Single Pane of Glass: Consolidating Your Production Alerts

Your team uses six different tools to understand what's happening in production:

• Slack - Team discussions
• GitHub - Deployments and releases
• PagerDuty - Incident alerts
• Datadog - Infrastructure metrics
• Sentry - Error tracking
• Linear - Issue tracking

When something breaks, your on-call engineer has to open all six tools, piece together information from each one, and construct a mental model of what happened. By the time they understand the problem, 30+ minutes have passed.

This is the problem with fragmented tooling. And it's costing you time, money, and team morale.

In this guide, we'll explore:

• Why fragmented tools are killing productivity
• What a single pane of glass really means
• How to build one
• Real examples of teams doing it right

---

The Fragmentation Problem

What fragmentation looks like:

2:15 AM - Production incident fires
On-call opens PagerDuty:
"API is returning 500 errors"

Checks Slack:
"@channel api is down"
"anyone know what changed?"
"was there a deploy?"

Checks GitHub:
"Deploy 2.5 went out 2 min before incident"
"changes: Auth service refactor"

Checks Datadog:
"CPU spike 2:12 AM"
"Memory usage normal"
"Network traffic normal"

Checks Sentry:
"500 errors increasing"
"Auth service errors spiking"

Finally understands:
"Deploy caused auth service to use more CPU, hitting limit"

Total time: 35 minutes

The costs of fragmentation:

Time waste:

• Per incident: 30+ minutes gathering context
• Per year: 60+ hours per person
• Team of 8: 480+ hours per year
• Cost: $25K-$50K in lost productivity

Decision delays:

• Slow context = slow decisions
• Slow decisions = longer incidents
• Longer incidents = more customer impact

Turnover:

• On-call burnout increases (constant context-switching)
• Engineers leave (tired of the chaos)
• New engineers slow to ramp (have to learn all the tools)

Errors:

• Incomplete context = bad decisions
• Bad decisions = wrong fixes
• Wrong fixes = escalation of incidents

---

What is a "Single Pane of Glass"?

A single pane of glass is a centralized view that shows everything important at once.

Key characteristics:

Consolidated data

All events from all tools in one place:

• Slack discussions
• GitHub deployments
• PagerDuty alerts
• Datadog metrics
• Sentry errors

Unified timeline

• All events timestamped and ordered
• See what happened when
• Understand sequences and causality
• Spot patterns

Contextual information

• Who deployed?
• What changed?
• Why was it deployed?
• Team discussions

Searchable history

• Find past incidents quickly
• Search by service, keyword, timeframe
• Learn from history
• Faster diagnosis

Relevant filtering

• Show only what matters
• AI filters noise
• Critical events only
• Customizable views

---

Example of Single Pane of Glass

ENGINEERING BRIEF - Tuesday, Jan 15

🚀 Releases (3)
├─ 2:13 PM: v2.5 deployed (Auth service)
├─ 4:22 PM: v3.1 deployed (API service)
└─ 6:45 PM: v1.2 deployed (Web frontend)

⚠️ Incidents (2)
├─ 2:15 PM - 2:52 PM: Auth service 500 errors (RESOLVED)
│  Cause: v2.5 caused CPU spike
│  Impact: 15 minutes downtime
│  Status: Resolved via rollback
└─ 4:25 PM - 4:33 PM: API latency spike (RESOLVED)
   Cause: Database connection pool exhaustion
   Impact: 8 minutes degradation
   Status: Resolved via restart

📊 Infrastructure Changes (2)
├─ 2:00 PM: Database upgraded to new instance
└─ 3:30 PM: API service replicas increased from 3 → 5

💬 Key Discussions (5)
├─ "#devops: DB migration completed"
├─ "#engineering: v2.5 ready for deploy"
├─ "#incident: auth service down, investigating"
├─ "#engineering: rolling back v2.5"
└─ "#incident: all clear, services restored"

INSIGHTS:
✓ v2.5 caused incident (correlation: deploy → errors)
✓ 20 minute MTTR (good response)
✓ Team communicated clearly

This entire brief can be generated in 30 seconds from consolidated data.

