Workspace Insights

Workplace Service Delivery Insights

Overview

This skill covers generating insights and analytics for Workplace Service Delivery (WSD) in ServiceNow:

• Analyzing space utilization rates across buildings, floors, and zones
• Tracking desk and room booking patterns to optimize space allocation
• Monitoring facility management service request volumes, categories, and SLA compliance
• Identifying peak usage periods and underutilized spaces for capacity planning
• Generating facility management KPI dashboards with trend analysis
• Producing actionable recommendations for workplace optimization

When to use: When reviewing workplace space efficiency, planning office reconfigurations, analyzing facility service delivery performance, optimizing hot-desking strategies, or preparing workplace analytics for real estate decisions.

Prerequisites

• Roles: wsd_admin, facility_manager, admin, or workspace_admin
• Plugins: com.snc.workplace_service_delivery (Workplace Service Delivery), com.snc.facility_management (Facility Management) recommended
• Access: Read access to wsd_space, wsd_reservation, wsd_floor, wsd_building, fm_facility_request, sc_req_item tables
• Data: Active workplace spaces with reservation and sensor data
• Related Skills: reporting/executive-dashboard for dashboard creation, reporting/trend-analysis for trend analytics

Procedure

Step 1: Retrieve Building and Floor Inventory

Establish the baseline of available workspace inventory.

MCP Approach:

Tool: SN-Query-Table
Parameters:
  table_name: wsd_building
  query: active=true
  fields: sys_id,name,location,total_floors,total_capacity,address,time_zone,operational_status
  limit: 50

Tool: SN-Query-Table
Parameters:
  table_name: wsd_floor
  query: building=[BUILDING_SYS_ID]^active=true
  fields: sys_id,name,building,floor_number,total_spaces,available_spaces,floor_plan
  limit: 20

REST Approach:

GET /api/now/table/wsd_building
  ?sysparm_query=active=true
  &sysparm_fields=sys_id,name,location,total_floors,total_capacity,address,time_zone,operational_status
  &sysparm_display_value=true

GET /api/now/table/wsd_floor
  ?sysparm_query=building=[BUILDING_SYS_ID]^active=true
  &sysparm_fields=sys_id,name,building,floor_number,total_spaces,available_spaces
  &sysparm_display_value=true

Step 2: Analyze Space Utilization

Query space records to calculate utilization metrics.

MCP Approach:

Tool: SN-Query-Table
Parameters:
  table_name: wsd_space
  query: floor.building=[BUILDING_SYS_ID]^active=true
  fields: sys_id,name,space_type,capacity,floor,is_reservable,status,amenities
  limit: 500

Space Type Classification:

| Space Type | Typical Capacity | Bookable | Metrics Focus | |-----------|-----------------|----------|---------------| | Desk | 1 | Yes | Occupancy rate, booking frequency | | Meeting Room | 2-20 | Yes | Booking rate, no-show rate, avg duration | | Phone Booth | 1 | Yes | Utilization rate, peak hours | | Collaboration Zone | 4-12 | Sometimes | Foot traffic, usage duration | | Hot Desk | 1 | Yes | Daily booking rate, user diversity | | Private Office | 1-2 | No | Assignment vs actual presence |

Step 3: Analyze Reservation Patterns

Query booking data to identify usage patterns.

MCP Approach:

Tool: SN-Query-Table
Parameters:
  table_name: wsd_reservation
  query: space.floor.building=[BUILDING_SYS_ID]^start_date>=javascript:gs.daysAgo(30)^ORDERBYstart_date
  fields: sys_id,space,user,start_date,end_date,state,check_in_time,check_out_time,no_show,duration
  limit: 2000

REST Approach:

GET /api/now/table/wsd_reservation
  ?sysparm_query=space.floor.building=[BUILDING_SYS_ID]^start_date>=javascript:gs.daysAgo(30)
  &sysparm_fields=sys_id,space,user,start_date,end_date,state,check_in_time,check_out_time,no_show,duration
  &sysparm_limit=2000
  &sysparm_display_value=true

Key Metrics to Calculate:

| Metric | Formula | Target | |--------|---------|--------| | Booking Rate | Booked hours / Available hours | >60% | | No-Show Rate | No-show reservations / Total reservations | =javascript:gs.daysAgo(30)^state=completed fields: startdate,enddate,space.space_type,space.floor limit: 5000

Aggregate by day of week and hour to build a heatmap:

=== UTILIZATION HEATMAP === Building: [name] Period: Last 30 days

Mon Tue Wed Thu Fri 08:00 35% 42% 68% 55% 28% 09:00 62% 71% 88% 78% 45% 10:00 78% 85% 95% 89% 52% 11:00 82% 88% 97% 91% 48% 12:00 45% 52% 60% 55% 30% 13:00 70% 78% 92% 82% 42% 14:00 75% 82% 90% 85% 38% 15:00 68% 72% 85% 78% 32% 16:00 45% 50% 62% 52% 22% 17:00 20% 25% 30% 25% 10%

PEAK: Wednesday 11:00 (97%) LOW: Friday 17:00 (10%)

### Step 5: Analyze Facility Service Requests

Review facility management request patterns.

**MCP Approach:**

Tool: SN-Query-Table Parameters: tablename: fmfacilityrequest query: syscreatedon>=javascript:gs.daysAgo(30) fields: sysid,number,category,subcategory,priority,state,location,assignmentgroup,openedat,closedat,closecode limit: 500

**REST Approach:**

GET /api/now/table/fmfacilityrequest ?sysparmquery=syscreatedon>=javascript:gs.daysAgo(30) &sysparmfields=sysid,number,category,subcategory,priority,state,location,assignmentgroup,openedat,closedat,closecode &sysparmlimit=500 &sysparmdisplayvalue=true

### Step 6: Check SLA Compliance for Facility Services

Measure service delivery performance against SLA targets.

