Process
From first call to clean data.
Every engagement starts small and concrete. You know what is wrong, and what fixing it costs, before you commit to anything larger.
Stages
- 01
Discovery call
What data you have, which decisions depend on it, and what’s going wrong. We’ll tell you whether we think we can help.
- 02
Data Quality Audit
We examine a dataset or pipeline end to end and write up what’s wrong, how much it matters and what fixing it would take.
- 03
Curation
Agents clean, reconcile and restructure the data; changes are reviewed and logged. You see progress at regular checkpoints.
- 04
Monitoring
New data is checked as it arrives, so problems are caught before they reach your reports.
What you receive
The data, and the evidence behind it.
- Curated data
- In your format (CSV, Parquet, JSON or tables in your warehouse).
- Change log
- Each change: what it was, why it was made, and who approved it.
- Data dictionary
- What each field means, its units and its valid range.
- Quality report
- Issues found, how they were resolved and what remains open.
- Reusable rules
- The checks and transformations, so the work can be repeated on new data.
Sample deliverable
Data Quality Report
An illustrative report on a fictional dataset, in the same format as the one you receive at the end of an audit.
Engagement models
Data Quality Audit
A fixed-scope, fixed-fee review of one dataset or pipeline. You get a written report: what is wrong, how much it matters, and what fixing it would take.
Curation project
We clean, reconcile and restructure the data, with changes reviewed and logged. You keep the curated data and the rules that produced it.
Ongoing monitoring
New data is checked as it arrives. Issues are flagged or fixed before they reach your reports, and you get a monthly quality summary.