Data Mining

Turn your data into intelligent workflows that save money and drive business value

Data Mining: Your Workflow Starting Point

Data Mining is one of the primary ways to start workflows in Elementum. It’s what makes Elementum special - taking your business data (connected through CloudLinks) and operationalizing it into intelligent workflows that save money and drive real business value.

The Power of Data-Driven Automation: Data Mining transforms static data into dynamic business intelligence. Instead of manually reviewing reports and spreadsheets, Data Mining continuously monitors your data and automatically triggers workflows when specific conditions are met. When combined with Elementum’s AI agents, this creates an exceptionally capable system that can handle complex business decisions at scale.

Data Mining is the bridge between your raw business data and actionable workflows. It turns your CloudLink-connected data into a continuous stream of business intelligence that can trigger automations, alert teams, and even hand off decisions to AI agents.

What is a Data Mine?

A Data Mine is an intelligent data monitor that continuously watches your CloudLink-connected data tables and triggers automations when it finds records matching your specified criteria. Think of it as your dedicated data detective - always watching, never sleeping, and instantly acting when something important happens.

Key Components:

• 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

Important: Data Mines without automations provide no value. A Data Mine only becomes valuable when it triggers automations that take action on your behalf. Always plan your automation workflows before creating your Data Mine.

Understanding State Management: The ON/OFF System

Data Mining’s most valuable feature is its intelligent state management system. Rather than sending you the same alert every time it scans your data, it tracks the “state” of each record and only notifies you when something changes.

How State Management Works:

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)

This creates flexible workflow possibilities:

Example 1: High-Value Claims Processing

Business Scenario: Insurance claims over $10,000 need immediate attention

Data Mine Setup:

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

State Management in Action:

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

Example 2: Inventory Threshold Monitoring

Business Scenario: Automatically manage inventory levels across multiple warehouses

Data Mine Setup:

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

State Management in Action:

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

Example 3: SLA Violation Monitoring

Business Scenario: Customer support tickets must be responded to within 24 hours

Data Mine Setup:

• 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

State Management in Action:

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

AI Agent Integration: The Next Level

When Data Mining events are handed off to AI agents, the system becomes exceptionally capable. Agents can:

Analyze Context:

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

Make Intelligent Decisions:

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

Learn and Improve:

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

Example: Intelligent Claims Processing

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

Types of Data Mining

1. Logic-Based Rules Mining

Best for: Clear business rules and known patterns Example: “Alert when any order exceeds $5,000”

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

2. ML Anomaly Detection

Best for: Discovering unexpected patterns or behaviors Example: “Detect unusual spending patterns in expense reports”

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

3. Statistical Anomaly Detection

Best for: Finding numerical outliers using statistical methods Example: “Identify processing times that are unusually long”

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

Setting Up Data Mining: Complete Guide

Step 1: Plan Your Automation First

Before creating a Data Mine, plan what should happen when it triggers:

Questions to Answer:

• 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?

Step 2: Select Your Data Source

Choose your CloudLink-connected table:

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

Step 3: Define Identifying Columns

Purpose: These columns help the system track individual records over time

Best Practices:

• 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

Example for Customer Support:

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

Step 4: Build Your Matching Criteria

Simple Conditions:

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

Complex Logic with Groups:

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

Time-Based Conditions:

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

Step 5: Configure Schedule and Limits

Scheduling Guidelines:

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

Performance Considerations:

• 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

Step 6: Test and Validate

Before Going Live:

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)

Advanced Data Mining Patterns

Pattern 1: Cascade Monitoring

Monitor multiple related conditions in sequence:

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

Pattern 2: Threshold Escalation

Different actions based on severity:

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

Pattern 3: Trend Analysis

Monitor patterns over time:

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

Best Practices for Business Value

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

2. Design for Scale

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

3. Optimize for Business Users

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

4. Maintain and Evolve

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

Common Use Cases That Drive ROI

Financial Services

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

Healthcare

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

Manufacturing

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

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

Troubleshooting Common Issues

Data Mine Not Triggering

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

Too Many Notifications

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

Performance Issues

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

AI Agent Not Responding

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

Measuring Success

Key Metrics to Track:

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

ROI Calculation:

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

Success Indicators:

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

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Remember: Data Mining is your gateway to intelligent automation. When combined with CloudLinks for data access and AI agents for decision-making, it creates a sophisticated system that can transform how your business operates. Start with clear business objectives, design thoughtful automations, and watch your data become your competitive advantage.

For more information on building sophisticated automations that respond to Data Mining events, see our Automation System documentation.