Every key a library formula or an initiative template can read, grouped by the domain its key opens with. A key with a Fed by entry arrives from a connected system; the rest are recorded by a person, imported, or produced by a formula.

What the key's shape means, and where a metric's before-value comes from, is in Metric keys, and where a value comes from.

CRM

Key	What it measures	Type	Unit	Fed by
crm.contacts.lead_to_mql_rate	Percentage of new contacts that became Marketing Qualified Leads.	percentage	%	HubSpot
crm.contacts.mql_count	Contacts that reached MQL (Marketing Qualified Lead) status in the period.	count	leads	HubSpot
crm.contacts.mql_to_sql_rate	Percentage of the period's marketing-qualified contacts that also became sales-qualified.	percentage	%	HubSpot
crm.contacts.new_count	New contacts created in the CRM in the period.	count	contacts	HubSpot
crm.contacts.sql_count	Contacts that reached SQL (Sales Qualified Lead) status in the period.	count	leads	HubSpot
crm.contracts.avg_cycle_days	Average days from contract request to signature.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
crm.contracts.hours_per_contract	Combined legal, sales and operations hours to take one contract from request to signature.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
crm.contracts.processed_count	Contracts drafted, redlined and executed in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
crm.deals.at_risk_share	Percentage of open pipeline deals slipping or forecast to be lost.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
crm.deals.avg_demo_to_proposal_days	Average days from demo to proposal sent.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
crm.deals.avg_sales_cycle_days	Average days from deal creation to close across all closed deals in the period.	duration	days	HubSpot, Pipedrive, Salesforce
crm.deals.avg_value	Average closed-won deal size in the period.	currency	USD	HubSpot, Pipedrive, Salesforce
crm.deals.lost_count	Number of deals/opportunities closed-lost in the period.	count	deals	HubSpot, Pipedrive, Salesforce
crm.deals.pipeline_value	Value of the deals open in the period, taken as a snapshot when the CRM syncs.	currency	USD	HubSpot, Pipedrive, Salesforce
crm.deals.win_rate	Percentage of closed deals that were won. won_count / (won_count + lost_count).	percentage	%	HubSpot, Pipedrive, Salesforce
crm.deals.won_count	Number of deals/opportunities closed-won in the period.	count	deals	HubSpot, Pipedrive, Salesforce
crm.deals.won_value	Total value of all closed-won deals in the period.	currency	USD	HubSpot, Pipedrive, Salesforce
crm.leads.avg_lead_to_won_days	Average days from lead creation to closed-won.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
crm.leads.avg_qualify_minutes	Average staff minutes to qualify one inbound lead.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
crm.leads.qualification_rate	Percentage of scored leads that pass the qualification threshold.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
crm.leads.qualified_count	Leads that met the qualification threshold and were routed to a rep.	count	leads	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
crm.leads.scored_count	Inbound leads scored in the period.	count	leads	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
crm.leads.sql_rate	Sales-qualified leads in the period as a percentage of the new leads created in it.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
crm.proposals.hours_per_proposal	Staff hours to assemble and send one proposal.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
crm.proposals.sent_count	Proposals sent to prospects in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
crm.reps.admin_hours_per_week	Sales rep hours per week on administrative work rather than selling.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

HubSpot

Key	What it measures	Type	Unit	Fed by
hubspot.contacts.lead_to_mql_rate	HubSpot's lead-to-mql rate for the month. Written to crm.contacts.lead_to_mql_rate instead when this connection is the primary source.	percentage	%	HubSpot
hubspot.contacts.mql_count	HubSpot's marketing qualified leads for the month. Written to crm.contacts.mql_count instead when this connection is the primary source.	count	leads	HubSpot
hubspot.contacts.mql_to_sql_rate	HubSpot's mql-to-sql rate for the month. Written to crm.contacts.mql_to_sql_rate instead when this connection is the primary source.	percentage	%	HubSpot
hubspot.contacts.new_count	HubSpot's new contacts for the month. Written to crm.contacts.new_count instead when this connection is the primary source.	count	contacts	HubSpot
hubspot.contacts.sql_count	HubSpot's sales qualified leads for the month. Written to crm.contacts.sql_count instead when this connection is the primary source.	count	leads	HubSpot
hubspot.deals.avg_sales_cycle_days	HubSpot's avg sales cycle for the month. Written to crm.deals.avg_sales_cycle_days instead when this connection is the primary source.	duration	days	HubSpot
hubspot.deals.avg_value	HubSpot's average won deal size for the month. Written to crm.deals.avg_value instead when this connection is the primary source.	currency	USD	HubSpot
hubspot.deals.lost_count	HubSpot's lost deals for the month. Written to crm.deals.lost_count instead when this connection is the primary source.	count	deals	HubSpot
hubspot.deals.open_count	Deals open in HubSpot at sync time, a snapshot rather than a period total, written once per month and rewritten on every sync.	count	—	HubSpot
hubspot.deals.open_value	Value of the deals open in HubSpot at sync time, a snapshot rather than a period total, written once per month and rewritten on every sync.	currency	USD	HubSpot
hubspot.deals.win_rate	HubSpot's deal win rate for the month. Written to crm.deals.win_rate instead when this connection is the primary source.	percentage	%	HubSpot
hubspot.deals.won_count	HubSpot's won deals for the month. Written to crm.deals.won_count instead when this connection is the primary source.	count	deals	HubSpot
hubspot.deals.won_value	HubSpot's won deal value for the month. Written to crm.deals.won_value instead when this connection is the primary source.	currency	USD	HubSpot
hubspot.marketing.campaigns_launched	HubSpot's campaigns launched for the month. Written to marketing.campaigns.launched_count instead when this connection is the primary source.	count	campaigns	HubSpot
hubspot.marketing.email_open_rate	HubSpot's marketing email open rate for the month. Written to marketing.emails.open_rate instead when this connection is the primary source.	percentage	%	HubSpot
hubspot.marketing.organic_sessions	HubSpot's organic search sessions for the month. Written to marketing.web.organic_sessions instead when this connection is the primary source.	count	sessions	HubSpot
hubspot.marketing.posts_published	HubSpot's content published for the month. Written to marketing.content.published_count instead when this connection is the primary source.	count	posts	HubSpot
hubspot.tickets.avg_resolution_days	HubSpot's avg ticket resolution time for the month. Written to support.tickets.avg_resolution_days instead when this connection is the primary source.	duration	days	HubSpot
hubspot.tickets.closed_count	HubSpot's support tickets closed for the month. Written to support.tickets.closed_count instead when this connection is the primary source.	count	tickets	HubSpot
hubspot.tickets.new_count	HubSpot's support tickets opened for the month. Written to support.tickets.volume instead when this connection is the primary source.	count	tickets	HubSpot
hubspot.tickets.open_count	Tickets open in HubSpot at sync time, a snapshot rather than a period total, written once per month and rewritten on every sync.	count	—	HubSpot
hubspot.tickets.resolution_rate	HubSpot's ticket resolution rate for the month. Written to support.tickets.resolution_rate instead when this connection is the primary source.	percentage	%	HubSpot

