> ## Documentation Index
> Fetch the complete documentation index at: https://docs.elementary-data.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Start Using Elementary

<Check>
  Here's a link to a checkbox version of this guide that you can download and use to track your progress: [Download as markdown](/cloud/guides/start-using-elementary-checklist.md).
</Check>

With your environment connected (see [Set up Elementary](/cloud/guides/set-up-elementary) for technical onboarding), the next step is to see Elementary in action.

We'll take a few of your key assets and run them through the core reliability steps: setting clear ownership, adding the right tests, configuring alerts, and understanding how issues are detected and resolved. This gives you a practical, hands-on view of how Elementary keeps your most important data reliable.

## Identify Critical Assets

*Goal: Identify business-critical assets in your pipeline and make sure their documentation and ownership are clear.*

* In the [Catalog](/cloud/features/collaboration-and-communication/catalog), identify and mark three [critical assets](/cloud/features/data-governance/critical_assets)
* Add or update [descriptions, tags, and owners](/cloud/features/collaboration-and-communication/catalog) for these assets
* Leverage the [Governance AI agent](/cloud/ai-agents/governance-agent) - Complete missing metadata based on project context and according to your instructions.
* View [end-to-end, column-level lineage](/cloud/features/data-lineage/lineage) for each critical asset

## Setup monitoring on critical assets

*Goal: Ensure critical assets are covered with the right tests so issues are detected before they affect consumers.*

* Open the [Test Coverage](/cloud/features/data-tests/test-coverage-screen) view to understand existing coverage by dimension
* [Add missing tests](/cloud/features/data-tests/cloud-tests-overview) such as freshness, volume, uniqueness, [anomaly detection](/data-tests/how-anomaly-detection-works), [schema changes](/data-tests/schema-tests/schema-changes) or custom logic
* Leverage the [Test Recommendation AI agent](/cloud/ai-agents/test-recommendation-agent) for suggested tests based on patterns and lineage
* For source monitoring: Elementary automatically adds ML-based [freshness](/cloud/features/anomaly-detection/automated-freshness) and [volume](/cloud/features/anomaly-detection/automated-volume) tests to all sources to catch pipeline and ingestion issues early

## Setup Alerts

*Goal: Create a routing process that gets the right alerts to the right people while avoiding alert fatigue.*

* Use [tags, owners, and subscribers](/cloud/features/alerts-and-incidents/owners-and-subscribers) to define who should be notified and who is responsible for action
* Build an internal alert response plan: who fixes the issue, who needs awareness, and what the SLA should be (see the full playbook [here](https://www.elementary-data.com/post/breaking-alert-fatigue-the-enterprise-playbook-for-data-alerts))
* Create [alert rules](/cloud/features/alerts-and-incidents/alert-rules) that translate this plan into routing, using tags, owners, subscribers, and severity

## Triage & Resolution

*Goal: Investigate and resolve issues quickly and confidently.*

* Triage: Review lineage and upstream failures alongside recent commits and dbt run history to understand what changed and what the issue is impacting
* Use the [Triage & Resolution AI agent](/cloud/ai-agents/triage-resolution-agent) to run the investigation for you by analyzing lineage, failures, recent changes, and dependencies, and surfacing root causes
* Use the [Incidents](/cloud/features/alerts-and-incidents/incidents) screen to manage issues collaboratively and keep track of everything that's currently open

## Optimize Performance

*Goal: Meet your SLAs, improve run times, and ensure your pipelines operate efficiently.*

* Identify long-running tests and models using the [Performance pages](/cloud/features/performance-monitoring/performance-monitoring)
* Use the [Optimization AI agent](/cloud/ai-agents/performance-cost-agent) to optimize queries by surfacing inefficiencies, suggesting improved SQL patterns, and identifying opportunities to reduce data scans or simplify logic

## Measure Progress and Data Health

*Goal: Track improvements in reliability and identify where further attention is needed.*

* Use the [Data Health](/cloud/features/collaboration-and-communication/data-health) screen to monitor overall health scores for your assets
* Filter the screen by domain, owners, or critical assets to measure progress at a higher resolution and keep accountability clear

## Enable analysts and business users

*Goal: Make it easy for anyone to discover assets, understand what they represent, see how they're built, and know whether they can be trusted.*

* Use the [Catalog](/cloud/features/collaboration-and-communication/catalog) to explore assets, their definitions, ownership, and current health status
* Review [Lineage](/cloud/features/data-lineage/lineage) to see how the asset is built, what depends on it, and whether any upstream issues or test failures affect it
* Use the [Discovery AI agent](/cloud/ai-agents/catalog-agent) to get clear explanations of the asset, how it's calculated, and any current reliability concerns

## Advanced: Use MCP to Extend Elementary Everywhere

### For Engineers

*Goal: Make development safer and faster by bringing full pipeline context into your coding environment.*

* [Enable Elementary MCP](/cloud/mcp/setup-guide) inside your IDE or coding assistant (Cursor, Claude Code, etc.)
* Add metadata or validations at scale without touching the UI or code manually
* Check lineage, coverage, and asset health while you code so you can spot issues early, understand downstream impact, and prevent problems from reaching production

### For Analysts and Business Users

*Goal: Bring Elementary's context into the tools people already use.*

* [Connect Elementary MCP](/cloud/mcp/setup-guide) to any MCP-enabled client (Claude, ChatGPT, or internal AI agents)
* Allow users to ask about assets, see definitions and ownership, review lineage, and check health or incidents directly through their AI assistant
* (Optional) Connect additional MCPs such as dbt or Atlan so users can navigate across multiple systems from a single conversational interface