# Metadata Activation

## Overview

**Metadata Activation** is Euno's framework for turning passive metadata into automated actions that improve data governance, reduce manual work, and keep your data stack healthy.

Instead of just cataloging metadata, Euno lets you:

* **React automatically** when metadata changes
* **Enforce policies** through automated workflows
* **Keep systems in sync** without manual intervention
* **Get notified** about data governance issues

***

## Why Metadata Activation Matters

Most data catalogs stop at *observing* your data stack. Metadata Activation takes the next step: **doing something about it**.

### The Problem

Without Metadata Activation, your team faces:

❌ **Manual Governance Enforcement**

* Manually checking for undocumented tables
* Chasing down resource owners for descriptions
* Spreadsheets to track PII and critical data

❌ **Sync Drift Between Tools**

* BI models become outdated when dbt changes
* Analysts rebuild logic that already exists in dbt
* Inconsistent metrics across tools

❌ **Reactive Incident Response**

* Discover breaking changes after they happen
* Scramble to find downstream impacts
* No visibility into who's affected

❌ **Lost Productivity**

* Data teams spend time on repetitive tasks
* Analysts wait for central teams to make changes
* Everyone duplicates work

### The Solution

Metadata Activation helps you:

✓ **Automate Governance**

* Alert when new ungoverned resources appear
* Notify owners when resources lack documentation
* Track and propagate PII tags automatically

✓ **Keep Tools in Sync**

* Auto-update BI models when dbt changes
* Push metrics from dbt to Looker automatically
* Ensure everyone uses the same definitions

✓ **Prevent Issues Proactively**

* Get notified before making breaking changes
* Understand blast radius of proposed changes
* Alert stakeholders about upcoming impacts

✓ **Scale Data Operations**

* Reduce manual coordination overhead
* Enable self-service with guardrails
* Free data teams for higher-value work

***

## How It Works

Metadata Activation consists of three core capabilities:

### 1. **Workflows** 🔔

Define triggers based on your metadata and get notified when conditions are met.

**How it works:**

1. Write a query (using EQL) that describes what you want to monitor
2. Set a condition (e.g., "when query returns > 0 results")
3. Choose notification channels (Slack, Email)
4. Workflows run daily and notify you when triggered

**Example Use Cases:**

| Scenario                      | Workflow                                                                            |
| ----------------------------- | ----------------------------------------------------------------------------------- |
| **Governance Monitoring**     | "Notify #data-governance when new tables appear without a `domain` tag"             |
| **Documentation Enforcement** | "Alert #analytics when dbt models are created without descriptions"                 |
| **Usage Monitoring**          | "Notify dashboard owners when their dashboards haven't been viewed in 90 days"      |
| **PII Tracking**              | "Alert #security when tables with PII columns are queried by non-approved users"    |
| **Cost Management**           | "Notify #data-platform when Snowflake warehouse costs increase >20% week-over-week" |
| **Change Detection**          | "Alert #bi-team when new columns are added to certified Looker explores"            |

[Learn more about Workflows →](/using-euno/metadata-activation/workflows.md)

### 2. **Data Model Sync** 🔄

Keep your BI tools automatically in sync with your transformation logic.

**How it works:**

1. Euno detects changes in your dbt manifest
2. Automatically generates updated LookML (or other BI format)
3. Creates a pull request in your BI repository
4. Your team reviews and merges the changes

**Supported Platforms:**

* ✓ Looker (via LookML sync from dbt)
* 🔜 Tableau (coming soon)
* 🔜 Power BI (coming soon)

**Example Benefits:**

| Without Sync                                                                                                              | With Sync                                                                                                           |
| ------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------- |
| Analytics engineer adds metric in dbt → BI developer manually recreates it in Looker → 3 days later, possibly with errors | Analytics engineer adds metric in dbt → Euno auto-generates LookML → PR created automatically → Merge in 10 minutes |
| Column renamed in dbt → BI dashboards break → Scramble to fix → Users see errors                                          | Column renamed in dbt → Euno updates LookML → PR shows exactly what changed → Controlled rollout                    |
| Metrics calculated differently in dbt vs. BI → Inconsistent reports → Trust erosion                                       | Single source of truth in dbt → Always synced to BI → Consistent definitions everywhere                             |

[Learn more about Data Model Sync →](/using-euno/metadata-activation/data-application-sync.md)

### 3. **Impact Analysis** 📊

Understand the ripple effects of changes before you make them.

