A field is filled but the value is wrong
Completeness alone does not make data usable. Dates, codes, documents and addresses can be populated and still fail the business rule.
Data Quality
A data error becomes expensive once a decision has already been built on it
Data quality is not one abstract percentage. The useful question is which errors can change a payment, coverage estimate, report or management conclusion.
When this becomes a problem
Data Quality
Duplicates change the total
The same person, organisation or event appears in several records and distorts coverage or value.
Problems are discovered in the final report
Quality control sits at the end of the process. It needs to move closer to data capture and exchange.
What we work on
What the work covers
I do not start with a catalogue of one hundred checks. We first identify the fields and scenarios where an error changes money, access, identity or reporting.
- Profile data and identify anomalies and unexpected distributions.
- Check completeness, validity, uniqueness, consistency and timeliness.
- Translate business requirements into data quality rules for critical fields and documents.
- Find duplicates, conflicts and identity matching problems across systems.
- Build quality dashboards and a practical issue resolution process.
How I work01 02 03 04
How I work
Critical data
Identify where an error actually changes the outcome.
Profile
Inspect values, formats, missingness, duplicates and distributions.
Rules
Turn business requirements into checks that can be automated.
Control
Make clear who sees the issue, who fixes it and how the result is verified.