**MCP Approach:**

Tool: SN-Query-Table Parameters: tablename: tasksla query: task.sysclassname=fmfacilityrequest^syscreatedon>=javascript:gs.daysAgo(30) fields: sysid,task,sla,stage,hasbreached,plannedendtime,percentage,business_percentage limit: 500

Calculate SLA metrics:

| SLA Category | Total | Met | Breached | Compliance |
|-------------|-------|-----|----------|------------|
| Cleaning | [n] | [n] | [n] | [%] |
| Maintenance | [n] | [n] | [n] | [%] |
| HVAC | [n] | [n] | [n] | [%] |
| Security | [n] | [n] | [n] | [%] |

### Step 7: Analyze Workplace Incidents

Review facility-related incidents for recurring issues.

**MCP Approach:**

Tool: SN-Query-Table Parameters: tablename: incident query: category=facilities^syscreatedon>=javascript:gs.daysAgo(30) fields: sysid,number,shortdescription,category,subcategory,priority,state,location,assignmentgroup,openedat,resolvedat limit: 200

### Step 8: Generate Workplace Insights Report

Compile all analytics into an actionable insights report.

=== WORKPLACE SERVICE DELIVERY INSIGHTS === Report Period: [startdate] - [enddate] Scope: [Building/Campus/All Locations]

SPACE INVENTORY: Buildings: [count] | Floors: [count] | Total Spaces: [count] Desks: [count] | Meeting Rooms: [count] | Phone Booths: [count]

UTILIZATION SUMMARY: Overall Space Utilization: [%] Desks: [%] | Meeting Rooms: [%] | Phone Booths: [%] Peak Day: [day] ([%]) | Lowest Day: [day] ([%]) Peak Hour: [time] ([%]) | Lowest Hour: [time] ([%])

BOOKING ANALYTICS: Total Reservations (30 days): [count] Unique Users: [count] Avg Daily Bookings: [count] No-Show Rate: [%] ([count] no-shows) Avg Booking Duration: [hours] Most Popular Spaces: [list top 5] Least Used Spaces: [list bottom 5]

FACILITY SERVICE METRICS: Total Requests (30 days): [count] Open: [count] | In Progress: [count] | Closed: [count] Avg Resolution Time: [hours/days] SLA Compliance: [%]

TOP REQUEST CATEGORIES: | Category | Volume | Avg Resolution | SLA Met | |----------|--------|---------------|---------| | [category] | [count] | [time] | [%] |

FACILITY INCIDENTS: Total: [count] | Recurring: [count] Top Issues: [list]

RECOMMENDATIONS:

1. [Space optimization recommendation based on utilization data]
2. [No-show reduction strategy based on booking patterns]
3. [Service improvement recommendation based on SLA data]
4. [Capacity planning recommendation based on peak analysis]
5. [Cost optimization based on underutilized spaces]

### Step 9: Generate Desk Booking Analytics

Deep-dive into hot-desking and desk booking metrics.

**MCP Approach:**

Tool: SN-Query-Table Parameters: tablename: wsdreservation query: space.spacetype=desk^startdate>=javascript:gs.daysAgo(30) fields: sysid,space,user,startdate,state,no_show,space.floor,space.floor.building limit: 2000

=== DESK BOOKING ANALYTICS === Total Desk Reservations: [count] Unique Desk Users: [count] Avg Desks Booked/Day: [count] of [total] ([%])

USER PATTERNS:

• Regular Bookers (>3x/week): [count] users
• Occasional (1-2x/week): [count] users
• Rare (=javascript:gs.beginningOfLastMonth()^start_dateONLast month@javascript:gs.beginningOfLastMonth()@javascript:gs.endOfLastMonth()

fields: space,user,startdate,state,noshow,space.space_type,space.floor.building limit: 5000

**Output:**

MONTHLY WORKPLACE REPORT - February 2026 Headquarters Campus

Overall Utilization: 67% (up from 61% in January) Peak Day: Wednesday at 89% capacity Underutilized Floors: Building B, Floors 4-5 (32% avg)

RECOMMENDATION: Consolidate Building B floors 4-5 to reduce leased space by 15,000 sq ft. Estimated annual savings: $450K.

### Example 2: Facility Service Performance Review

**Scenario:** Generate quarterly facility service delivery scorecard.

FACILITY SERVICES SCORECARD - Q1 2026 Total Requests: 1,247 SLA Compliance: 94.2% (target: 95%)

BY CATEGORY:

• Cleaning: 412 requests, 97% SLA met
• Maintenance: 298 requests, 91% SLA met (BELOW TARGET)
• HVAC: 187 requests, 93% SLA met
• Security: 156 requests, 98% SLA met
• Other: 194 requests, 92% SLA met

ACTION: Maintenance SLA below target due to parts procurement delays. Recommend pre-stocking common replacement parts at each building.

## Related Skills

- `reporting/executive-dashboard` - Build executive dashboards for workplace metrics
- `reporting/trend-analysis` - Advanced trend analysis for facility data
- `reporting/sla-analysis` - Detailed SLA compliance analysis
- `admin/instance-management` - Instance configuration for WSD module

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [Happy-Technologies-LLC](https://github.com/Happy-Technologies-LLC)
- **Source:** [Happy-Technologies-LLC/happy-platform-skills](https://github.com/Happy-Technologies-LLC/happy-platform-skills)
- **License:** Apache-2.0

Install and usage instructions live in the source repository linked above.