**How it works:**

1. Select a resource (table, column, dashboard, etc.)
2. Euno analyzes all downstream dependencies
3. Get a comprehensive report of what would be affected
4. Make informed decisions about changes

**Example Questions Answered:**

* "If I delete this table, what dashboards will break?"
* "If I change this column name, what needs to be updated?"
* "If I deprecate this dbt model, what's the blast radius?"
* "Who should I notify before making this change?"

[Learn more about Impact Analysis →](/using-euno/impact-analysis.md)

***

## Getting Started with Metadata Activation

### Step 1: Connect Your Sources

Metadata Activation works best when Euno has visibility into your entire stack:

* Connect your transformation tool (dbt)
* Connect your data warehouse (Snowflake, BigQuery)
* Connect your BI tools (Tableau, Looker)

More connections = more powerful automation.

### Step 2: Start with Workflows

Workflows are the easiest entry point. Start with these simple use cases:

**Beginner Workflow:**

```
Alert #data-team when new Snowflake tables are created
```

**Intermediate Workflow:**

```
Notify owners when dbt models haven't been updated in 90 days
```

**Advanced Workflow:**

```
Alert #governance when tables containing PII are accessed by 
non-approved roles, and the table isn't tagged as audited
```

[Create Your First Workflow →](/using-euno/metadata-activation/workflows.md)

### Step 3: Enable Data Model Sync (if applicable)

If you use dbt + Looker:

1. Configure your dbt and Looker sources
2. Set up the Data Model Sync automation
3. Run your first sync to see the generated LookML
4. Review and merge the PR

[Set Up Data Model Sync →](/using-euno/metadata-activation/data-application-sync.md)

### Step 4: Use Impact Analysis Before Changes

Before deprecating a table or changing logic:

1. Open the resource in Euno
2. Run Impact Analysis
3. Review all downstream dependencies
4. Notify affected stakeholders
5. Proceed with confidence

[Learn Impact Analysis →](/using-euno/impact-analysis.md)

***

## Real-World Examples

### Example 1: Automated Governance at Scale

**Company:** Mid-size B2B SaaS company\
**Challenge:** 500+ Snowflake tables, 40% undocumented\
**Solution:** Workflow that alerts table owners weekly about missing documentation

**Workflow Configuration:**

```
Query: Find tables without descriptions
Condition: Results > 0
Action: Send Slack message to table owner
Schedule: Every Monday at 9 AM
```

**Results:**

* Documentation rate increased from 60% → 92% in 3 months
* Reduced "what does this table do?" Slack questions by 75%
* New tables are documented within 1 week

### Example 2: dbt-to-Looker Automation

**Company:** E-commerce company\
**Challenge:** BI team spending 20 hours/week manually syncing dbt changes to Looker\
**Solution:** Data Model Sync from dbt to LookML

**Before:**

1. Analytics engineer updates dbt metric
2. Creates Jira ticket for BI team
3. BI developer recreates metric in LookML
4. Back-and-forth to verify calculation
5. 3-5 days elapsed time

**After:**

1. Analytics engineer updates dbt metric
2. Euno auto-generates LookML
3. PR created automatically with exact changes
4. BI team reviews and merges
5. 15 minutes elapsed time

**Results:**

* 90% reduction in sync time
* Zero calculation inconsistencies
* BI team can focus on complex visualizations

### Example 3: Proactive Change Management

**Company:** Financial services firm\
**Challenge:** Frequent unintended dashboard breakages from upstream changes\
**Solution:** Impact Analysis before every change + automated stakeholder notifications

