Data foundations
Data Cleaning and Standards
We correct inconsistent, duplicated and incomplete data so that organisations can trust their reporting.
Discuss this serviceWhen this helps
Different files. Different answers. One decision to make.
Two teams submit spreadsheets with different names, duplicate entries and totals that do not reconcile.
- Duplicate records
- Missing information
- Inconsistent definitions
| Record | Date | Province | Review |
|---|---|---|---|
| C101 | 2026-10-01 | KZN | Checked |
| C102 | 2026-10-02 | Gauteng | Checked |
| C102 | 2026-10-02 | Gauteng | Duplicate |
| C103 | 2026-10-03 | Missing | Review |
The scope
The work, made clear.
Identifying duplicate, inconsistent and incomplete records.
Defining shared data standards across departments and partners.
Repairing historical data so trend analysis is trustworthy.
Documenting rules so cleaned data stays clean.
Our approach
Agree the definitions, identify exceptions, resolve duplicates and document the rules used to clean the records.
The result to aim for
A reconciled dataset and shared standards give the reporting process a clearer starting point.
A better basis for action
What this makes possible.
- Departments stop reporting different numbers for the same metric.
- Leadership can trust the figures behind decisions.
Scope and intended outcomes are agreed around your data, systems and reporting requirements.
See the work in context
How the pieces come together.
Explore the data foundation, analysis and reporting layers in our programme reporting case study.