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Data Mining: Your Workflow Starting Point

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What is a Data Mine?

• Data Source: Your CloudLink-connected Snowflake tables
• Matching Criteria: The business rules that define what to look for
• State Management: Smart tracking of when conditions are met or no longer met
• Automation Triggers: The workflows that execute when events occur

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Understanding State Management: The ON/OFF System

Record State: OFF → ON  = Automation fires with "Data Mine Triggered" event
Record State: ON → ON   = No action (stays quiet)
Record State: ON → OFF  = Automation fires with "Data Mine Cleared" event
Record State: OFF → OFF = No action (stays quiet)

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Example 1: High-Value Claims Processing

• Source: Claims CloudLink table
• Criteria: claim_amount > 10000 AND status = 'open'
• Schedule: Every 15 minutes

1. New High-Value Claim (OFF → ON):
   • Triggers automation: “High-Value Claim Detected”
   • Actions: Assign to senior adjuster, notify manager, start approval process
   • AI Agent: Review claim details and flag potential issues
2. Claim Stays High-Value (ON → ON):
   • No additional notifications (avoids spam)
   • Continues monitoring
3. Claim Resolved or Reduced (ON → OFF):
   • Triggers automation: “High-Value Claim Cleared”
   • Actions: Notify team, update reports, archive documentation

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Example 2: Inventory Threshold Monitoring

• Source: Inventory CloudLink table
• Criteria: stock_level < reorder_threshold AND status = 'active'
• Schedule: Hourly

1. Low Stock Detected (OFF → ON):
   • Triggers automation: “Reorder Required”
   • Actions: Create purchase order, notify procurement team
   • AI Agent: Analyze historical usage patterns and recommend optimal reorder quantities
2. Stock Remains Low (ON → ON):
   • No repeat notifications
   • Continues monitoring for restocking
3. Stock Replenished (ON → OFF):
   • Triggers automation: “Stock Level Restored”
   • Actions: Update forecasting models, notify sales team of availability

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Example 3: SLA Violation Monitoring

• Source: Support tickets CloudLink table
• Criteria: created_date < NOW() - INTERVAL '24 hours' AND status = 'open' AND first_response_date IS NULL
• Schedule: Every 30 minutes

1. SLA Violation Detected (OFF → ON):
   • Triggers automation: “SLA Breach Alert”
   • Actions: Escalate to manager, assign to senior agent, send customer notification
   • AI Agent: Analyze ticket complexity and suggest resolution strategies
2. Violation Continues (ON → ON):
   • No additional escalation alerts
   • Continues monitoring
3. Response Provided (ON → OFF):
   • Triggers automation: “SLA Restored”
   • Actions: Update metrics, notify team of resolution

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AI Agent Integration: The Next Level

• Review historical patterns
• Understand business rules
• Consider multiple data points simultaneously

• Determine appropriate actions based on data patterns
• Escalate or resolve issues automatically
• Adapt responses based on context

• Track successful outcomes
• Adjust recommendations over time
• Identify new patterns worth monitoring

1. Data Mine detects: High-value claim submitted
2. AI Agent analyzes: Claim history, customer profile, similar claims
3. Agent decides: Auto-approve, request additional documentation, or flag for human review
4. Agent executes: Appropriate workflow based on analysis
5. Agent learns: Tracks outcomes to improve future decisions

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Types of Data Mining

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1. Logic-Based Rules Mining

Criteria: order_total > 5000
Actions: Route to approval workflow, notify finance team

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2. ML Anomaly Detection

AI Model: Learns normal spending patterns
Detection: Flags expenses that deviate significantly from learned norms
Actions: Flag for review, request additional documentation

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3. Statistical Anomaly Detection

Statistical Method: Z-score analysis
Threshold: 2 standard deviations above mean
Actions: Alert operations team, investigate bottlenecks

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Setting Up Data Mining: Complete Guide

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Step 1: Plan Your Automation First

• What action should occur when the condition is first met?
• What should happen when the condition is no longer met?
• Who needs to be notified?
• What data should be passed to the automation?
• Should an AI agent be involved in the decision-making?

