Meeting overload is crushing productivity across organizations worldwide. The average executive spends 23 hours a week in meetings, nearly half of which could be cut without impacting productivity. (Worklytics) For HR analysts and workplace insights teams, understanding meeting patterns at scale requires more than anecdotal evidence—it demands data-driven analysis across your entire organization.
Worklytics provides Google Calendar Data Analytics to measure and optimize employee engagement, integrating with Google Calendar data along with over 25 other tools in your tech stack. (Worklytics) By leveraging Worklytics' BigQuery export capabilities, you can calculate rolling 30-day averages of meeting hours per full-time employee (FTE), compare them against industry benchmarks, and surface organizational hotspots using just a 20-line SQL query.
This tutorial demonstrates how to extract actionable insights from Google Calendar data without manual data cleansing, thanks to Worklytics' pre-modeled tables that eliminate the complexity of raw calendar data processing. (Worklytics)
Meeting metrics serve as a critical indicator of organizational health and productivity. Technology is the highest-growing proportion of the HR budget, with most firms planning to increase their investment in HR tools over the next two years. (HR.com's State of Today's HR Tech Stack and Integrations 2024) However, many organizations struggle to achieve business objectives with their HR technology stacks due to challenges with integrating multiple solutions and difficulties with reconfiguring the stack to adapt to changing business needs.
Worklytics can analyze trends and patterns for team meetings and employee collaboration, providing real-time team metrics, customizable dashboards, and actionable insights from your Google Calendar data. (Worklytics) This capability becomes essential when you consider that excessive meeting time correlates with:
By calculating average meeting hours per employee, HR teams can identify departments or teams that may benefit from meeting hygiene training, process optimization, or workload rebalancing.
Worklytics integrates with Google Calendar to generate actionable analytics from calendar data, enabling businesses to measure and optimize employee engagement. (Worklytics) The platform aggregates and anonymizes data, creating actionable insights and information-rich reports while maintaining privacy compliance with GDPR, CCPA, and other data protection standards. (Worklytics)
The integration captures comprehensive meeting data including:
Worklytics generates over 400 metrics and provides insights at your fingertips, allowing you to see just how engaged your employees are and how they use the tools available to them. (Worklytics) This comprehensive data collection forms the foundation for calculating meaningful meeting hour averages across your organization.
Before diving into SQL queries, you need to configure Worklytics to export your Google Calendar data to BigQuery. Worklytics provides a dataset for SQL queries, making it straightforward to access pre-processed meeting data. (Worklytics)
The setup process involves:
Once configured, Worklytics automatically exports meeting data to your BigQuery dataset, typically with a 24-48 hour delay to ensure data completeness and privacy processing.
Worklytics' pre-modeled tables eliminate manual cleansing by providing clean, structured data ready for analysis. (Worklytics) Here's the core SQL query to calculate rolling 30-day average meeting hours per employee:
| Query Component | Purpose | Key Fields |
|---|---|---|
| Base table | Meeting events with participant data | meeting_id, duration_minutes, participant_email, date |
| Employee mapping | Link meeting participants to FTE status | employee_id, employment_status, department |
| Date filtering | Rolling 30-day window | date >= CURRENT_DATE() - 30 |
| Aggregation | Sum meeting minutes per employee | SUM(duration_minutes), COUNT(DISTINCT employee_id) |
| Calculation | Convert to hours and average | SUM(duration_minutes) / 60 / COUNT(DISTINCT employee_id) |
The query structure leverages Worklytics' normalized data model, which handles common data quality issues like duplicate events, cancelled meetings, and partial participant lists automatically.
The first component identifies active full-time employees within your organization:
WITH active_employees AS (
SELECT DISTINCT employee_id, department, manager_id
FROM `your_project.worklytics.employee_roster`
WHERE employment_status = 'ACTIVE'
AND employment_type = 'FULL_TIME'
AND date = CURRENT_DATE()
)
This ensures your calculations only include current FTE staff, excluding contractors, interns, or recently departed employees who might skew averages.
Next, the query sums meeting minutes for each employee over the rolling 30-day period:
meeting_hours AS (
SELECT
e.employee_id,
e.department,
SUM(m.duration_minutes) / 60.0 AS total_meeting_hours
FROM active_employees e
JOIN `your_project.worklytics.meeting_events` m
ON e.employee_id = m.participant_employee_id
WHERE m.date >= CURRENT_DATE() - 30
AND m.meeting_type IN ('SCHEDULED', 'RECURRING')
GROUP BY e.employee_id, e.department
)
This step handles the core aggregation while filtering out informal or cancelled meetings that shouldn't count toward formal meeting time.
