Module 10.2: Event Correlation Platforms

Toolkit Track | Complexity: [MEDIUM] | Time: 40-45 min

Prerequisites

Before starting this module:

Module 6.3: Event Correlation — Correlation concepts
Understanding of ITSM/incident management
Familiarity with enterprise monitoring tools

What You’ll Be Able to Do

After completing this module, you will be able to:

Deploy event correlation platforms that aggregate and deduplicate alerts from multiple monitoring sources
Configure correlation rules to identify root cause from cascading alert patterns across services
Implement automated incident enrichment with topology context and historical incident matching
Evaluate event correlation platforms (BigPanda, Moogsoft, PagerDuty AIOps) for operational maturity levels

Why This Module Matters

You’ve learned correlation concepts—now understand the commercial platforms that implement them at enterprise scale. BigPanda, Moogsoft, PagerDuty AIOps, and ServiceNow ITOM each approach the problem differently. Choosing the right platform can mean the difference between 90% noise reduction and a shelfware investment.

Did You Know?

Moogsoft coined the term “AIOps” in 2016, defining it as “Artificial Intelligence for IT Operations.” Before this, the space was called “IT Operations Analytics” (ITOA). The term has since been adopted industry-wide.
BigPanda started as a hackathon project at Hewlett-Packard. The founders noticed engineers spending more time managing alerts than fixing problems and built the first prototype in 48 hours.
The average enterprise deals with 1,000+ monitoring tools generating millions of events daily. Gartner reports that organizations using AIOps platforms see 50-90% reduction in mean time to repair (MTTR).
PagerDuty processes over 30 million events per day across their platform. Their AIOps features emerged from analyzing patterns in this massive dataset to automatically identify what matters.

War Story: The $2M Platform That Nobody Used

A Fortune 500 company spent $2M on an enterprise AIOps platform after an impressive vendor demo. Six months later, it was shelfware. What went wrong?

No data hygiene first: Their alerts had inconsistent naming, missing tags, and no topology data. The platform couldn’t correlate what it couldn’t understand.
POC with vendor data: They tested with the vendor’s sample dataset, not their own messy alerts.
No integration plan: The platform sat isolated—alerts still went to the old system because nobody mapped the integrations.
Unrealistic expectations: Leadership expected 90% noise reduction in week one. When it didn’t happen, they lost faith.

The fix: They paused, spent 3 months cleaning alert quality and building a CMDB, then re-enabled the platform. It now achieves 85% noise reduction—but only after they fixed the foundation first.

The lesson: The best AIOps platform can’t fix bad data. Clean your alerts, build your topology, and POC with YOUR data before signing contracts.

Platform Landscape

EVENT CORRELATION PLATFORMS
─────────────────────────────────────────────────────────────────

                 CORRELATION FOCUS
                 ─────────────────
                        ▲
           ML-Heavy     │      Topology-Heavy
                        │
      ┌─────────────────┼─────────────────┐
      │                 │                 │
      │    Moogsoft     │    BigPanda     │
      │                 │                 │
◀─────┼─────────────────┼─────────────────┼─────▶
Startup               │                Enterprise
Focus                 │                Focus
      │                 │                 │
      │   PagerDuty     │   ServiceNow    │
      │     AIOps       │      ITOM       │
      │                 │                 │
      └─────────────────┼─────────────────┘
                        │
           Light AI     │     Heavy ITSM
                        ▼
                 INTEGRATION FOCUS

BigPanda

Overview

BigPanda pioneered the “Event Correlation and Automation” category. Its strength is topology-aware correlation—using service dependency data to group alerts intelligently.

Key Features

BIGPANDA ARCHITECTURE
─────────────────────────────────────────────────────────────────

DATA SOURCES                    BIGPANDA                 OUTPUT
─────────────────────────────────────────────────────────────────