---

Why Fragmentation is Worse Than It Seems

Hidden costs of fragmentation:

1. Cognitive overload

Brain power breakdown per incident:

1. Open PagerDuty: 1 minute
2. Open Slack: 1 minute (find relevant channel)
3. Open GitHub: 1 minute (find relevant repo)
4. Open Datadog: 1 minute (find relevant dashboard)
5. Open Sentry: 1 minute (find relevant service)
6. Total: 5 minutes just opening tools
7. Mental context-switching: 10 minutes
8. Actual problem-solving: 20 minutes

2. Information loss

Engineer 1: "I think it's related to deploy"
Engineer 2: "Let me check GitHub"
Engineer 3: "Found it, but where's the error?"
Engineer 1: "Try Sentry"
Engineer 4: "Can someone explain what we're doing?"

Result: Wasted time, confused team, slow decision-making

3. Institutional knowledge loss

When engineer leaves:

• "Where are incident playbooks?"
• "Which dashboard is for metrics?"
• "How do we search for past incidents?"
• "What tools do we use?"

New engineer spends 1-2 weeks learning the tooling maze

4. Alert fatigue

• Tool 1: 50 alerts/day
• Tool 2: 30 alerts/day
• Tool 3: 20 alerts/day
• Total: 100 alerts/day

Team ignores 95% of them. Real incident gets lost in noise.

---

How to Build a Single Pane of Glass

Option 1: Manual consolidation (Not recommended)

Create a dashboard that pulls data from all tools:

• Slack API → get recent messages from #incident, #devops
• GitHub API → get recent deployments
• PagerDuty API → get current/recent incidents
• Datadog API → get metrics and events
• Sentry API → get error spikes

Problems:

• Takes time to build and maintain
• Limited to what you can customize
• Breaks when APIs change
• Ongoing engineering effort

Option 2: Use existing tools

Some tools have integrations:

• Datadog - Can pull from Slack, GitHub, PagerDuty
• PagerDuty - Can notify across channels
• Slack - Can receive alerts from most tools

Problems:

• Limited consolidation
• Still requires manual checking
• Not searchable history
• Not AI-filtered

Option 3: Dedicated ops intelligence platform

Use a platform designed for this (like OpsBrief):

• Automatic consolidation from all sources
• Daily digest emails
• Searchable timeline
• AI filtering
• Built for ops teams

Benefits:

• Turnkey solution
• Regular updates
• Dedicated support
• Focus on actual incidents, not tool management

---

Building Your Own: Step-by-Step

If you want to build it yourself:

Step 1: Choose your sources

const sources = {
  slack: { 
    api: 'https://api.slack.com', 
    events: ['messages', 'reactions'] 
  },
  github: { 
    api: 'https://api.github.com', 
    events: ['push', 'release'] 
  },
  pagerduty: { 
    api: 'https://api.pagerduty.com', 
    events: ['incident'] 
  },
  datadog: { 
    api: 'https://api.datadoghq.com', 
    events: ['alert', 'event'] 
  },
  sentry: { 
    api: 'https://sentry.io/api', 
    events: ['error', 'spike'] 
  }
};

Step 2: Extract and normalize events

interface ConsolidatedEvent {
  timestamp: Date;
  source: string; // slack, github, pagerduty, etc
  type: string; // release, incident, error, message
  title: string;
  description: string;
  severity: 'critical' | 'high' | 'medium' | 'low';
  metadata: Record<string, any>;
}

Step 3: Create a timeline

// Get all events from all sources
const events = await Promise.all([
  fetchSlackEvents(),
  fetchGitHubEvents(),
  fetchPagerDutyEvents(),
  fetchDatadogEvents(),
  fetchSentryEvents()
]);

// Flatten and sort by timestamp
const timeline = events.flat().sort((a, b) => 
  a.timestamp - b.timestamp
);

// Return to frontend for display
return timeline;

Step 4: Make it searchable

// Index all events
const searchIndex = timeline.map(event => ({
  id: event.id,
  text: `${event.title} ${event.description} ${event.type}`,
  timestamp: event.timestamp,
  source: event.source
}));

// Search by keyword
function search(query: string) {
  return searchIndex.filter(item => 
    item.text.toLowerCase().includes(query.toLowerCase())
  );
}

