Your team owns the build.
Learn through courses, practical training and workshops. Get feedback as you apply the lessons to your own project.
Explore courses and workshops ↗Prepare evidence-based cleanup suggestions for customer records without silently overwriting valuable context.
Duplicate organizations, inconsistent names and stale fields make automation unreliable. AI can suggest a correction but needs a review boundary for identity and ownership.
Give operations a prioritized queue of explainable record-quality issues.
A data owner selects an approved record set for review.
Identify possible duplicates and inconsistencies with supporting evidence, keeping uncertain matches separate.
A data steward confirms identity and authorizes corrections or merges. Customer history must not be discarded based on similarity alone.
Confirm field ownership and downstream automation effects. Avoid overwriting a recent authoritative update with older evidence.
Measure false duplicate matches, corrected fields, review time and downstream issues avoided.
Before expanding the workflow, compare the result with the current process. Include review effort and exceptions in that comparison so the improvement reflects the team's actual experience.
Read the implementation guide ↗The workflow stays centered on your business. The engagement determines who learns, implements and operates it.
Learn through courses, practical training and workshops. Get feedback as you apply the lessons to your own project.
Explore courses and workshops ↗Get everything in DIY, three months of workshops, weekly team sessions and direct implementation coaching.
Explore guided implementation ↗Include all the learning and guidance, with ownership of agreed operations and major implementation decisions.
Explore managed delivery ↗Looking for a course, help with a difficult problem, or someone to build a solution? Tell us where you are and what you'd like to do next.