┌─────────────┐               ┌───────────────────┐
│ Monitoring  │──────────────▶│   Normalization   │
│ (Datadog,   │               │   - Schema mapping│
│  Prometheus)│               │   - Enrichment    │
└─────────────┘               └─────────┬─────────┘
                                        │
┌─────────────┐                         ▼
│   Alerts    │──────────────▶┌───────────────────┐
│ (PagerDuty, │               │   Correlation     │
│  OpsGenie)  │               │   - Topology-based│   ┌─────────┐
└─────────────┘               │   - Time-based    │──▶│Incidents│
                              │   - Tag-based     │   └─────────┘
┌─────────────┐               └─────────┬─────────┘
│  ITSM       │──────────────▶│         │
│ (ServiceNow,│               │         ▼
│  Jira)      │               ┌───────────────────┐
└─────────────┘               │      Analysis     │
                              │   - Root cause    │   ┌─────────┐
┌─────────────┐               │   - Impact        │──▶│ Actions │
│  Topology   │──────────────▶│   - Automation    │   └─────────┘
│ (CMDB, auto-│               └───────────────────┘
│  discovery) │
└─────────────┘

Correlation Capabilities

Capability	Description
Topology correlation	Uses CMDB/service maps to group related alerts
Tag-based correlation	Groups alerts with matching tags/labels
Time-based correlation	Windows-based grouping
ML clustering	Text similarity for unknown relationships
Pattern recognition	Learns from historical incidents

Configuration Example

# BigPanda correlation rule (conceptual)
correlation_rules:
  - name: "Database Cascade"
    conditions:
      - tag: "component" matches "database|mysql|postgres"
      - severity: "critical|high"
    correlation:
      type: "topology"
      window: "5m"
      grouping:
        - by: "application"
        - by: "environment"
    actions:
      - notify: "dba-team"
      - auto_remediate:
          runbook: "database-health-check"
          approval: "auto"  # or "manual"

Strengths & Limitations

Strengths	Limitations
Excellent topology correlation	Premium pricing
Strong integration ecosystem	Complex initial setup
Automation capabilities	Learning curve for advanced features
Enterprise-grade scalability	Requires good data hygiene

Moogsoft

Overview

Moogsoft (now part of Dell) pioneered “AIOps” as a term. Its strength is ML-based alert clustering—grouping similar alerts even without explicit topology data.

Key Features

MOOGSOFT ARCHITECTURE
─────────────────────────────────────────────────────────────────

             ┌──────────────────────────────────────┐
             │          MOOGSOFT PLATFORM           │
             │                                      │
EVENTS ─────▶│  ┌────────────────────────────────┐ │
             │  │     Ingestion & Dedup          │ │
             │  └─────────────┬──────────────────┘ │
             │                │                    │
             │  ┌─────────────▼──────────────────┐ │
             │  │    Clustering (Sigaliser)      │ │
             │  │    - Text similarity           │ │
             │  │    - Temporal proximity        │ │
             │  │    - Service context           │ │
             │  └─────────────┬──────────────────┘ │
             │                │                    │
             │  ┌─────────────▼──────────────────┐ │
             │  │    Situation Room              │ │
             │  │    - Correlated incidents      │ │──▶ INCIDENTS
             │  │    - Probable cause            │ │
             │  │    - Affected services         │ │
             │  └─────────────┬──────────────────┘ │
             │                │                    │
             │  ┌─────────────▼──────────────────┐ │
             │  │    Workflow Automation         │ │──▶ ACTIONS
             │  │    - Runbook execution         │ │
             │  │    - Notification routing      │ │
             │  └────────────────────────────────┘ │
             │                                      │
             └──────────────────────────────────────┘

ML Clustering (Sigaliser)

Moogsoft’s Sigaliser uses:

Temporal clustering: Alerts close in time
Text similarity: Similar descriptions/messages
Entropy-based: Information content analysis
Topology hints: When available

SIGALISER CLUSTERING
─────────────────────────────────────────────────────────────────

Raw Alerts                         Clustered Situation
─────────────────────────────────────────────────────────────────

[10:30] db-01: Connection refused
[10:30] api-01: Database timeout      ──▶   SITUATION: Database
[10:31] api-02: Database timeout              Connectivity Issue
[10:31] web-01: API 504 error                 ────────────────────
[10:31] web-02: API 504 error                 Alerts: 10
[10:32] health: /api failing                  Services: 5
[10:32] queue: Messages backing up            Probable cause:
[10:33] api-01: Database timeout              db-01
...                                           First seen: 10:30

Strengths & Limitations

Strengths	Limitations
ML clustering (no topology needed)	Complex UI
Good for heterogeneous environments	Resource intensive
Strong noise reduction	Can over-cluster
On-prem option available	Steep learning curve

PagerDuty AIOps

Overview

PagerDuty’s AIOps is integrated into their incident management platform. It’s simpler than BigPanda/Moogsoft but benefits from tight integration with on-call and incident response.

