---
title: Data Steward course
url: https://documentation.cluedin.net/learning-paths/data-steward
content_type: landing
section: Learning paths
---

# Data Steward course

The Data Steward course is for people responsible for the operational quality and trustworthiness of data in CluedIn. The course teaches you to investigate a reported problem, establish evidence, choose the right remediation path, verify the result, and escalate structural problems with enough context for a Data Architect to act.

The course does not duplicate product instructions. Every practical module has a **Read** section that points to the relevant product documentation. Read those pages first, then complete the scenario and exercise in CluedIn. The first page is an orientation to the learning path and explains how the modules work.

## Course outcomes

By the end of the course, you should be able to:

- explain how source data becomes a golden record
- isolate a data-quality problem with search and filters
- distinguish source, mapping, processing, identity, and record-level issues
- use validations, clean projects, deduplication, glossary, tags, and AI-assisted remediation appropriately
- verify that remediation worked rather than assuming it did
- recognize downstream impact and produce useful handoffs for Data Architects

## Before you begin

Use a non-production environment and choose one training business domain or dataset that you can use throughout the course. Keep a simple issue log containing the problem, affected population, evidence, action taken, verification result, and any escalation.

## Module sequence

1. [About this learning path](https://documentation.cluedin.net/learning-paths/data-steward/course-purpose-and-setup.md)
2. [First tour of the instance and the golden record mindset](https://documentation.cluedin.net/learning-paths/data-steward/instance-tour-and-golden-record-mindset.md)
3. [How ingestion, mapping, and processing affect stewardship](https://documentation.cluedin.net/learning-paths/data-steward/ingestion-mapping-and-processing-for-stewards.md)
4. [Find and inspect records with search, filters, and saved searches](https://documentation.cluedin.net/learning-paths/data-steward/search-filters-and-record-inspection.md)
5. [Review source quality with validations and mapping checkpoints](https://documentation.cluedin.net/learning-paths/data-steward/validations-and-mapping-checkpoints.md)
6. [Clean recurring quality issues with clean projects](https://documentation.cluedin.net/learning-paths/data-steward/clean-projects-and-remediation.md)
7. [Resolve duplicates and understand merge decisions](https://documentation.cluedin.net/learning-paths/data-steward/deduplication-and-conflict-resolution.md)
8. [Use glossary, tags, and governance views to organize work](https://documentation.cluedin.net/learning-paths/data-steward/glossary-tags-and-governance.md)
9. [Use AI agents safely for stewardship work](https://documentation.cluedin.net/learning-paths/data-steward/ai-assisted-stewardship.md)
10. [Understand streams, downstream impact, and architect handoffs](https://documentation.cluedin.net/learning-paths/data-steward/streams-downstream-impact-and-handoffs.md)
11. [Capstone operating loop and readiness checklist](https://documentation.cluedin.net/learning-paths/data-steward/capstone-operating-loop.md)

## In this section

- [About this learning path](https://documentation.cluedin.net/learning-paths/data-steward/course-purpose-and-setup.md) (tutorial): This learning path is for Data Stewards who are responsible for the operational quality and trustworthiness of data in CluedIn.
- [First tour of the instance and the golden record mindset](https://documentation.cluedin.net/learning-paths/data-steward/instance-tour-and-golden-record-mindset.md) (tutorial): Trace a visible golden-record value back to the evidence that contributed to it.
- [How ingestion, mapping, and processing affect stewardship](https://documentation.cluedin.net/learning-paths/data-steward/ingestion-mapping-and-processing-for-stewards.md) (tutorial): Recognize when a quality problem originates in source data, mapping, identifiers, or processing rather than in the golden record itself.
- [Find and inspect records with search, filters, and saved searches](https://documentation.cluedin.net/learning-paths/data-steward/search-filters-and-record-inspection.md) (tutorial): Isolate a problem population, save the investigation, and inspect representative records before deciding how to remediate them.
- [Review source quality with validations and mapping checkpoints](https://documentation.cluedin.net/learning-paths/data-steward/validations-and-mapping-checkpoints.md) (tutorial): Use validation evidence and mapping review to distinguish bad values from structural mapping problems.
- [Clean recurring quality issues with clean projects](https://documentation.cluedin.net/learning-paths/data-steward/clean-projects-and-remediation.md) (tutorial): Correct a repeatable data-quality problem with a focused clean project and verify the outcome.
- [Resolve duplicates and understand merge decisions](https://documentation.cluedin.net/learning-paths/data-steward/deduplication-and-conflict-resolution.md) (tutorial): Review duplicate candidates conservatively, justify a merge decision, and recognize when duplicate symptoms indicate an identity-design problem.
- [Use glossary, tags, and governance views to organize work](https://documentation.cluedin.net/learning-paths/data-steward/glossary-tags-and-governance.md) (tutorial): Use named record populations and quality signals to turn one-off investigations into repeatable stewardship work.
- [Use AI agents safely for stewardship work](https://documentation.cluedin.net/learning-paths/data-steward/ai-assisted-stewardship.md) (tutorial): Scope, test, review, and verify AI-assisted remediation without treating AI output as automatically correct.
- [Understand streams, downstream impact, and architect handoffs](https://documentation.cluedin.net/learning-paths/data-steward/streams-downstream-impact-and-handoffs.md) (tutorial): Explain how stewardship changes can affect downstream consumers and produce an actionable handoff for structural follow-up.
- [Capstone operating loop and readiness checklist](https://documentation.cluedin.net/learning-paths/data-steward/capstone-operating-loop.md) (tutorial): Resolve one realistic data-quality scenario end to end using evidence, deliberate tool selection, verification, and escalation where necessary.

Up: [Learning paths](https://documentation.cluedin.net/learning-paths.md)