Data & Analytics

Automate your data pipelines and reporting workflows

Overview

Draft & Goal helps data teams automate reporting, synchronize data between systems, and build data pipelines without code.

Common data workflows

Automated reporting

Generate reports on a schedule:

• Pull data from multiple sources
• Transform and aggregate
• Generate insights with AI
• Deliver via email or Slack

Nodes used: Google Analytics, BigQuery, LLM, Google Slides, Email

Data synchronization

Keep systems in sync:

• Export from source system
• Transform data format
• Load into destination
• Verify and log results

Nodes used: Google Sheets, Loop, BigQuery Writer

Data enrichment

Add value to raw data:

• Fetch records from database
• Enrich with external data
• Apply AI analysis
• Write back enriched data

Nodes used: BigQuery, Web Scraper, LLM, BigQuery Writer

Monitoring and alerts

Track metrics and alert on anomalies:

• Query metrics regularly
• Compare to thresholds
• Detect anomalies
• Send alerts

Nodes used: Google Analytics, Conditional, LLM, Email Sender

Available data nodes

Google Suite

Node	Description
Google Sheets	Read/write spreadsheets
Google BigQuery Reader	Query data warehouse
Google BigQuery Writer	Insert data to BigQuery
Google Analytics	Web analytics data
Google Slides	Create presentations

Transformation

Node	Description
JSON Path Extractor	Extract from JSON
Merge	Populate templates with data
Find & Replace	Transform text
Loop	Process arrays

External APIs

Node	Description
API Connector	Pre-built API integrations

Example: Weekly KPI dashboard

Automatically generate a weekly KPI dashboard:

graph LR
    A[Google Analytics] --> D[Merge & Transform]
    B[BigQuery Sales] --> D
    C[HubSpot CRM] --> D
    D --> E[LLM Insights]
    E --> F[Google Slides]
    F --> G[Email to Team]

What it does

1. Fetch data from Analytics, BigQuery, and HubSpot
2. Merge into a unified dataset
3. Analyze with AI to generate insights
4. Create slides with charts and commentary
5. Email to stakeholders automatically

Time saved

Task	Manual	Automated
Data collection	2 hours	0
Analysis	3 hours	5 minutes
Report creation	2 hours	0
Distribution	30 min	0
Total	7.5 hours	5 minutes

Data pipeline patterns

ETL (Extract, Transform, Load)

Source → Extract → Transform → Load → Destination

Draft & Goal handles all three stages.

Event-driven pipelines

Webhook → Process → Update → Notify

React to events in real-time.

Scheduled batch processing

Schedule → Fetch batch → Process → Output

Run heavy operations during off-hours.

Best practices

Error handling

Always handle data pipeline failures:

• Log all operations
• Alert on failures
• Implement retries
• Have fallback paths

Data validation

Validate data quality:

• Check for nulls
• Verify data types
• Validate ranges
• Flag anomalies

Incremental processing

For large datasets:

• Track last processed timestamp
• Only fetch new records
• Maintain state between runs

Getting started

Next steps