Key Features

PAGERDUTY AIOPS
─────────────────────────────────────────────────────────────────

┌───────────────────────────────────────────────────────────────┐
│                    PAGERDUTY PLATFORM                         │
│                                                               │
│  ┌─────────────┐    ┌─────────────┐    ┌─────────────┐       │
│  │   Events    │───▶│  Intelligent│───▶│  Incident   │       │
│  │   API       │    │  Grouping   │    │  Response   │       │
│  └─────────────┘    └─────────────┘    └─────────────┘       │
│                            │                   │              │
│                     ┌──────▼──────┐     ┌──────▼──────┐      │
│                     │  Noise      │     │   On-Call   │      │
│                     │  Reduction  │     │  Scheduling │      │
│                     └─────────────┘     └─────────────┘      │
│                            │                   │              │
│                     ┌──────▼──────┐     ┌──────▼──────┐      │
│                     │   Related   │     │  Automation │      │
│                     │   Incidents │     │  Actions    │      │
│                     └─────────────┘     └─────────────┘      │
│                                                               │
└───────────────────────────────────────────────────────────────┘

Configuration

# PagerDuty Event Orchestration (conceptual)
rules:
  - name: "High Severity Database"
    conditions:
      - field: "severity"
        value: "critical"
      - field: "source"
        contains: "database"
    actions:
      - route: "database-team"
      - priority: "P1"
      - auto_pause_notifications:
          duration: "5m"  # Wait for related alerts

  - name: "Noise Suppression"
    conditions:
      - field: "class"
        value: "health_check"
      - field: "environment"
        value: "staging"
    actions:
      - suppress: true

intelligent_grouping:
  enabled: true
  time_window: "5m"
  content_based: true  # Group similar alert messages

Strengths & Limitations

Strengths	Limitations
Tight incident management integration	Less sophisticated correlation
Easy to adopt (if using PD)	Limited topology awareness
Good event orchestration	Basic ML capabilities
Strong mobile experience	Not a standalone AIOps platform

ServiceNow ITOM

Overview

ServiceNow’s IT Operations Management extends their ITSM platform with AIOps capabilities. Its strength is integration with the broader ServiceNow ecosystem.

Key Features

SERVICENOW ITOM
─────────────────────────────────────────────────────────────────

┌───────────────────────────────────────────────────────────────┐
│                   SERVICENOW PLATFORM                         │
│                                                               │
│  ┌─────────────────────────────────────────────────────────┐ │
│  │                     ITOM SUITE                          │ │
│  │                                                         │ │
│  │  ┌───────────┐ ┌───────────┐ ┌───────────┐            │ │
│  │  │ Discovery │ │  Event    │ │  Health   │            │ │
│  │  │           │ │Management │ │   Log     │            │ │
│  │  │ (CMDB)    │ │(Correlate)│ │ Analytics │            │ │
│  │  └─────┬─────┘ └─────┬─────┘ └─────┬─────┘            │ │
│  │        │             │             │                   │ │
│  │        └─────────────┼─────────────┘                   │ │
│  │                      │                                 │ │
│  │                ┌─────▼─────┐                          │ │
│  │                │ Predictive│                          │ │
│  │                │Intelligence                          │ │
│  │                │(AI/ML)    │                          │ │
│  │                └─────┬─────┘                          │ │
│  │                      │                                 │ │
│  └──────────────────────┼──────────────────────────────────┘ │
│                         │                                     │
│  ┌──────────────────────▼──────────────────────────────────┐ │
│  │                  ITSM INTEGRATION                       │ │
│  │  ┌───────────┐ ┌───────────┐ ┌───────────┐            │ │
│  │  │ Incidents │ │  Changes  │ │ Knowledge │            │ │
│  │  └───────────┘ └───────────┘ └───────────┘            │ │
│  └─────────────────────────────────────────────────────────┘ │
│                                                               │
└───────────────────────────────────────────────────────────────┘

Strengths & Limitations

Strengths	Limitations
Deep ITSM integration	Complex implementation
CMDB-powered correlation	ServiceNow ecosystem lock-in
Enterprise governance	Expensive
Comprehensive workflow	Steeper learning curve