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Step 2: Select Your Data Source

• Ensure data is current and reliable
• Verify you have appropriate access permissions
• Consider data refresh frequency
• Check for any data quality issues

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Step 3: Define Identifying Columns

• Use stable, unique identifiers (ID, UUID, etc.)
• Include business-relevant fields (customer_id, order_number)
• Avoid frequently changing fields
• Consider using composite keys for complex scenarios

Identifying Columns:
- ticket_id (primary identifier)
- customer_id (for customer context)
- created_date (for time-based tracking)

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Step 4: Build Your Matching Criteria

WHERE priority = 'High'
WHERE amount > 1000
WHERE status IN ('pending', 'review')

WHERE (
    (priority = 'High' AND amount > 500) 
    OR 
    (priority = 'Critical' AND amount > 100)
)
AND status = 'active'

WHERE created_date > NOW() - INTERVAL '7 days'
WHERE last_updated < NOW() - INTERVAL '24 hours'

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Step 5: Configure Schedule and Limits

• Real-time needs: Every 15-30 minutes
• Business hours monitoring: Hourly during business hours
• Daily summaries: Once daily
• Weekly reports: Once weekly

• Keep matching records under 20,000 for optimal performance
• Use specific criteria to reduce dataset size
• Consider peak usage times when scheduling
• Monitor execution times and adjust as needed

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Step 6: Test and Validate

1. Use “VIEW MATCHING DATA” to verify results
2. Test with a small dataset first
3. Verify automation triggers work correctly
4. Check that state transitions behave as expected
5. Validate AI agent responses (if applicable)

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Advanced Data Mining Patterns

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Pattern 1: Cascade Monitoring

Data Mine 1: New orders > $1000 → Trigger credit check
Data Mine 2: Credit check completed → Trigger fulfillment
Data Mine 3: Fulfillment started → Trigger shipping notifications

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Pattern 2: Threshold Escalation

Data Mine 1: Response time > 2 hours → Notify team lead
Data Mine 2: Response time > 4 hours → Escalate to manager
Data Mine 3: Response time > 8 hours → Alert executive team

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Pattern 3: Trend Analysis

Data Mine 1: Daily sales < target → Alert sales team
Data Mine 2: Weekly sales trend negative → Trigger strategy review
Data Mine 3: Monthly performance decline → Executive intervention

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Best Practices for Business Value

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1. Start with High-Impact Use Cases

• Focus on processes that save the most money
• Target repetitive manual tasks
• Address compliance requirements
• Improve customer experience

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2. Design for Scale

• Plan for data growth
• Consider multiple time zones
• Build in error handling
• Monitor performance metrics

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3. Optimize for Business Users

• Use clear, business-friendly naming
• Document business rules and assumptions
• Provide training for key stakeholders
• Create dashboards for monitoring

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4. Maintain and Evolve

• Review effectiveness quarterly
• Update criteria as business rules change
• Archive unused Data Mines
• Continuously improve based on outcomes

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Common Use Cases That Drive ROI

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Financial Services

• Fraud Detection: Monitor transactions for unusual patterns
• Risk Management: Track exposure levels and compliance violations
• Customer Onboarding: Automate approval workflows

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Healthcare

• Claims Processing: Automate review and approval workflows
• Patient Care: Monitor treatment protocols and outcomes
• Compliance: Track regulatory requirements

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Manufacturing

• Quality Control: Monitor production metrics and defect rates
• Supply Chain: Track inventory levels and supplier performance
• Maintenance: Predict equipment failures and schedule repairs

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Retail

• Inventory Management: Optimize stock levels and reorder points
• Customer Service: Route tickets based on complexity and priority
• Pricing: Monitor competitor pricing and market conditions

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Troubleshooting Common Issues

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Data Mine Not Triggering

• Verify CloudLink connectivity
• Check matching criteria syntax
• Ensure data meets conditions
• Review schedule configuration

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Too Many Notifications

• Refine matching criteria to be more specific
• Adjust schedule frequency
• Review state management logic
• Consider grouping related conditions

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Performance Issues

• Reduce matching result set size
• Optimize database queries
• Adjust schedule timing
• Consider data archiving

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AI Agent Not Responding

• Verify agent configuration
• Check data quality and completeness
• Review agent training and context
• Monitor agent performance metrics

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Measuring Success

• Time saved on manual processes
• Reduction in missed opportunities
• Improvement in response times
• Cost savings from automation
• User satisfaction scores

ROI = (Time Saved × Hourly Rate × Frequency) - (Setup + Maintenance Costs)

• Decreased manual intervention
• Improved consistency in processes
• Faster response to business events
• Better compliance with business rules
• Enhanced customer satisfaction