Finance

Key	What it measures	Type	Unit	Fed by
finance.ap.data_entry_minutes_per_invoice	Staff minutes to key the data from one supplier invoice.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
finance.ap.days_payable_outstanding	Average days between invoice receipt and payment.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
finance.ap.invoices_processed	Supplier invoices processed through the extraction pipeline in the period.	count	invoices	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
finance.ap.minutes_per_invoice	Staff minutes to process one supplier invoice end to end.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
finance.bills.count	Total vendor bills received in the period.	count	bills	NetSuite, QuickBooks, Xero
finance.bills.error_rate	Percentage of vendor bills entered with an error that needed correcting — amount, coding, vendor or a duplicate.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
finance.bills.volume	Total value of all vendor bills received in the period.	currency	USD	NetSuite, QuickBooks, Xero
finance.close.cycle_days	Calendar days from period end to final sign-off of the close.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
finance.disputes.avg_resolution_days	Average days to resolve a billing dispute.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
finance.invoices.count	Total outbound invoices created in the period.	count	invoices	NetSuite, QuickBooks, Xero
finance.invoices.error_rate	Percentage of invoices issued with an error requiring correction.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
finance.invoices.paid_count	Number of invoices marked paid in the period.	count	invoices	NetSuite, QuickBooks, Xero
finance.invoices.volume	Total billed on invoices created in the period, tax included.	currency	USD	NetSuite, QuickBooks, Xero
finance.pnl.expenses	Total operating expenses for the period from the accounting system.	currency	USD	NetSuite, QuickBooks, Xero
finance.pnl.net_income	Net income (revenue minus expenses) for the period.	currency	USD	NetSuite, QuickBooks, Xero
finance.pnl.revenue	Total revenue recognized in the period from the accounting system.	currency	USD	NetSuite, QuickBooks, Xero
finance.reconciliation.hours	Staff hours on manual reconciliations per close cycle.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
finance.revenue_recognition.adjustment_count	Revenue recognition adjustments or corrections required per period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
finance.revenue_recognition.manual_hours	Staff hours per period building revenue recognition schedules by hand.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
finance.treasury.avg_cash_balance	Average daily cash balance in the period.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
finance.treasury.manual_hours_per_cycle	Staff hours to build and update the cash flow model per forecast cycle.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

Professional services

Key	What it measures	Type	Unit	Fed by
professional_services.billing.realization_rate	Billed fees as a percentage of standard-rate value of hours worked.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
professional_services.billing.unbilled_hours_per_practitioner_weekly	Billable hours worked but not captured per practitioner per week.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
professional_services.knowledge.active_users	Practitioners actively using the knowledge system in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
professional_services.knowledge.search_minutes	Average minutes per search for prior work and precedent.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
professional_services.knowledge.searches_per_practitioner	Times per period a practitioner searches for prior work, methods or precedent.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
professional_services.practitioners.available_hours	Working hours available per practitioner in the period.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
professional_services.practitioners.billable_count	Billable practitioners in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
professional_services.practitioners.billable_hours_worked	Hours worked by billable practitioners in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
professional_services.projects.at_risk_detection_lead_weeks	Average weeks before delivery failure that an at-risk project is identified.	duration	weeks	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
professional_services.projects.at_risk_identified_count	At-risk projects identified in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
professional_services.projects.budget_overrun_rate	Percentage of projects that exceeded the approved budget.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
professional_services.projects.pm_count_using_automation	Project managers using the automated reporting system.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
professional_services.projects.recovery_hours_per_project	Senior PM hours spent recovering a late-detected at-risk project.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
professional_services.proposals.prep_hours	Staff hours to assemble one proposal.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
professional_services.proposals.submitted_count	Proposals or statement-of-work responses submitted in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
professional_services.reporting.client_reports_delivered	Client status reports delivered in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
professional_services.reporting.hours_per_client_report	Staff hours to compile, format and deliver one client report.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
professional_services.reporting.reports_per_pm	Client reports each project manager prepares in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
professional_services.utilization.bench_share	Percentage of available practitioner hours spent on the bench.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
professional_services.utilization.billable_rate	Billable hours as a percentage of available practitioner hours.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

Construction

Key	What it measures	Type	Unit	Fed by
construction.bids.awarded_value	Contract value of projects awarded in the period.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
construction.bids.submitted_count	Bids submitted in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
construction.documents.search_hours_per_week	Staff hours per week locating current drawing versions and document history.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
construction.estimating.hours_per_bid	Estimator hours per bid.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
construction.estimating.variance_rate	Average estimate-to-actual variance as a percentage of the estimate.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
construction.job_costing.review_cycle_days	Average days between formal job cost reviews on active projects.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
construction.job_costing.review_cycles	Formal job cost review cycles in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
construction.job_costing.review_hours_per_project	PM and controller hours per project per job cost review cycle.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
construction.labor.hours_worked	Field labor hours worked in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
construction.payroll.processing_hours_per_week	Staff hours per week collecting, reconciling and processing timecards.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
construction.payroll.timecard_error_rate	Percentage of timecards requiring correction.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
construction.payroll.timecards_processed	Timecards processed in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
construction.projects.active_count	Active projects in the portfolio during the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
construction.projects.cost_overrun_rate	Percentage of projects that exceed the approved budget.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
construction.rfis.response_days	Average calendar days from RFI submission to formal response.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
construction.rfis.submitted_count	Requests for information submitted in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
construction.safety.observation_hours_per_week	Staff hours per week on safety observation reporting and job hazard analysis distribution.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
construction.safety.recordable_incident_rate	OSHA recordable incident rate (incidents × 200,000 ÷ hours worked).	ratio	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