Platform Comparison

Feature Matrix

FEATURE COMPARISON
─────────────────────────────────────────────────────────────────

                     BigPanda  Moogsoft  PagerDuty  ServiceNow
─────────────────────────────────────────────────────────────────
ML Correlation          ★★★      ★★★       ★★         ★★
Topology Correlation    ★★★      ★★        ★          ★★★
Integration Ecosystem   ★★★      ★★        ★★★        ★★
ITSM Integration       ★★        ★★        ★★         ★★★
Automation             ★★★       ★★        ★★         ★★★
Ease of Setup          ★★        ★         ★★★        ★
Pricing                $$$       $$$       $$         $$$$
On-Premise Option      ✓         ✓         ✗          ✓
─────────────────────────────────────────────────────────────────

★ = Basic  ★★ = Good  ★★★ = Excellent

Selection Guide

PLATFORM SELECTION
─────────────────────────────────────────────────────────────────

"We have strong topology/CMDB data"
└──▶ BigPanda
     • Leverages your existing investment
     • Best topology-based correlation

"We have messy, heterogeneous monitoring"
└──▶ Moogsoft
     • ML clustering works without topology
     • Good for complex environments

"We already use PagerDuty for on-call"
└──▶ PagerDuty AIOps
     • Seamless integration
     • Lower adoption barrier

"We're standardized on ServiceNow"
└──▶ ServiceNow ITOM
     • Full ITSM integration
     • Unified platform

"We're a smaller team with limited budget"
└──▶ PagerDuty AIOps or Open Source
     • More affordable
     • Good enough for many use cases

Evaluation Criteria

POC Checklist

When evaluating platforms:

# Event Correlation Platform POC Checklist

## Data Integration
- [ ] Connects to our monitoring tools (Prometheus, Datadog, etc.)
- [ ] Handles our event volume (X events/day)
- [ ] Supports our alert formats
- [ ] API available for custom integrations

## Correlation Quality
- [ ] Reduces alert volume by >80%
- [ ] Groups related alerts accurately
- [ ] False positive rate acceptable (<10%)
- [ ] Root cause suggestions are helpful

## Automation
- [ ] Supports our runbook format
- [ ] Integrates with our automation tools
- [ ] Approval workflows configurable
- [ ] Rollback capabilities

## Operations
- [ ] Acceptable latency (alert to incident)
- [ ] Scales to our requirements
- [ ] SLA meets our needs
- [ ] Support responsiveness acceptable

## Cost
- [ ] Within budget
- [ ] Pricing model predictable
- [ ] Implementation cost reasonable
- [ ] Ongoing operational cost acceptable

ROI Calculation

def calculate_aiops_roi(
    monthly_alerts,
    avg_triage_time_minutes,
    hourly_eng_cost,
    platform_monthly_cost,
    expected_noise_reduction=0.8
):
    """
    Calculate ROI for AIOps platform investment.
    """
    # Current cost
    hours_triaging = (monthly_alerts * avg_triage_time_minutes) / 60
    current_monthly_cost = hours_triaging * hourly_eng_cost

    # With AIOps
    reduced_alerts = monthly_alerts * (1 - expected_noise_reduction)
    new_hours_triaging = (reduced_alerts * avg_triage_time_minutes) / 60
    new_monthly_cost = new_hours_triaging * hourly_eng_cost + platform_monthly_cost

    # ROI
    monthly_savings = current_monthly_cost - new_monthly_cost
    roi_percent = (monthly_savings / platform_monthly_cost) * 100

    return {
        'current_monthly_cost': current_monthly_cost,
        'new_monthly_cost': new_monthly_cost,
        'monthly_savings': monthly_savings,
        'annual_savings': monthly_savings * 12,
        'roi_percent': roi_percent
    }

# Example
result = calculate_aiops_roi(
    monthly_alerts=50000,
    avg_triage_time_minutes=5,
    hourly_eng_cost=100,
    platform_monthly_cost=5000,
    expected_noise_reduction=0.85
)

print(f"Monthly savings: ${result['monthly_savings']:,.0f}")
print(f"Annual savings: ${result['annual_savings']:,.0f}")
print(f"ROI: {result['roi_percent']:.0f}%")

# Expected output:
# Monthly savings: $30,417
# Annual savings: $365,000
# ROI: 508%
#
# Typical enterprise values:
# - Monthly alerts: 10,000 - 500,000
# - Avg triage time: 3-10 minutes
# - Hourly eng cost: $75-150 (fully loaded)
# - Platform cost: $3,000-50,000/month
# - Noise reduction: 70-90% (after tuning)

