Case study
Analysts spend up to 80% of their time preparing data. We give them that time back.
Duplicate customer records, badly formatted phone numbers, village names spelled five different ways: these flaws look harmless, yet they distort every indicator computed afterwards.
We audit your datasets, measure their quality across six dimensions (accuracy, completeness, consistency, uniqueness, validity, timeliness), then fix them with documented, reproducible rules in Python, SQL or your own tools.
Above all, we put safeguards in place so errors don't come back: master data, entry controls, quality alerts.
- −95%duplicates in a customer base
- 6quality dimensions measured
- 100%of rules documented and reusable
Our know-how
- 01
Quality audit
Automatic profiling, quality score per table and field, anomaly mapping.
- 02
De-duplication & matching
Fuzzy matching (names, addresses, phones), record merging, history preserved.
- 03
Standardisation & enrichment
Harmonised formats, geographic reference data, geocoding, open-data enrichment.
- 04
Master data management
A single version of the truth for customers, products, sites or beneficiaries.
Case studies
Context, solution, result: two representative assignments.
Context
Four branches, four software packages, 210,000 customer records, many of them duplicates: actual exposure per customer was unknown.
Solution
Quality audit, fuzzy matching on name, date of birth and phone, business validation of ambiguous cases, a unique customer ID.
Result
A consolidated base of 148,000 unique customers, reliable outstanding amounts, regulatory reporting in one day instead of a week.
Context
Some beneficiaries registered several times, others missing from payment lists.
Solution
Identity standardisation, cross-checks with payment lists, a traced complaints procedure.
Result
A clean register, better-targeted payments and a full audit trail for the donor.
Frequently asked questions
We can work on your servers, in a dedicated encrypted environment, or on pseudonymised data. A non-disclosure agreement is signed before any work starts.
An audit usually takes one to two weeks. Cleaning depends on volume and complexity; we deliver a first corrected batch from the second week.
Let's talk about your data.
Free first conversation, reply within one business day.