Finally, the query computes department-level and organization-wide averages:
SELECT
department,
COUNT(employee_id) as employee_count,
AVG(total_meeting_hours) as avg_meeting_hours_per_employee,
PERCENTILE_CONT(total_meeting_hours, 0.5) OVER (PARTITION BY department) as median_meeting_hours,
MAX(total_meeting_hours) as max_meeting_hours
FROM meeting_hours
GROUP BY department
ORDER BY avg_meeting_hours_per_employee DESC
This provides both averages and distribution metrics to identify outliers and understand meeting hour patterns across different organizational units.
Manager effectiveness is crucial for team performance, retention, and organizational success, yet traditional measurement methods rely on annual surveys which often deliver outdated insights. (Worklytics) The 23-hour weekly meeting benchmark for executives provides a useful comparison point for your calculated averages.
To incorporate this benchmark into your analysis:
WITH benchmark_comparison AS (
SELECT
department,
avg_meeting_hours_per_employee,
CASE
WHEN avg_meeting_hours_per_employee > 23 THEN 'Above Executive Benchmark'
WHEN avg_meeting_hours_per_employee BETWEEN 15 AND 23 THEN 'Within Normal Range'
WHEN avg_meeting_hours_per_employee < 15 THEN 'Below Average'
END as benchmark_category,
(avg_meeting_hours_per_employee / 23.0) * 100 as benchmark_percentage
FROM department_averages
)
This categorization helps identify departments that may be over-meeting relative to executive-level expectations, suggesting opportunities for meeting optimization.
Worklytics can help improve areas like productivity & performance, company culture, employee engagement, remote & hybrid work, meetings & collaboration, and retention & turnover. (Worklytics) By extending your SQL analysis, you can surface specific hotspots that require attention:
Identify departments with consistently high meeting loads:
SELECT
department,
avg_meeting_hours_per_employee,
employee_count,
(avg_meeting_hours_per_employee * employee_count) as total_dept_meeting_hours
FROM department_averages
WHERE avg_meeting_hours_per_employee > 20
ORDER BY total_dept_meeting_hours DESC
Analyze how meeting time distributes across your organization:
SELECT
CASE
WHEN total_meeting_hours < 10 THEN 'Low (< 10 hrs/week)'
WHEN total_meeting_hours BETWEEN 10 AND 20 THEN 'Moderate (10-20 hrs/week)'
WHEN total_meeting_hours BETWEEN 20 AND 30 THEN 'High (20-30 hrs/week)'
ELSE 'Excessive (> 30 hrs/week)'
END as meeting_load_category,
COUNT(*) as employee_count,
ROUND(COUNT(*) * 100.0 / SUM(COUNT(*)) OVER (), 2) as percentage
FROM meeting_hours
GROUP BY meeting_load_category
ORDER BY employee_count DESC
This analysis reveals what percentage of your workforce falls into different meeting load categories, helping prioritize intervention strategies.
Worklytics brings siloed data under one digital roof, allowing you to see if Google Calendar is a useful and frequent part of collaboration and meeting processes and workflows. (Worklytics) Once you've calculated meeting hour averages, Looker Studio provides powerful visualization capabilities to make insights actionable.
Executive Summary Scorecard:
Department Comparison Chart:
Distribution Histogram:
Trend Analysis:
Looker Studio can connect directly to your BigQuery dataset containing Worklytics data, enabling automated dashboard updates as new meeting data flows in. Configure scheduled email reports to deliver weekly or monthly meeting insights to HR leadership, department heads, and executive teams.