Hands-On Exercise: Platform Evaluation

Create an evaluation framework for your organization:

Step 1: Document Requirements

# AIOps Platform Requirements

## Must-Have
- [ ] Integration with [your monitoring tools]
- [ ] Handle [X] events per day
- [ ] < [Y] second correlation latency
- [ ] SOC2 / compliance requirement

## Should-Have
- [ ] Topology-based correlation
- [ ] ML-based clustering
- [ ] Auto-remediation support
- [ ] Mobile app

## Nice-to-Have
- [ ] On-premise option
- [ ] Custom ML models
- [ ] ChatOps integration

Step 2: Create Scoring Matrix

# Platform Scoring Matrix

| Criteria (Weight) | BigPanda | Moogsoft | PagerDuty | ServiceNow |
|-------------------|----------|----------|-----------|------------|
| Integration (25%) |   /10    |   /10    |    /10    |    /10     |
| Correlation (25%) |   /10    |   /10    |    /10    |    /10     |
| Automation (20%)  |   /10    |   /10    |    /10    |    /10     |
| Ease of Use (15%) |   /10    |   /10    |    /10    |    /10     |
| Cost (15%)        |   /10    |   /10    |    /10    |    /10     |
|-------------------|----------|----------|-----------|------------|
| TOTAL             |   /100   |   /100   |   /100    |   /100     |

Step 3: Run POC

Use sample data to test correlation:

# Generate test events for POC
import json
from datetime import datetime, timedelta
import random

def generate_poc_events(n_events=1000, n_incidents=20):
    """
    Generate realistic events for platform POC.
    """
    events = []
    base_time = datetime.now() - timedelta(hours=24)

    # Services and their dependencies
    services = {
        'frontend': ['api-gateway'],
        'api-gateway': ['user-service', 'order-service'],
        'user-service': ['database', 'cache'],
        'order-service': ['database', 'queue'],
        'database': [],
        'cache': [],
        'queue': []
    }

    # Generate incident cascades
    for incident_id in range(n_incidents):
        incident_time = base_time + timedelta(minutes=random.randint(0, 1440))
        root_cause = random.choice(['database', 'cache', 'queue'])

        # Root cause event
        events.append({
            'id': f'evt-{len(events)}',
            'timestamp': incident_time.isoformat(),
            'source': root_cause,
            'severity': 'critical',
            'message': f'{root_cause} is down',
            'tags': {'service': root_cause, 'incident': f'inc-{incident_id}'}
        })

        # Cascade events
        for service, deps in services.items():
            if root_cause in deps or (root_cause in ['database'] and 'database' in deps):
                delay = random.randint(10, 120)  # seconds
                events.append({
                    'id': f'evt-{len(events)}',
                    'timestamp': (incident_time + timedelta(seconds=delay)).isoformat(),
                    'source': service,
                    'severity': random.choice(['high', 'critical']),
                    'message': f'Connection to {root_cause} failed',
                    'tags': {'service': service, 'incident': f'inc-{incident_id}'}
                })

    # Add noise events
    noise_count = n_events - len(events)
    for _ in range(noise_count):
        events.append({
            'id': f'evt-{len(events)}',
            'timestamp': (base_time + timedelta(minutes=random.randint(0, 1440))).isoformat(),
            'source': random.choice(list(services.keys())),
            'severity': 'warning',
            'message': random.choice([
                'High CPU usage',
                'Memory threshold exceeded',
                'Disk usage warning',
                'Health check slow'
            ]),
            'tags': {'type': 'noise'}
        })

    # Sort by timestamp
    events.sort(key=lambda e: e['timestamp'])

    return events

# Generate and save
events = generate_poc_events(n_events=1000, n_incidents=20)

with open('poc_events.json', 'w') as f:
    json.dump(events, f, indent=2)

print(f"Generated {len(events)} events")
print(f"Expected incidents: 20")
print(f"Expected correlation rate: {20 / len(events) * 100:.1f}%")

Success Criteria

You’ve completed this exercise when:

