resolved
- Raw personal data never exposed in the process
EVERY RECORD SCORED FOR TRUST
Quality you can measure and filter.
- Every record carries a trust score you can see
- Filter, rank and present records with that score attached
Raw rows to a dataset your finance team trusts
Every row is checked, matched and scored before anyone reports on it. Rows that fail are held with the reason attached. You decide whether to fix the source, adjust the rule or accept the row on record.
Four jobs it pays for in the first quarter
Ending the dashboard disagreement
Every dashboard reads the same validated dataset, so two teams stop reporting two different numbers.
Onboarding a new market
The taxonomy and validation rules apply automatically, without a fresh mapping project.
Auditing a vendor feed
Source reliability scores flag a feed that keeps failing before it corrupts a report.
Preparing data for activation
Deduplicated, scored records are what downstream segments and audiences are actually built from.
Questions buyers ask
What does a validation rule actually catch?
Missing required fields, impossible values, currency and date drift, duplicate rows across sources, and metrics that break their own definition, such as clicks above impressions.
Who decides the taxonomy?
You do. Naming and metric definitions are configured once at the workspace level, then inherited by every future load, including new markets and new brands.
What happens to rows that fail?
They are quarantined with the reason attached, not silently deleted. You can fix the source, adjust the rule, or accept the row with a recorded override.
Does this slow down reporting?
No. Dashboards read the cleaned dataset, so the quality work is done before anyone opens a report.
Run a quality report on your current data
We load a recent month, apply the validation rules, and show you exactly which rows would have failed and why.