# Source: https://docs.hypermode.com/agents/30-days-of-agents/day-14.md

# Day 14: Data & Analytics Intelligence - Neo4j & MongoDB

> Build data analysis agents that understand graph relationships in Neo4j and document structures in MongoDB for complex business intelligence.

<Card title="Day 14 challenge" icon="database">
  **Goal**: create data intelligence agents for graph and document databases

  **Theme**: domain specialization week - data & analytics

  **Time investment**: \~20 minutes
</Card>

Welcome to Day 14! Today you'll build **data intelligence agents** that work
with modern databases—Neo4j for graph relationships and MongoDB for document
structures. These agents understand complex data patterns, not just queries.

## What you'll accomplish today

* Build agents that understand graph relationships and document structures
* Connect Neo4j for relationship analysis and pattern detection
* Integrate MongoDB for flexible document querying
* Create insights from complex, interconnected data

<Warning>
  You'll need access to Neo4j and/or MongoDB instances to complete today's
  hands-on exercises. The concepts apply to any modern database.
</Warning>

## Step 1: Understanding modern data patterns

Data intelligence agents must grasp:

### Graph intelligence Neo4j

* **Relationship mapping**: How entities connect and influence each other
* **Pattern detection**: Finding hidden connections in networks
* **Path analysis**: Shortest paths, influence chains, impact radius
* **Community detection**: Clusters, groups, and anomalies

### Document intelligence MongoDB

* **Schema flexibility**: Varying document structures and embedded data
* **Aggregation patterns**: Complex queries across nested documents
* **Time-series analysis**: Historical patterns and trends
* **X insights**: Location-based patterns and relationships

<Tip>
  **Beyond SQL thinking** modern data agents understand relationships and
  document structures, not just tables and rows.
</Tip>

## Step 2: Create specialized data agents

### Graph intelligence agent (Neo4j)

```text
I want to create a customer intelligence agent using our Neo4j graph database.

The agent should:
- Analyze customer relationship networks and referral chains
- Identify influential customers based on connection patterns
- Find communities and clusters in our user base
- Detect unusual relationship patterns that might indicate fraud
- Recommend connections that could drive growth

Think like a network analyst who understands social dynamics and graph theory.
```

### Document analytics agent (MongoDB)

```text
I want to create a product analytics agent using our MongoDB database.

The agent should:
- Analyze product usage patterns across different user segments
- Track feature adoption through nested event documents
- Identify user journeys from unstructured activity logs
- Find patterns in customer feedback and support tickets
- Generate insights from varying document schemas

Think like a data scientist working with semi-structured data.
```

## Step 3: Connect and explore your databases

### Neo4j connection capabilities

Your agent can:

* **Traverse relationships**: Follow connections multiple degrees deep
* **Find patterns**: Match complex relationship structures
* **Calculate centrality**: Identify important nodes in networks
* **Detect communities**: Find natural groupings and clusters

### MongoDB connection capabilities

Your agent can:

* **Query nested documents**: Access deeply embedded data
* **Aggregate flexibly**: Group, filter, and transform documents
* **Handle variety**: Work with different document structures
* **Process time-series**: Analyze temporal patterns

## Step 4: Implement data intelligence workflows

### Graph analysis example (Neo4j)

```text
Who are our most influential customers based on referral networks?
How can we leverage these relationships for growth?
```

Your agent analyzes:

* Referral chains and their success rates
* Network effects from key customers
* Community structures and growth patterns
* Opportunities for connection strategies

### Document analysis example (MongoDB)

```text
What user behavior patterns predict successful feature adoption?
Which segments are struggling with our new features?
```

Your agent examines:

* Feature usage sequences in activity logs
* Correlation between user properties and adoption
* Time-to-value patterns across segments
* Drop-off points in user journeys

## Step 5: Create advanced data workflows

**Network health monitoring** (Neo4j):

```text
Weekly, analyze our customer network for:
- Growing communities that might need dedicated support
- Isolated users who could benefit from connections
- Referral chains that have gone cold
- Emerging influencers based on connection growth

Provide actionable recommendations for community management.
```

**Behavioral intelligence** (MongoDB):

```text
Daily, process user activity documents to identify:
- Unusual usage patterns indicating potential issues
- Feature combinations that drive engagement
- User segments with declining activity
- Success patterns we should replicate

Focus on actionable insights, not just statistics.
```

## What you've accomplished

In 20 minutes, you've built sophisticated data intelligence agents:

**Graph understanding** Agents that see relationships, not just data points

**Document flexibility** working with varied, nested, and complex structures

**Pattern recognition** finding insights in connected and unstructured data

**Actionable intelligence** recommendations based on data patterns, not just
queries

## The power of modern data agents

Data agents that understand modern databases can:

* **Reveal hidden connections** in relationship networks
* **Find patterns in chaos** of unstructured documents
* **Predict behaviors** from historical patterns
* **Enable data democracy** without query language expertise

<Card title="Tomorrow - Day 15" icon="arrow-right" href="/agents/30-days-of-agents/day-15">
  Customer relationship management with Attio and Google Sheets. Build agents
  for sales intelligence and spreadsheet automation.
</Card>

## Pro tip for today

Challenge your data agents:

```text
What non-obvious patterns in our data could give us a competitive advantage?
Show me connections or trends I wouldn't think to look for.
```

This pushes agents beyond basic queries to true intelligence.

***

**Time to complete**: \~20 minutes

**Skills learned** graph database intelligence, document database analytics,
pattern recognition, modern data workflows

**Next** Day 15 - CRM and spreadsheet intelligence with Attio and Google Sheets

<Tip>
  **Remember** the best data agents don't just run queries—they understand the
  meaning behind the data and find patterns humans might miss.
</Tip>