Data quality
Quality is a process, not a headline number
We describe our methodology rather than quoting an accuracy percentage we can't evidence. Transparent, repeatable, documented.
Four dimensions
How we define quality data
Good data isn't just a high accuracy claim — it's measurable across four distinct dimensions that together determine whether your team can actually use it.
Accuracy
Records are checked against sources to reduce errors and bounces.
Completeness
We fill the fields your team needs to segment and act, not just the obvious ones.
Consistency
Standardised formats and de-duplication keep your database coherent.
Timeliness
Scheduled refreshes help limit decay as companies and contacts change.
Our approach
Verification, cleansing and security
Source-based checks to confirm contact and company data is current and accurate.
Automated de-duplication and standardisation to remove noise from your database.
Privacy-conscious handling with documented access controls and data governance.
Methodology
Our verification approach
A documented, repeatable pipeline. The exact checks depend on the dataset and your requirements — we never promise what we can't evidence.
Ingest
Data is received from permissioned sources and staged for processing.
Standardise
Field formats, naming conventions and phone numbers are normalised.
Deduplicate
Duplicate detection removes redundant records across sources.
Verify
Source-based checks validate key attributes against reference data.
Enrich
Missing firmographic attributes are appended where possible.
Deliver
Clean, verified data is delivered in your preferred format.
Ready to work with better data?
Tell us what you're trying to reach. We'll review your requirements and respond with next steps and, where relevant, a sample.
After you submit, a specialist reviews your request and replies by email — typically within two business days.