Worklytics can be used to identify and monitor driving factors that negatively impact employee retention, making meeting analysis part of a broader employee experience strategy. (Worklytics) Consider these advanced analytical approaches:
Combine meeting duration with participant counts to calculate efficiency metrics:
SELECT
department,
AVG(duration_minutes * participant_count) as avg_person_minutes_per_meeting,
AVG(duration_minutes) as avg_meeting_duration,
AVG(participant_count) as avg_participants_per_meeting
FROM meeting_events
WHERE date >= CURRENT_DATE() - 30
GROUP BY department
Measure how much meeting time involves cross-departmental collaboration:
WITH cross_dept_meetings AS (
SELECT
meeting_id,
COUNT(DISTINCT department) as dept_count
FROM meeting_participants p
JOIN employee_roster e ON p.employee_id = e.employee_id
GROUP BY meeting_id
HAVING COUNT(DISTINCT department) > 1
)
SELECT
department,
SUM(CASE WHEN cd.meeting_id IS NOT NULL THEN duration_minutes ELSE 0 END) / 60.0 as cross_dept_meeting_hours,
SUM(duration_minutes) / 60.0 as total_meeting_hours,
ROUND(SUM(CASE WHEN cd.meeting_id IS NOT NULL THEN duration_minutes ELSE 0 END) * 100.0 / SUM(duration_minutes), 2) as cross_dept_percentage
FROM meeting_events m
LEFT JOIN cross_dept_meetings cd ON m.meeting_id = cd.meeting_id
GROUP BY department
Analyze meeting loads by organizational level:
SELECT
CASE
WHEN manager_level = 0 THEN 'Individual Contributor'
WHEN manager_level = 1 THEN 'First-Line Manager'
WHEN manager_level >= 2 THEN 'Senior Manager+'
END as role_category,
AVG(total_meeting_hours) as avg_meeting_hours,
PERCENTILE_CONT(total_meeting_hours, 0.5) OVER (PARTITION BY manager_level) as median_meeting_hours
FROM meeting_hours h
JOIN employee_roster e ON h.employee_id = e.employee_id
GROUP BY role_category
ORDER BY avg_meeting_hours DESC
Worklytics uses a proxy to completely anonymize all data at the source, ensuring that individual employee privacy is protected while still enabling organizational insights. (Worklytics) When implementing meeting hour analysis, consider these privacy best practices:
Employee experience is a top priority for many organizations, with many hoping that additional training and the introduction of AI and automation innovations will enhance the end-user experience. (HR.com's State of Today's HR Tech Stack and Integrations 2024) Once you've calculated average meeting hours per employee, the real value comes from taking action on the insights.
Meeting Audit Process:
Process Optimization:
Culture Shifts:
Technology Solutions:
Worklytics helps streamline and optimize meetings, track productivity and performance metrics, analyze diversity, equity, and inclusion, assess management and leadership metrics, and get insight into employee satisfaction, retention, and turnover. (Worklytics) Establish key performance indicators (KPIs) to track the success of your meeting optimization efforts:
Calculating average meeting hours per employee across your Google Workspace organization doesn't have to be a complex, manual process. Worklytics' BigQuery export capabilities combined with pre-modeled data tables enable HR analysts to generate comprehensive meeting insights with just a 20-line SQL query. (Worklytics)
By leveraging the 23-hour executive benchmark and surfacing organizational hotspots through Looker Studio visualizations, you can transform raw calendar data into actionable insights that drive meaningful workplace improvements. The key is moving beyond simple averages to understand meeting patterns, efficiency metrics, and the relationship between collaboration time and organizational outcomes.
Worklytics generates and pushes over 400 metrics to you, providing the comprehensive data foundation needed for sophisticated workplace analytics. (Worklytics) As organizations continue to evolve their hybrid work practices and optimize for productivity, meeting hour analysis becomes an essential tool for HR teams focused on creating better employee experiences while maintaining operational effectiveness.
Start with the basic SQL query outlined in this tutorial, then gradually expand your analysis to include efficiency scoring, cross-functional collaboration patterns, and correlation with business outcomes. Remember that the goal isn't to minimize meetings entirely, but to ensure that the time your employees spend in meetings drives real value for both individuals and the organization as a whole.
You can use Worklytics' BigQuery export feature with a simple 20-line SQL query to calculate rolling 30-day average meeting hours per employee. The query aggregates Google Calendar data across your entire Google Workspace organization, providing insights into meeting patterns and productivity metrics.
According to Worklytics research, the average executive spends 23 hours a week in meetings. Nearly half of these meetings could be cut without impacting productivity, highlighting the significant opportunity for organizations to optimize their meeting culture and improve workplace efficiency.
Worklytics seamlessly integrates with Google Calendar to provide actionable analytics for businesses while maintaining privacy. The platform can analyze trends and patterns for team meetings and employee collaboration, and integrate Google Calendar data with over 25 other tools in your tech stack for comprehensive insights.
Yes, Worklytics provides sample SQL queries for event-level data analysis in their knowledge base documentation. These queries help you extract meaningful insights from your Google Workspace meeting data, including calculating average meeting hours, attendance patterns, and collaboration metrics across your organization.
Worklytics is built with privacy and security as core principles, using a proxy system to completely anonymize all data at the source. The platform aggregates and anonymizes employee work data to create actionable insights while ensuring individual privacy is protected throughout the analytics process.
Meeting analytics can reveal patterns of meeting overload that crush productivity across organizations. By analyzing metrics like average meeting hours per employee, meeting frequency, and collaboration patterns, you can identify teams or individuals spending excessive time in meetings and implement targeted interventions to optimize workplace efficiency.