Healthcare

Key	What it measures	Type	Unit	Fed by
healthcare.appointments.avg_length	Scheduled minutes of a patient visit of the type recovered clinician time would be booked as.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
healthcare.appointments.no_show_rate	Percentage of scheduled appointments the patient did not attend.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
healthcare.appointments.scheduled_count	Appointments scheduled in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
healthcare.audits.prep_hours_per_cycle	Staff hours preparing documentation for one quality audit or regulatory review.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
healthcare.claims.denial_rate	Percentage of submitted claims denied on first submission.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
healthcare.claims.denied_count	Claims denied on first submission in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
healthcare.coding.charts_coded	Charts coded in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
healthcare.coding.charts_per_coder_per_day	Charts coded per medical coder per day.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
healthcare.coding.coder_count	Medical coders using AI-assisted coding.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
healthcare.coding.error_rate	Percentage of coded charts with an error.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
healthcare.denials.appeal_success_rate	Share of appealed denials overturned.	ratio	0–1	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
healthcare.documentation.after_hours_charting_hours_per_week	Hours per week clinicians spend on documentation outside clinical hours.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
healthcare.documentation.clinician_count	Clinicians using ambient documentation.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
healthcare.documentation.encounters_per_clinician_per_day	Patient encounters per clinician per clinical day.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
healthcare.documentation.minutes_per_encounter	Clinician documentation minutes per patient encounter.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
healthcare.quality.care_gap_closure_rate	Percentage of identified care gaps closed.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
healthcare.quality.value_based_revenue_at_risk	Revenue at risk or bonus-eligible under value-based contracts, for a performance year.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
healthcare.revenue_cycle.days_in_ar	Average days from claim submission to payment.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

HR

Key	What it measures	Type	Unit	Fed by
hr.hiring.days_to_first_interview	Average days from job posting to first interview.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hr.hiring.hires_count	Positions filled in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hr.hiring.recruiter_hours_per_hire	Recruiter hours on resume review and screening per hire.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hr.inquiries.avg_response_hours	Average hours from employee inquiry to HR response for routine questions.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hr.inquiries.chatbot_deflection_rate	Share of employee inquiries fully resolved by the chatbot without HR involvement.	ratio	0–1	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hr.inquiries.tier1_per_month	Routine HR inquiries received per month (policy, PTO, benefits, payroll).	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hr.onboarding.admin_hours_per_hire	HR and IT staff hours on onboarding tasks per new hire.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hr.onboarding.completion_rate	Percentage of required onboarding tasks completed within the target window.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hr.onboarding.days_to_productivity	Days from start date until a new hire is independently productive.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hr.onboarding.hires_count	New employees who completed onboarding in the period.	count	hires	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hr.onboarding.tasks_automated	Onboarding checklist items (provisioning, welcome communications, acknowledgments) now handled automatically.	count	tasks	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hr.reporting.hours_per_month	Staff hours per month building workforce reports.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hr.reviews.admin_hours_per_cycle	HR and manager hours coordinating, collecting and calibrating one performance review cycle.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hr.reviews.admin_hours_per_employee	HR and manager admin hours per employee per review cycle.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hr.reviews.completion_rate	Percentage of required performance reviews completed on time.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hr.reviews.employees_reviewed	Employees included in the performance review cycle.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hr.workforce.headcount	Total employee headcount in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hr.workforce.voluntary_attrition_rate	Annualized voluntary attrition rate.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

Supply chain

Key	What it measures	Type	Unit	Fed by
supply_chain.deliveries.on_time_rate	Percentage of orders delivered on or before the promised date.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
supply_chain.deliveries.revenue_at_risk	Revenue exposed to late deliveries in the period through penalties, cancellations and credits.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
supply_chain.forecasting.accuracy	Demand forecast accuracy against actual demand.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
supply_chain.inventory.avg_value	Average inventory value on hand during the period.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
supply_chain.inventory.excess_value	Value of inventory held above target levels.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
supply_chain.inventory.stockout_events	Stockout incidents in the period.	count	events	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
supply_chain.inventory.stockout_rate	Percentage of SKUs or order lines that were out of stock.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
supply_chain.procurement.hours_per_po	Staff hours per purchase-order cycle, from creation through invoice matching.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
supply_chain.procurement.invoice_matching_hours_per_week	AP staff hours per week matching purchase orders to invoices.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
supply_chain.procurement.po_cycle_days	Average calendar days from purchase requisition to approved purchase order.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
supply_chain.procurement.purchase_orders_processed	Purchase orders processed in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
supply_chain.shipments.cost_per_shipment	Average total cost per shipment.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
supply_chain.shipments.count	Outbound shipments in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
supply_chain.suppliers.disruptions_per_year	Supply disruptions per year.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
supply_chain.suppliers.review_hours_per_month	Staff hours per month on supplier health reviews and risk assessments.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
supply_chain.warehouse.pick_error_rate	Percentage of picks resulting in an error.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
supply_chain.warehouse.picks_count	Units picked in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
supply_chain.warehouse.picks_per_labor_hour	Units picked per warehouse labor hour.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

