How AI Agents Reduce Manual MDM Stewardship

Video Summary:

Manual data stewardship remains one of the biggest constraints on the scale and speed of Master Data Management.

Data stewards and subject matter experts are frequently asked to investigate duplicate records, resolve conflicting values, classify sensitive data, fill missing attributes, review data quality issues and approve changes. Their business knowledge is essential, but requiring people to perform every task manually creates backlogs that grow faster than most teams can resolve them.

In this video, Tim Ward, CEO and Co-Founder of CluedIn, explains how AI agents can reduce manual MDM stewardship by completing much of the repetitive analysis, investigation and preparation before work reaches a person.

You will learn how agents can help identify potential duplicates, recommend validation rules, classify and enrich records, assess lineage and source trust, explain proposed changes and prioritise the exceptions that genuinely require human judgement.

Tim also explores the governance controls required to use agents safely against production data. These include read-only observation, reviewable recommendations, deterministic actions, approval workflows, audit history, explainability, attribution and rollback.

The objective is not autonomous MDM without accountability. It is a more scalable operating model in which agents perform the high-volume preparation while data professionals become orchestrators, validators and auditors.

The video also covers practical examples from Komatsu and SEGA and explains how organisations can begin with one measurable business domain before expanding Agentic MDM across additional use cases.