Granola Observability

Overview

Monitor Granola usage, track meeting patterns, and build analytics dashboards. Granola Enterprise includes a usage analytics dashboard. For deeper insights, build custom pipelines using Zapier to stream meeting metadata to BigQuery, Metabase, or other analytics platforms.

Prerequisites

Granola Business or Enterprise plan
Admin access for organization-level analytics
Optional: BigQuery/Metabase for custom dashboards, Zapier for data pipeline

Instructions

Step 1 — Built-in Analytics (Enterprise)

Access the analytics dashboard at Settings > Analytics (Enterprise plan):

| Metric | What It Shows | |--------|--------------| | Total meetings captured | Meeting volume over time | | Active users | Users who recorded meetings this period | | Hours captured | Total meeting hours transcribed | | Notes shared | How often notes are distributed | | Action items created | Extracted action items across org | | Adoption rate | Active users / total licensed seats |

Step 2 — Define Key Metrics

Track these metrics to measure Granola's impact:

| Category | Metric | Target | Formula | |----------|--------|--------|---------| | Adoption | Activation rate | >80% | Users with 1+ meeting / total seats | | Adoption | Weekly active users | >70% | Users recording this week / total seats | | Quality | Capture rate | >70% | Meetings captured / total calendar meetings | | Quality | Share rate | >50% | Notes shared / notes created | | Efficiency | Time saved | >10 min/meeting | Survey: manual notes time - Granola time | | Efficiency | Action completion | >80% | Actions completed / actions created | | Health | Processing success | >99% | Successful enhancements / total attempts | | Health | Integration uptime | >99% | Successful syncs / total sync attempts |

Step 3 — Build a Custom Analytics Pipeline

Stream meeting metadata from Granola to a data warehouse via Zapier:

# Zapier: Granola → BigQuery pipeline
Trigger: Granola — Note Added to Folder ("All Meetings")

Step 1 — Code by Zapier (extract metadata):
  const data = {
    meeting_id: inputData.title + '_' + inputData.calendar_event_datetime,
    title: inputData.title,
    date: inputData.calendar_event_datetime,
    creator: inputData.creator_email,
    attendee_count: JSON.parse(inputData.attendees || '[]').length,
    has_action_items: inputData.note_content.includes('- [ ]'),
    action_item_count: (inputData.note_content.match(/- \[ \]/g) || []).length,
    has_decisions: inputData.note_content.includes('## Decision') ||
                   inputData.note_content.includes('## Key Decision'),
    word_count: inputData.note_content.split(/\s+/).length,
    is_external: JSON.parse(inputData.attendees || '[]')
      .some(a => !a.email?.endsWith('@company.com')),
    workspace: inputData.folder || 'unknown',
    captured_at: new Date().toISOString(),
  };
  output = [data];

Step 2 — BigQuery: Insert Row
  Dataset: meeting_analytics
  Table: granola_meetings
  Row: {{metadata from step 1}}

BigQuery schema:

CREATE TABLE meeting_analytics.granola_meetings (
  meeting_id STRING NOT NULL,
  title STRING,
  date TIMESTAMP,
  creator STRING,
  attendee_count INT64,
  has_action_items BOOL,
  action_item_count INT64,
  has_decisions BOOL,
  word_count INT64,
  is_external BOOL,
  workspace STRING,
  captured_at TIMESTAMP
);

Step 4 — Analytics Queries

-- Weekly meeting volume by workspace
SELECT
  workspace,
  DATE_TRUNC(date, WEEK) AS week,
  COUNT(*) AS meeting_count,
  SUM(action_item_count) AS total_actions,
  AVG(attendee_count) AS avg_attendees
FROM meeting_analytics.granola_meetings
WHERE date >= DATE_SUB(CURRENT_DATE(), INTERVAL 12 WEEK)
GROUP BY workspace, week
ORDER BY week DESC, workspace;

-- Adoption: active users per week
SELECT
  DATE_TRUNC(date, WEEK) AS week,
  COUNT(DISTINCT creator) AS active_users
FROM meeting_analytics.granola_meetings
WHERE date >= DATE_SUB(CURRENT_DATE(), INTERVAL 8 WEEK)
GROUP BY week
ORDER BY week DESC;

-- Meeting efficiency score (has action items + decisions + < 8 attendees)
SELECT
  title,
  date,
  CASE
    WHEN has_action_items AND has_decisions AND attendee_count <= 8 THEN 'Efficient'
    WHEN has_action_items OR has_decisions THEN 'Partially Efficient'
    ELSE 'Low Efficiency'
  END AS efficiency_rating
FROM meeting_analytics.granola_meetings
ORDER BY date DESC
LIMIT 50;

-- External vs internal meeting ratio
SELECT
  DATE_TRUNC(date, MONTH) AS month,
  COUNTIF(is_external) AS external_meetings,
  COUNTIF(NOT is_external) AS internal_meetings,
  ROUND(COUNTIF(is_external) * 100.0 / COUNT(*), 1) AS external_pct
FROM meeting_analytics.granola_meetings
GROUP BY month
ORDER BY month DESC;

Step 5 — Automated Reporting

Weekly Slack digest (via Zapier Schedule):

Trigger: Schedule by Zapier — Every Friday at 5 PM

Step 1 — BigQuery: Run Query
  Query: "SELECT COUNT(*) as meetings, SUM(action_item_count) as actions,
          COUNT(DISTINCT creator) as active_users
          FROM meeting_analytics.granola_meetings
          WHERE date >= DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY)"

Step 2 — Slack: Send Message to #leadership
  Message: |
    :bar_chart: *Weekly Granola Report*

    *This Week:*
    - Meetings captured: {{meetings}}
    - Action items created: {{actions}}
    - Active users: {{active_users}}

    [View full dashboard →]

Step 6 — Health Monitoring and Alerts

Set up alerts for operational issues:

| Alert | Condition | Channel | |-------|-----------|---------| | Low adoption | Active users <50% of seats (weekly) | Slack #it-alerts | | Processing failures | >5% enhancement failures (daily) | PagerDuty | | Integration outage | Slack/Notion/CRM sync failures >3 (hourly) | Slack #it-alerts | | Zero meetings captured | No meetings for any workspace (daily) | Email to workspace admin |

Status monitoring:

# Check Granola service status
curl -s https://status.granola.ai/api/v2/status.json | python3 -c "
import json, sys
data = json.load(sys.stdin)
status = data.get('status', {}).get('description', 'Unknown')
print(f'Granola Status: {status}')
"

Output

Built-in analytics reviewed and baselines established
Custom analytics pipeline streaming to data warehouse
Dashboard visualizing adoption, efficiency, and meeting patterns
Automated weekly/monthly reports delivered to stakeholders
Health monitoring alerts configured for operational issues

Error Handling

| Error | Cause | Fix | |-------|-------|-----| | Missing data in pipeline | Zapier trigger failed | Check Zap history, reconnect if needed | | Duplicate entries in BigQuery | Zapier retry on timeout | Add deduplication (MERGE or INSERT IGNORE) | | Dashboard shows stale data | Pipeline paused | Monitor Zapier health, restart paused Zaps | | Low adoption alert false positive | New seats just added | Adjust alert threshold, use percentage not absolute |

Examples

Test an alert with a fictional meeting event and record dashboard=meeting-r6; alert=delivery-lag; probe=pass; destination=restricted-ops; metrics=aggregate-only; retention=14d; rollback=alert-r5. Never include transcript, attendee, or meeting-topic fields in observability evidence.

Resources

Next Steps

Proceed to granola-incident-runbook for incident response procedures.