Support

Key	What it measures	Type	Unit	Fed by
support.calls.avg_handle_time	Average minutes an agent spends on one inbound phone call — talk, hold and wrap-up.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
support.knowledge_base.article_count	Knowledge base articles being maintained.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
support.knowledge_base.avg_article_age_days	Average days since knowledge base articles were last reviewed or updated.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
support.knowledge_base.update_minutes_per_article	Minutes to review and update one knowledge base article per cycle.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
support.quality.review_minutes_per_interaction	Minutes for a QA analyst to review and score one interaction.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
support.quality.sample_rate	Share of support interactions reviewed for quality.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
support.tickets.ai_resolution_rate	Percentage of the tickets resolved in the period that AI resolved, without human escalation.	percentage	%	Freshdesk, Intercom, Zendesk
support.tickets.ai_resolved_count	Support tickets fully resolved by AI without human escalation in the period.	count	tickets	Freshdesk, Intercom, Zendesk
support.tickets.avg_first_response_hours	Average hours from ticket creation to the first agent reply, across tickets opened in the period.	duration	hours	Freshdesk, Intercom, Zendesk
support.tickets.avg_handle_time	Average time agents spent handling each ticket in the period.	duration	hours	Freshdesk, Zendesk
support.tickets.avg_resolution_days	Average days from ticket creation to resolution (Intercom close time ÷ 24).	duration	days	HubSpot, Intercom, Salesforce
support.tickets.closed_count	Total support tickets closed/resolved in the period.	count	tickets	Freshdesk, HubSpot, Intercom, Salesforce, Zendesk
support.tickets.cost_per_ticket	Blended cost to resolve one support ticket (agent labor plus tooling).	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
support.tickets.csat_score	Average customer satisfaction on closed tickets, normalized to 0–1 (Zendesk good/bad → 0/1; Intercom 1–5 → (rating − 1) ÷ 4).	score	0–1	Intercom, Zendesk
support.tickets.deflected_count	Tickets resolved by self-service or AI without an agent in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
support.tickets.resolution_rate	Percentage of tickets opened in the period that were also closed.	percentage	%	HubSpot, Salesforce
support.tickets.self_serve_deflection_rate	Percentage of support inquiries resolved without agent involvement.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
support.tickets.volume	Total support tickets opened in the period (help-desk tickets, Intercom conversations, Salesforce cases).	count	tickets	Freshdesk, HubSpot, Intercom, Salesforce, Zendesk

Financial services

Key	What it measures	Type	Unit	Fed by
financial_services.advisors.accounts_per_advisor	Client accounts managed per advisor.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
financial_services.advisors.aum_per_advisor	Average assets under management per advisor.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
financial_services.advisors.count	Advisors using the client intelligence platform.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
financial_services.advisors.proactive_outreach_rate	Percentage of clients contacted proactively per quarter.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
financial_services.fraud.false_positive_rate	Percentage of flagged transactions that were legitimate.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
financial_services.fraud.flagged_count	Transactions flagged by the fraud system in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
financial_services.fraud.loss_rate_bps	Fraud losses as basis points of transaction volume.	count	bps	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
financial_services.kyc.review_hours_per_account	Staff hours to review and approve one account.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
financial_services.onboarding.accounts_count	New accounts onboarded through KYC/AML in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
financial_services.onboarding.days_to_active	Average days from application to active account.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
financial_services.regulatory_reporting.error_rate	Percentage of regulatory report line items requiring correction before submission.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
financial_services.regulatory_reporting.line_items_per_cycle	Line items across the regulatory reports filed in one reporting cycle.	average	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
financial_services.regulatory_reporting.prep_hours_per_report	Staff hours to prepare and validate one regulatory report.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
financial_services.transactions.volume	Total transaction volume processed in the period.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
financial_services.underwriting.decision_hours	Average hours from application submission to decision.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
financial_services.underwriting.manual_review_rate	Percentage of applications requiring manual underwriter review.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
financial_services.underwriting.review_hours_per_application	Average underwriter and analyst hours of hands-on review per application decided, an automatically decided one counting as none — not the elapsed time to a decision.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

Real estate

Key	What it measures	Type	Unit	Fed by
real_estate.leasing.annual_renewal_rate	Percentage of expiring leases renewed.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
real_estate.leasing.days_to_lease	Average days from inquiry to signed lease.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
real_estate.leasing.staff_hours_per_lease	Leasing staff hours per signed lease (lead response, tours, application processing).	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
real_estate.leasing.units_leased	Units signed to new leases in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
real_estate.leasing.units_up_for_renewal	Units with lease expirations in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
real_estate.maintenance.emergency_repairs_per_month	Unplanned emergency repair incidents per month.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
real_estate.portfolio.aum	Portfolio value under management at the time of measurement.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
real_estate.portfolio.property_count	Properties in the portfolio.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
real_estate.portfolio.unit_count	Residential or commercial units in the portfolio.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
real_estate.reporting.hours_per_property_monthly	Staff hours per property per month compiling performance and financial reports.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
real_estate.reporting.review_cycle_days	Calendar days between formal portfolio performance reviews.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
real_estate.tenants.contacts_per_unit_monthly	Inbound phone and email contacts per unit per month requiring staff handling.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
real_estate.tenants.minutes_per_contact	Staff minutes to handle one inbound tenant contact.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
real_estate.tenants.satisfaction_score	Average tenant satisfaction score from periodic surveys.	score	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
real_estate.work_orders.admin_minutes_per_order	Staff minutes per work order on creation, vendor calls and status follow-ups.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
real_estate.work_orders.created_count	Work orders created in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
real_estate.work_orders.resolution_days	Average days from work order submission to resolution.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

Marketing

Key	What it measures	Type	Unit	Fed by
marketing.budget.destination_pipeline_per_dollar	Pipeline created per dollar of spend in the channels budget was moved to, from the attribution model.	ratio	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
marketing.budget.reallocated	The period's marketing spend that went to the channels attribution favored instead of the channels it was taken from.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
marketing.budget.source_pipeline_per_dollar	Pipeline created per dollar of spend in the channels budget was taken from, from the attribution model.	ratio	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
marketing.campaigns.avg_list_size	Average recipients per campaign send.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
marketing.campaigns.conversion_rate	Percentage of campaign recipients who completed the campaign's goal.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
marketing.campaigns.hours_per_launch	Staff hours to configure and launch one campaign.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
marketing.campaigns.launched_count	Marketing campaigns with a start date in the period.	count	campaigns	HubSpot
marketing.content.hours_per_piece	Staff hours from brief to published asset.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
marketing.content.pieces_per_month	Content pieces produced per month.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
marketing.content.published_count	Blog posts published in the period on the connected CMS.	count	posts	HubSpot
marketing.emails.open_rate	Opens as a percentage of deliveries across marketing emails published in the period.	percentage	%	HubSpot
marketing.nurture.manual_hours_per_week	Staff hours per week on lead follow-up and segmentation done by hand.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
marketing.pipeline.sourced_share	Percentage of total sales pipeline attributed to marketing-sourced leads.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
marketing.reporting.hours_per_week	Staff hours per week on marketing performance reports.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
marketing.seo.research_hours_per_month	Staff hours per month on keyword research and on-page optimization.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
marketing.web.organic_sessions	Website sessions from organic search in the period.	count	sessions	HubSpot

