Data Quality Audits
Data Quality Audits assess the accuracy, completeness, and consistency of your data across systems. We identify issues, fix problems, and establish processes for ongoing data quality.
Our process.
Data Source Inventory & Assessment
We inventory all your data sources, assess data quality dimensions — accuracy, completeness, consistency, timeliness, validity.
Quality Analysis & Gap Identification
We analyse data quality across sources, identify gaps, duplicates, inconsistencies, and issues affecting reporting and decision-making.
Remediation Plan & Implementation
We develop a prioritised remediation plan, implement fixes, data cleaning, validation rules, and automated quality checks.
Ongoing Monitoring & Governance
We set up data quality monitoring dashboards, establish governance processes, and provide recommendations for maintaining data quality.
What you get.
Questions we get asked.
How do you measure data quality?
We assess five dimensions: accuracy (is it correct?), completeness (is it all there?), consistency (is it the same across sources?), timeliness (is it current?), and validity (does it meet defined rules?).
How often should data quality be audited?
We recommend an initial comprehensive audit, then quarterly spot checks and continuous monitoring through automated quality checks. High-volume or critical data may need more frequent attention.
What causes poor data quality?
Common causes include manual data entry errors, system migration issues, lack of validation rules, inconsistent formats across sources, API changes, and data decay (e.g., outdated contact information).
Ready to start growing?
Free audit. No commitment. First results within 60 days or we fix it.