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 ↗Help data stewards investigate candidates without making destructive automatic merges.
Which identifiers are reliable? What would be lost in a merge? How can an incorrect decision be reversed?
Never merge solely on a name similarity score. Protect personal information and preserve distinct legal entities.
We use these answers to agree on integration boundaries, responsible reviewers, access controls and a definition of done. The proposal identifies dependencies and what is included before implementation begins.
Pilot a labeled dataset with both true duplicates and similar but distinct records.
Measure false matches, missed duplicates, lost relationships and review effort.
Execute accepted merges through the system's supported process and retain an audit trail.
Document the owner, support path and recovery procedure. Define how the workflow behaves when information is incomplete, access fails or a reviewer declines the proposed result. Agree on what needs fresh validation after a model or integration change.
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 ↗Prepare a review queue for data-quality issues with clear ownership, source evidence and downstream context.
Explore this solution ↗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.