Anthropic

Key	What it measures	Type	Unit	Fed by
anthropic.claude_code.acceptance_rate	Accepted tool actions as a share of those proposed in Anthropic's Claude Code report for the month. Read as engineering.coding_assistant.acceptance_rate when Anthropic is the primary coding-assistant source.	percentage	%	Anthropic
anthropic.claude_code.acceptances	Tool actions developers accepted in Anthropic's Claude Code report for the month — one accepted edit or write each, not a bundled activity as GitHub counts them. Read as engineering.coding_assistant.acceptances when Anthropic is the primary coding-assistant source.	count	acceptances	Anthropic
anthropic.claude_code.active_users_avg	Developers who ran Claude Code on an average day in the month. Read as engineering.coding_assistant.active_users when Anthropic is the primary coding-assistant source.	average	users	Anthropic
anthropic.claude_code.lines_accepted	Lines of code Claude Code added in the month. Read as engineering.coding_assistant.lines_accepted when Anthropic is the primary coding-assistant source.	count	lines	Anthropic
anthropic.claude_code.proposals	Tool actions Claude Code proposed in the month, accepted and rejected together. Read as engineering.coding_assistant.suggestions when Anthropic is the primary coding-assistant source.	count	suggestions	Anthropic
anthropic.usage.cost_per_million_output_tokens	Anthropic's spend for the month over the output tokens it generated, per million. Written to ai.cost.per_million_output_tokens instead when this connection is the primary source for AI usage.	currency	USD	Anthropic
anthropic.usage.input_tokens	Anthropic's ai input tokens for the month. Written to ai.usage.input_tokens instead when this connection is the primary source.	count	tokens	Anthropic
anthropic.usage.model_input_tokens	Input tokens sent to one Claude model in the month; the model is in the observation's dimensions.	count	tokens	Anthropic
anthropic.usage.model_output_tokens	Output tokens generated by one Claude model in the month; the model is in the observation's dimensions.	count	tokens	Anthropic
anthropic.usage.model_spend	Anthropic API spend on one model for the month; the model is in the observation's dimensions.	currency	USD	Anthropic
anthropic.usage.model_total_tokens	Input plus output tokens for one Claude model in the month; the model is in the observation's dimensions.	count	tokens	Anthropic
anthropic.usage.output_tokens	Anthropic's ai output tokens for the month. Written to ai.usage.output_tokens instead when this connection is the primary source.	count	tokens	Anthropic
anthropic.usage.spend	Anthropic's usage spend for the month.	currency	USD	Anthropic
anthropic.usage.total_tokens	Anthropic's ai total tokens for the month. Written to ai.usage.total_tokens instead when this connection is the primary source.	count	tokens	Anthropic

IT

Key	What it measures	Type	Unit	Fed by
it.audits.hours_per_cycle	IT staff hours per software inventory and license reconciliation audit cycle.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
it.deployments.change_failure_rate	Percentage of deployments causing a rollback or incident.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
it.deployments.count	Production deployments in the period.	count	—	GitHub
it.deployments.hours_per_deployment	Engineer hours per production deployment, including manual steps, monitoring and validation.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
it.deployments.per_week	Production deployments per week.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
it.helpdesk.avg_users_per_incident	End-users affected or blocked by a typical IT incident.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
it.helpdesk.mean_time_to_resolution_hours	Mean hours to resolve a tier-1 IT issue.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
it.helpdesk.tickets_per_month	IT helpdesk tickets per month requiring agent handling.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
it.security.incident_count	Security incidents investigated in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
it.security.incidents_per_year	Security incidents per year.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
it.security.mean_time_to_contain_hours	Mean hours from detection of a security incident to its containment.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
it.security.mean_time_to_detect_hours	Mean hours from a security incident starting to its detection.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
it.software.annual_spend	Total annual software license spend.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
it.software.unused_license_share	Percentage of purchased licenses that are unassigned or unused.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

Legal

Key	What it measures	Type	Unit	Fed by
legal.compliance.change_response_days	Days from a regulatory change being published to controls updated and staff notified.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
legal.compliance.gaps_prevented	Compliance control gaps identified and closed before becoming findings, in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
legal.compliance.monitoring_hours_per_week	Staff hours per week tracking regulatory publications and updates.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
legal.contracts.missed_renewals_per_year	Contracts that auto-renewed or lapsed unintentionally per year.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
legal.contracts.search_hours_per_week	Staff hours per week locating and retrieving contracts.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
legal.matters.closed_count	Legal matters handled to completion in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
legal.matters.research_hours_per_matter	Legal research hours per matter.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
legal.outside_counsel.monthly_spend	Monthly spend on outside counsel for matters that could be handled in-house.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
legal.policies.attestation_completion_rate	Percentage of employees who complete required policy attestations by the deadline.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
legal.training.admin_hours_per_cycle	Staff hours per compliance training cycle on enrollment, reminders, tracking and attestation.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
legal.vendors.onboarded_count	New vendors onboarded through due diligence in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
legal.vendors.onboarding_days	Average days from vendor selection to fully approved and active.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
legal.vendors.portfolio_count	Active vendors in the third-party risk portfolio.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
legal.vendors.review_hours_per_vendor	Staff hours per vendor for a periodic risk reassessment.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

Manufacturing

Key	What it measures	Type	Unit	Fed by
manufacturing.data.reconciliation_hours_per_week	Staff hours per week reconciling data between shop floor systems and the ERP.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
manufacturing.equipment.oee	Overall equipment effectiveness.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
manufacturing.equipment.oee_reporting_hours_per_week	Staff hours per week collecting and compiling OEE data.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
manufacturing.equipment.unplanned_downtime_hours_per_month	Unplanned equipment downtime hours per month.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
manufacturing.maintenance.annual_cost_per_asset	Average annual maintenance cost per asset including reactive repairs and planned servicing.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
manufacturing.maintenance.annual_spend	Annual maintenance spend across monitored assets, planned and reactive.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
manufacturing.maintenance.cost_reduction_rate	Percentage reduction in total maintenance spend from condition-based servicing.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
manufacturing.materials.shortage_incidents_per_month	Production stoppages caused by material shortages per month.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
manufacturing.production.theoretical_capacity_units	Units producible in the period at nameplate capacity.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
manufacturing.production.units_produced	Units produced in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
manufacturing.quality.defect_escape_rate	Percentage of produced units with a defect that reaches the customer.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
manufacturing.quality.first_pass_yield	Percentage of units completing production without defect, rework or scrap.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
manufacturing.quality.scrap_cost_per_month	Monthly scrap and rework cost.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
manufacturing.scheduling.hours_per_week	Staff hours per week building and adjusting production schedules.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

