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

  1. 01

    Quality audit

    Automatic profiling, quality score per table and field, anomaly mapping.

  2. 02

    De-duplication & matching

    Fuzzy matching (names, addresses, phones), record merging, history preserved.

  3. 03

    Standardisation & enrichment

    Harmonised formats, geographic reference data, geocoding, open-data enrichment.

  4. 04

    Master data management

    A single version of the truth for customers, products, sites or beneficiaries.

Case studies

Context, solution, result: two representative assignments.

Frequently asked questions

Let's talk about your data.

Free first conversation, reply within one business day.

Contact us

Newsletter