Key Takeaways

No perfect platform: Each has strengths and weaknesses
Match to your environment: Topology-heavy? BigPanda. Messy data? Moogsoft
Integration matters: Best platform is useless if it doesn’t integrate
POC with real data: Vendor demos don’t show your data’s quirks
Calculate ROI: Justify investment with noise reduction metrics
Consider total cost: License + implementation + operations

Common Mistakes

Mistake	Problem	Solution
Buying before cleaning data	Garbage in, garbage out	Clean alert quality first, then evaluate platforms
Ignoring topology data	Missing the best correlation signal	Invest in CMDB/service maps before or alongside platform
Skipping POC with real data	Vendor demos don’t reflect reality	Always test with your actual event stream
Over-configuring rules	Complex rules become unmaintainable	Start simple, let ML learn patterns
Expecting instant ROI	Platforms need tuning time	Plan for 3-6 month optimization period
No integration strategy	Platform becomes isolated silo	Map all integrations before purchase

Quiz

1. What's the key difference between BigPanda's and Moogsoft's correlation approaches?

Answer:

BigPanda excels at topology-based correlation—it leverages CMDB data, service dependencies, and infrastructure relationships to group related alerts. Best when you have strong topology/dependency data.
Moogsoft excels at ML-based clustering—it uses text similarity, temporal proximity, and pattern recognition to group alerts even without explicit topology. Best when you have messy, heterogeneous environments without clean dependency data.

BigPanda needs good data hygiene; Moogsoft can work with messier environments but may over-cluster.

2. When would you choose PagerDuty AIOps over a dedicated platform like BigPanda?

Answer: Choose PagerDuty AIOps when:

You’re already using PagerDuty for on-call and incident management
Your correlation needs are moderate (not highly complex)
Tight integration with incident response matters more than advanced correlation
Budget is a concern (included in PagerDuty tiers)
Your team values simplicity over feature depth

Choose BigPanda when:

You need sophisticated topology-based correlation
You have complex multi-tool environments
Advanced automation/runbook execution is critical
You need a dedicated AIOps solution

3. What should you evaluate during an AIOps platform POC?

Answer: Key POC evaluation criteria:

Data Integration:

Connects to your monitoring tools without custom development
Handles your event volume without lag
API available for custom integrations

Correlation Quality:

Alert volume reduction (target: >80%)
Grouping accuracy (related alerts correctly clustered)
False positive rate (<10%)
Root cause suggestions usefulness

Operations:

Alert-to-incident latency
Scales to your requirements
Support responsiveness

Cost:

Total cost (license + implementation + operations)
Pricing model predictability

4. Why is topology/CMDB data so valuable for event correlation?

Answer: Topology data enables correlation to understand causality, not just coincidence:

Dependency chains: If database fails, the platform knows which services depend on it and groups those alerts together
Blast radius: Instantly understand impact scope from topology
Root cause identification: Topology points to the originating component
Noise reduction: Group downstream symptoms with upstream cause
Automation targeting: Know exactly where to apply remediation

Without topology, platforms rely on time proximity and text similarity, which may incorrectly group unrelated alerts that happen to occur simultaneously.

Summary

Event correlation platforms—BigPanda, Moogsoft, PagerDuty AIOps, ServiceNow ITOM—each take different approaches. BigPanda excels at topology-based correlation, Moogsoft at ML clustering, PagerDuty at incident management integration, and ServiceNow at ITSM workflows. Choose based on your existing investments, data quality, and team capabilities.

Next Module

Continue to Module 10.3: Observability AI Features to learn about AI built into observability platforms.

Module 10.2: Event Correlation Platforms

Prerequisites

What You’ll Be Able to Do

Why This Module Matters

Did You Know?

War Story: The $2M Platform That Nobody Used

Platform Landscape

BigPanda

Overview

Key Features

Correlation Capabilities

Configuration Example

Strengths & Limitations

Moogsoft

Overview

Key Features

ML Clustering (Sigaliser)

Strengths & Limitations

PagerDuty AIOps

Overview

Key Features

Configuration

Strengths & Limitations

ServiceNow ITOM

Overview

Key Features

Strengths & Limitations

Platform Comparison

Feature Matrix

Selection Guide

Evaluation Criteria

POC Checklist

ROI Calculation

Hands-On Exercise: Platform Evaluation

Step 1: Document Requirements

Step 2: Create Scoring Matrix

Step 3: Run POC

Success Criteria

Key Takeaways

Common Mistakes

Quiz

Further Reading

Summary

Next Module