Insurance

Key	What it measures	Type	Unit	Fed by
insurance.claims.adjuster_hours_per_claim	Average adjuster hours of hands-on work per claim closed, a straight-through claim counting as none — not the days from first notice of loss to close.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
insurance.claims.auto_adjudication_rate	Share of claims processed straight-through without adjuster review.	ratio	0–1	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
insurance.claims.avg_cycle_days	Average days from first notice of loss to claim closure.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
insurance.claims.incurred_losses	Incurred losses on the affected book in the period.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
insurance.claims.lae_ratio	Loss adjustment expense as a percentage of incurred losses.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
insurance.claims.leakage_rate	Paid loss attributable to fraud or excess adjustments as a percentage of claims paid.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
insurance.claims.processed_count	Claims submitted and processed in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
insurance.fraud.false_positive_siu_referrals	Special investigations unit referrals per month that do not substantiate fraud.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
insurance.policy_servicing.calls_per_month	Inbound policy-servicing call volume per month.	count	calls	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
insurance.underwriting.earned_premium	Earned premium on the targeted book in the period.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
insurance.underwriting.loss_ratio	Incurred losses as a percentage of earned premium.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
insurance.underwriting.quote_hours	Average underwriter hours from application to bindable quote.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
insurance.underwriting.submission_count	New business submissions reviewed in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

Salesforce

Key	What it measures	Type	Unit	Fed by
salesforce.cases.avg_resolution_days	Salesforce's avg ticket resolution time for the month. Written to support.tickets.avg_resolution_days instead when this connection is the primary source.	duration	days	Salesforce
salesforce.cases.closed_count	Salesforce's support tickets closed for the month. Written to support.tickets.closed_count instead when this connection is the primary source.	count	tickets	Salesforce
salesforce.cases.open_count	Cases open in Salesforce at sync time, a snapshot rather than a period total, written once per month and rewritten on every sync.	count	—	Salesforce
salesforce.cases.resolution_rate	Salesforce's ticket resolution rate for the month. Written to support.tickets.resolution_rate instead when this connection is the primary source.	percentage	%	Salesforce
salesforce.cases.volume	Salesforce's support tickets opened for the month. Written to support.tickets.volume instead when this connection is the primary source.	count	tickets	Salesforce
salesforce.opportunities.avg_sales_cycle_days	Salesforce's avg sales cycle for the month. Written to crm.deals.avg_sales_cycle_days instead when this connection is the primary source.	duration	days	Salesforce
salesforce.opportunities.avg_value	Salesforce's average won opportunity size for the month. Written to crm.deals.avg_value instead when this connection is the primary source.	currency	USD	Salesforce
salesforce.opportunities.lost_count	Salesforce's lost deals for the month. Written to crm.deals.lost_count instead when this connection is the primary source.	count	deals	Salesforce
salesforce.opportunities.open_count	Opportunities open in Salesforce at sync time, a snapshot rather than a period total, written once per month and rewritten on every sync.	count	—	Salesforce
salesforce.opportunities.open_value	Value of the opportunities open in Salesforce at sync time, a snapshot rather than a period total, written once per month and rewritten on every sync.	currency	USD	Salesforce
salesforce.opportunities.win_rate	Salesforce's deal win rate for the month. Written to crm.deals.win_rate instead when this connection is the primary source.	percentage	%	Salesforce
salesforce.opportunities.won_count	Salesforce's won deals for the month. Written to crm.deals.won_count instead when this connection is the primary source.	count	deals	Salesforce
salesforce.opportunities.won_value	Salesforce's won deal value for the month. Written to crm.deals.won_value instead when this connection is the primary source.	currency	USD	Salesforce

GitHub

Key	What it measures	Type	Unit	Fed by
github.copilot.acceptance_rate	Accepted code activities as a share of code generation activities in GitHub's Copilot organization report for the month. Read as engineering.coding_assistant.acceptance_rate when GitHub is the primary coding-assistant source.	percentage	%	GitHub
github.copilot.active_users_avg	Daily active Copilot users in GitHub's organization report, averaged over the month. Read as engineering.coding_assistant.active_users when GitHub is the primary coding-assistant source.	average	—	GitHub
github.copilot.lines_accepted	Lines of code accepted in GitHub's Copilot organization report for the month. Read as engineering.coding_assistant.lines_accepted when GitHub is the primary coding-assistant source.	count	—	GitHub
github.copilot.suggestions_accepted	Code acceptance activities in GitHub's Copilot organization report for the month. Read as engineering.coding_assistant.acceptances when GitHub is the primary coding-assistant source.	count	—	GitHub
github.copilot.suggestions_shown	Code generation activities in GitHub's Copilot organization report for the month. Read as engineering.coding_assistant.suggestions when GitHub is the primary coding-assistant source.	count	suggestions	GitHub
github.cost.copilot_spend	GitHub's Copilot and AI-credit net spend for the month. Written as ai.cost.spend when this connection is the primary AI-cost source.	currency	USD	GitHub
github.cost.product_spend	GitHub spend on one product (Actions, Copilot, Packages, …) for the month; the product is in the observation's dimensions.	currency	USD	GitHub
github.cost.total_spend	GitHub's cost total spend for the month.	currency	USD	GitHub
github.deployments.count	Deployments GitHub recorded to a production environment in the month. Written to it.deployments.count instead when this connection is the primary source for deployments.	count	deployments	GitHub
github.pull_requests.additions_avg	GitHub's pull requests additions avg for the month.	average	—	GitHub
github.pull_requests.cycle_time_avg	GitHub's pull requests cycle time avg for the month.	duration	—	GitHub
github.pull_requests.merged_count	GitHub's pull requests merged count for the month.	count	—	GitHub