Step 5: Filter with AI

// Mark only critical events
const criticalEvents = timeline.filter(event => {
  // Rules:
  // - Incidents: Always critical
  // - Deployments: If near incident time
  // - Errors: If spiking
  // - Infrastructure changes: If significant

  return event.severity === 'critical' || 
         event.isNearIncident ||
         event.isErrorSpike ||
         event.isMajorChange;
});

Step 6: Deliver to team

// Option 1: Email digest
sendDailyDigestEmail(criticalEvents);

// Option 2: Slack message
sendSlackDigest(criticalEvents);

// Option 3: Dashboard
displayDashboard(timeline, searchIndex);

---

Real Example: Team A's Consolidation Journey

Before consolidation

Team:

• 8 engineers
• Using: Slack, GitHub, PagerDuty, Datadog, Sentry
• No unified view
• MTTR: 35-45 minutes

Incident workflow:

1. Alert fires in PagerDuty (on-call sees it)
2. On-call opens Slack, scrolls through 100+ messages
3. Checks GitHub, looks at recent commits
4. Opens Datadog, checks metrics
5. Opens Sentry, checks errors
6. Finally understands problem
7. Starts fixing (30+ min later)

Consolidation plan:

• Week 1-2: Set up API connections to all tools
• Week 3: Build normalized event model
• Week 4: Create searchable timeline
• Week 5: Add AI filtering
• Week 6: Deploy email digest and dashboard

After consolidation

Team:

• Same 8 engineers
• Same tools (but consolidated view on top)
• Daily digest + searchable timeline
• MTTR: 10-15 minutes

New incident workflow:

1. Alert fires in PagerDuty
2. On-call has full context in digest/dashboard:
   • Recent deploy (GitHub)
   • Infrastructure changes (Datadog)
   • Error spikes (Sentry)
   • Team discussions (Slack)
3. Understands problem immediately
4. Starts fixing (2-3 min later)

Impact:

• MTTR reduced by 70%
• Context gathering time: 30 min → 30 seconds
• On-call satisfaction: 4/10 → 8/10
• One resignation prevented (saved $100K+)
• Better team morale

---

Overcoming Implementation Challenges

Challenge 1: API rate limits

Problem: Hitting API limits when fetching from all tools

Solution:

• Cache data locally
• Batch requests
• Use webhooks instead of polling

Challenge 2: Data inconsistency

Problem: Different tools have different timestamp formats, severity levels, etc.

Solution:

• Normalize everything to standard schema
• Map tool-specific levels to unified levels
• Use ISO 8601 for timestamps

Challenge 3: Volume of data

Problem: Too many events to show (100+ per day)

Solution:

• Filter to critical only
• Use AI to detect important events
• Customize what each team sees

Challenge 4: Keeping it updated

Problem: Building once is easy, maintaining is hard

Solution:

• Use webhooks for real-time updates
• Automated testing for API changes
• Documentation for on-call

---

Measuring the Impact

Track these metrics:

1. Context gathering time

• Before: 30+ minutes
• After: < 1 minute
• Goal: < 30 seconds

2. MTTR (Mean Time To Response)

• Before: 35-45 minutes
• After: 10-15 minutes
• Goal: < 15 minutes

3. Team satisfaction

• Before: 4/10
• After: 8/10
• Goal: > 8/10

4. Incident accuracy

• Before: 50% correct diagnosis
• After: 90% correct diagnosis
• Goal: > 95%

5. On-call burnout

• Before: High
• After: Low
• Goal: Prevent resignations

---

Conclusion

Fragmented tooling is costing your team:

• 30+ minutes per incident gathering context
• Higher MTTR (slower incident resolution)
• On-call burnout
• Turnover
• Lost productivity

A single pane of glass fixes all of this by:

• Consolidating all data into one view
• Making it searchable and timestamped
• Filtering for critical events
• Delivering daily digests
• Giving on-call instant context

The investment (building or buying) pays for itself in weeks through:

• Faster incident response
• Prevented burnout
• Prevented resignations
• Better customer experience
• Happier team

The teams that are winning in 2025 are the ones with consolidated ops data and instant context.

Ready to build your single pane of glass?

OpsBrief provides instant consolidation of incidents, releases, deployments, and infrastructure changes. Get your daily brief in 30 seconds. Start free trial