Not just a point of view.
Something you can test.
Hands-on, browser-based demonstrations of business-first data quality and AI readiness. All examples use illustrative data, not client systems.
Explore how the same dataset can mean different things for quality, business ownership and remediation.
Data quality needs
a business response.
Missing product identifiers
A completeness failure becomes useful when the affected process, responsible owner and resolution path are clear.
Six simple dimensions. Your inputs stay in your browser; this tool does not collect or store answers.
How ready is your data
for AI-enabled decisions?
Developing foundations
Choose your current maturity across six dimensions to see an indicative priority.
Illustrative self-assessment only, not a certification, audit or benchmark against other organisations.
Built to learn.
Built to challenge.
Independent exploration and internal proof-of-concept work—not presented as production client deployments.
Data Quality Codex
Rules, exceptions, waivers and issue ownership in a business-oriented quality experience.
Remediation automation
n8n experiments with structured inputs, workflow decisions and human-in-the-loop exception handling.
Databricks-native quality
Data quality rule execution closer to the lakehouse, with business definitions and monitoring concerns retained.
OpenMetadata exploration
Hands-on discovery of open metadata approaches and platform integration trade-offs.