Hospitality

Key	What it measures	Type	Unit	Fed by
hospitality.bookings.direct_share	Share of bookings made through direct channels.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hospitality.food_beverage.cost_of_goods	Food and beverage cost of goods in the period.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hospitality.food_beverage.prep_hours	Kitchen prep hours in the period.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hospitality.food_beverage.waste_share	Food cost lost to waste and spoilage as a percentage of food and beverage cost of goods.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hospitality.guests.ancillary_spend_per_guest	Average ancillary spend per guest.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hospitality.guests.count	Guests in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hospitality.housekeeping.minutes_per_room_turn	Housekeeping labor minutes per room turn.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hospitality.housekeeping.rooms_turned	Room turns (checkouts and cleans) in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hospitality.revenue_management.hours_per_week	Revenue manager hours per week on rate setting and reporting.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hospitality.rooms.available_room_nights	Available room-nights in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hospitality.rooms.revenue	Total room revenue across all channels in the period.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
hospitality.rooms.revpar	Revenue per available room.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

Operations

Key	What it measures	Type	Unit	Fed by
operations.applications.processed_count	Applications reviewed and decided in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
operations.documents.data_entry_minutes_per_document	Staff minutes to key the data from one document.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
operations.documents.extraction_accuracy	Percentage of extracted fields that are correct without human correction.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
operations.documents.processed_count	Documents (PDFs, forms, emails) processed by the extraction pipeline in the period.	count	documents	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
operations.forecasting.accuracy	Accuracy of the operating forecast (cash flow, demand or pipeline) against actuals.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
operations.forecasting.cycles	Forecast cycles run in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
operations.forecasting.hours_per_cycle	Staff hours to produce one complete forecast cycle.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
operations.processing.error_rate	Percentage of processed items that contained an error or needed manual correction.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
operations.reporting.delivery_lag_days	Days from period close to delivery of the period's report.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
operations.reporting.distribution_lag_hours	Hours between data availability and report delivery to stakeholders.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
operations.reporting.hours_per_week	Staff hours per week spent compiling and distributing reports by hand.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
operations.reporting.reports_automated	Distinct report types fully automated and removed from manual production.	count	reports	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

Retail

Key	What it measures	Type	Unit	Fed by
retail.customers.active_count	Active customers in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
retail.customers.repeat_purchase_rate	Percentage of customers making a second purchase within twelve months.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
retail.customers.retention_rate	Twelve-month customer retention rate.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
retail.inventory.accuracy	Percentage of inventory records matching physical stock.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
retail.inventory.markdown_eligible_sold_value	Full retail value of the seasonal or clearance-eligible stock that sold or cleared in the period, at target price or marked down.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
retail.inventory.sell_through_rate	Of the seasonal or clearance-eligible stock that sold or cleared in the period, the percentage by retail value that sold at or above target price.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
retail.orders.avg_value	Average order value.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
retail.orders.count	Orders placed in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
retail.pricing.gross_margin	Gross margin as a percentage of revenue.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
retail.pricing.markdown_rate	Percentage of the period's sales, by full retail value, made at a markdown price.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
retail.web.conversion_rate	Orders as a percentage of website sessions.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
retail.web.session_count	Website sessions in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

Education

Key	What it measures	Type	Unit	Fed by
education.admissions.admitted_count	Applicants offered admission in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
education.admissions.applicant_count	Applicants in the enrollment cycle.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
education.admissions.decision_days	Average days from application received to decision.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
education.admissions.review_minutes_per_application	Average staff minutes of hands-on work to review and process one application — not the days it waits for a decision.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
education.admissions.yield_rate	Percentage of admitted students who enroll.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
education.courses.dfw_rate	Percentage of students earning a D or F or withdrawing.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
education.enrollment.retention_rate	Year-over-year student retention rate.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
education.enrollment.student_count	Students enrolled in the affected courses or programs.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
education.faculty.admin_hours_per_week	Faculty hours per week on course administration.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
education.faculty.count	Faculty using AI course-operations tools.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
education.tutoring.cost_per_student	Cost of tutoring services per enrolled student in the period.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

Energy

Key	What it measures	Type	Unit	Fed by
energy.assets.deferred_capex	Asset replacement spend deferred by extending asset life.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
energy.assets.unplanned_outages_per_year	Unplanned critical-asset outage events per year.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
energy.grid.reserve_capacity_mw	Reserve generation capacity held, in megawatts.	count	MW	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
energy.load_forecasting.mape	Mean absolute percentage error of the load forecast.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
energy.rate_cases.count	Rate cases and major regulatory filings prepared in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
energy.rate_cases.external_counsel_hours	Outside counsel hours billed for a rate case or major regulatory filing.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
energy.rate_cases.prep_hours	Internal staff hours to prepare a rate case or major regulatory filing.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
energy.reliability.saidi_minutes	System Average Interruption Duration Index, in outage minutes per customer per year.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
energy.storms.call_volume_index	Inbound call volume during a typical major storm event.	count	index	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
energy.storms.events_per_year	Major storm or high-call-volume outage events per year.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
energy.storms.notification_hours_per_event	Staff hours spent communicating outage status to customers during one major storm event.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

OpenAI

Key	What it measures	Type	Unit	Fed by
openai.usage.cost_per_million_output_tokens	OpenAI's spend for the month over the output tokens it generated, per million. Written to ai.cost.per_million_output_tokens instead when this connection is the primary source for AI usage.	currency	USD	OpenAI
openai.usage.cost_per_request	OpenAI's spend for the month over the requests it served. Written to ai.cost.per_request instead when this connection is the primary source for AI usage.	currency	USD	OpenAI
openai.usage.input_tokens	OpenAI's ai input tokens for the month. Written to ai.usage.input_tokens instead when this connection is the primary source.	count	tokens	OpenAI
openai.usage.model_input_tokens	Input tokens sent to one OpenAI model in the month; the model is in the observation's dimensions.	count	tokens	OpenAI
openai.usage.model_output_tokens	Output tokens generated by one OpenAI model in the month; the model is in the observation's dimensions.	count	tokens	OpenAI
openai.usage.model_request_count	API requests to one OpenAI model in the month; the model is in the observation's dimensions.	count	requests	OpenAI
openai.usage.model_total_tokens	Input plus output tokens for one OpenAI model in the month; the model is in the observation's dimensions.	count	tokens	OpenAI
openai.usage.output_tokens	OpenAI's ai output tokens for the month. Written to ai.usage.output_tokens instead when this connection is the primary source.	count	tokens	OpenAI
openai.usage.request_count	OpenAI's ai api requests for the month. Written to ai.usage.request_count instead when this connection is the primary source.	count	requests	OpenAI
openai.usage.spend	OpenAI's usage spend for the month.	currency	USD	OpenAI
openai.usage.total_tokens	OpenAI's ai total tokens for the month. Written to ai.usage.total_tokens instead when this connection is the primary source.	count	tokens	OpenAI

