Calculate issues by type and source.
MyDataStories / What we solve
See the quality problem, not just a score
A quality dashboard should show exactly what failed, how many records are affected and who needs to resolve them.
Focus on the errors that change business decisions.
Assign ownership and track recurring defects.
We agree measures and definitions with each organisation. Any demonstration charts use illustrative data, not client performance claims.
Discuss your data →Data Quality & Cleansing
Know what can be trusted before reporting
Data-quality work starts with measurement: identify missing values, duplicates, unusual patterns and inconsistent business rules; then monitor improvements.
- Profiling, completeness and validity checks
- Duplicate resolution and standardised definitions
- Quality dashboards and auditable exception logs
Data quality — before and after checks
Completeness and validity improvement
Key / units: Quality score (%); illustrative before/after comparison. Values shown in the chart are synthetic examples, not customer results.
Illustrative example using synthetic data; not a customer result.
Data quality scorecard. Demonstration using illustrative / synthetic values, not a client performance claim. Read the numbers and units shown in the visual; production case studies will include the data period, source, measurement definition and validation method.
Understanding the figures
Charts on this page use illustrative sample data rather than verified customer outcomes.
Review headings, units, timescales and legends before interpreting any measure. We tailor real dashboards to validated client data.