**Process:**

1. Developer wants to rename a column in dbt
2. Runs Impact Analysis in Euno
3. Sees 15 downstream Tableau dashboards affected
4. Workflow automatically notifies dashboard owners
5. Coordinates change during maintenance window

**Results:**

* Zero unintended dashboard breakages in 6 months
* 95% reduction in "why is my dashboard broken?" incidents
* Improved trust between data and analytics teams

***

## Best Practices

### For Workflows

✓ **Start Small**

* Begin with simple, low-risk workflows
* Add complexity as you learn

✓ **Be Specific in Queries**

* Target exact resource types and conditions
* Avoid overly broad notifications

✓ **Choose the Right Cadence**

* Daily for governance checks
* Immediate for critical issues
* Weekly for cleanup reminders

✓ **Optimize Notification Channels**

* Use Slack for team alerts
* Use Email for individual notifications
* Create dedicated channels for workflow notifications

### For Data Model Sync

✓ **Start with a Subset**

* Sync a few models first to test
* Expand gradually as confidence grows

✓ **Use Staging Branches**

* Don't sync directly to production
* Review PRs before merging

✓ **Document Naming Conventions**

* Handle collisions consistently
* Use meta keys for custom naming

✓ **Monitor Sync Health**

* Review sync logs regularly
* Address failures quickly

### For Impact Analysis

✓ **Run Before Every Breaking Change**

* Column renames
* Table drops
* Schema changes

✓ **Document Your Findings**

* Export impact reports
* Share with stakeholders
* Track in project management tools

✓ **Communicate Early**

* Notify affected teams before changes
* Provide timeline and migration plan
* Offer support during transition

***

## Success Metrics

How to measure the impact of Metadata Activation:

### Governance Metrics

* % of resources with documentation
* % of tables with appropriate tags
* Time to document new resources

### Efficiency Metrics

* Time spent on manual sync tasks
* Number of governance-related Slack messages
* Hours saved per week on repetitive work

### Quality Metrics

* Number of dashboard breaking incidents
* Metric consistency across tools
* Data trust survey scores

### Adoption Metrics

* Number of active workflows
* % of changes using Impact Analysis
* Team satisfaction scores

***

## Limitations & Considerations

### Workflows

* Execute maximum once per day
* 60-second query timeout
* Maximum 100,000 results per query
* Requires source integrations to be active

### Data Model Sync

* Currently supports dbt → Looker only
* Requires Git access for PR creation
* Some complex dbt patterns may not sync
* Requires review before production deployment

### Impact Analysis

* Depends on connected sources
* Limited to relationships Euno can observe
* May not capture external dependencies
* Requires up-to-date metadata

***

## Next Steps

1. **Create Your First Workflow**
   * Start with a simple governance use case
   * [Workflows Guide →](/using-euno/metadata-activation/workflows.md)
2. **Explore Data Model Sync**
   * If you use dbt + Looker
   * [Data Model Sync Setup →](/using-euno/metadata-activation/data-application-sync.md)
3. **Try Impact Analysis**
   * Before your next schema change
   * [Impact Analysis Guide →](/using-euno/impact-analysis.md)
4. **Join the Community**
   * Share your automation ideas
   * Learn from other Euno users

***

## Related Documentation

* [Workflows](/using-euno/metadata-activation/workflows.md) - Automated notifications and alerts
* [Data Model Sync](/using-euno/metadata-activation/data-application-sync.md) - Keep BI tools in sync
* [Impact Analysis](/using-euno/impact-analysis.md) - Understand change impact
* [Using Euno](/using-euno.md) - Day-to-day features
* [Source Integrations](/sources.md) - Connect your data tools

***

## Questions?

* **In-App Support:** Click the chat icon
* **Email:** <support@euno.ai>
* **Documentation:** [Full Metadata Activation Docs](/using-euno/metadata-activation.md)

Ready to activate your metadata? [Start with Workflows →](/using-euno/metadata-activation/workflows.md)


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.euno.ai/using-euno/metadata-activation.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.