Logistics

Key	What it measures	Type	Unit	Fed by
logistics.customer_service.wismo_contacts_per_month	Where-is-my-order inbound contacts per month.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
logistics.deliveries.on_time_rate	Percentage of deliveries completed within the promised window.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
logistics.drivers.count	Last-mile drivers in the measured operation.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
logistics.drivers.operating_hours	Driver operating hours in the period.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
logistics.drivers.stops_per_hour	Completed delivery stops per driver per labor hour.	count	stops	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
logistics.fleet.annual_insurance_premium	Annual commercial auto and cargo insurance premium across the fleet.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
logistics.fleet.maintenance_hours_per_month	Technician hours per month on scheduled and reactive fleet maintenance.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
logistics.fleet.miles	Total fleet miles in the period.	count	miles	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
logistics.fleet.roadside_events_per_year	Unplanned roadside breakdown events per year.	count	events	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
logistics.safety.preventable_accidents_per_million_miles	Preventable accident frequency per million miles driven.	count	accidents	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

Media

Key	What it measures	Type	Unit	Fed by
media.audience.avg_active_users	Average monthly active users on the service.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
media.audience.monthly_watch_hours_per_user	Average monthly watch hours per active user.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
media.localization.cost_per_episode	Average cost to localize one episode (dubbing, subtitling and QC).	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
media.localization.episodes_localized	Episodes localized into additional languages in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
media.post_production.days_per_episode	Days from picture lock to delivery per episode.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
media.post_production.episode_count	Episodes entering post-production in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
media.rights.admin_hours_per_week	Staff hours per week on rights tracking, metadata correction and catalog reconciliation.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
media.royalties.dispute_rate	Share of royalty statements requiring dispute or correction.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
media.royalties.statement_count	Royalty statements issued in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

AI

Key	What it measures	Type	Unit	Fed by
ai.cost.per_million_output_tokens	All-in AI spend per million output tokens: everything the provider billed in the period (input, cache and output charges) divided by the output tokens it generated. The unit of AI Spend Optimization for a provider that reports tokens but no request count, such as Anthropic.	currency	USD	Anthropic, OpenAI
ai.cost.per_request	All-in AI spend per API request: everything the provider billed in the period divided by the requests made to it. The unit of AI Spend Optimization for a provider that reports request counts, such as OpenAI or Gemini.	currency	USD	OpenAI
ai.cost.spend	Total spend on the primary AI provider in the period.	currency	USD	Anthropic, GitHub, OpenAI
ai.outputs.human_review_rate	Percentage of AI outputs routed to a human reviewer before being acted on.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
ai.usage.input_tokens	Total prompt/input tokens consumed across the primary AI provider in the period.	count	tokens	Anthropic, Google Gemini, OpenAI
ai.usage.output_tokens	Total completion/output tokens generated by the primary AI provider in the period.	count	tokens	Anthropic, Google Gemini, OpenAI
ai.usage.request_count	Number of API requests made to the primary AI provider in the period. OpenAI and Gemini report it; Anthropic reports tokens but no request count.	count	requests	Google Gemini, OpenAI
ai.usage.total_tokens	Total tokens (input + output) used through the primary AI provider in the period.	count	tokens	Anthropic, Google Gemini, OpenAI

Google Gemini

Key	What it measures	Type	Unit	Fed by
gemini.usage.input_tokens	Google Gemini's ai input tokens for the month. Written to ai.usage.input_tokens instead when this connection is the primary source.	count	tokens	Google Gemini
gemini.usage.model_input_tokens	Input tokens sent to one Gemini model in the month; the model is in the observation's dimensions.	count	tokens	Google Gemini
gemini.usage.model_output_tokens	Output tokens generated by one Gemini model in the month; the model is in the observation's dimensions.	count	tokens	Google Gemini
gemini.usage.model_request_count	API requests to one Gemini model in the month; the model is in the observation's dimensions.	count	requests	Google Gemini
gemini.usage.model_total_tokens	Input plus output tokens for one Gemini model in the month; the model is in the observation's dimensions.	count	tokens	Google Gemini
gemini.usage.output_tokens	Google Gemini's ai output tokens for the month. Written to ai.usage.output_tokens instead when this connection is the primary source.	count	tokens	Google Gemini
gemini.usage.request_count	Google Gemini's ai api requests for the month. Written to ai.usage.request_count instead when this connection is the primary source.	count	requests	Google Gemini
gemini.usage.total_tokens	Google Gemini's ai total tokens for the month. Written to ai.usage.total_tokens instead when this connection is the primary source.	count	tokens	Google Gemini

NetSuite

Key	What it measures	Type	Unit	Fed by
netsuite.financials.expenses	NetSuite's expenses for the month. Written to finance.pnl.expenses instead when this connection is the primary source.	currency	USD	NetSuite
netsuite.financials.net_income	NetSuite's net income for the month. Written to finance.pnl.net_income instead when this connection is the primary source.	currency	USD	NetSuite
netsuite.financials.revenue	NetSuite's revenue for the month. Written to finance.pnl.revenue instead when this connection is the primary source.	currency	USD	NetSuite
netsuite.invoices.count	NetSuite's invoices created for the month. Written to finance.invoices.count instead when this connection is the primary source.	count	invoices	NetSuite
netsuite.invoices.paid_count	NetSuite's invoices paid for the month. Written to finance.invoices.paid_count instead when this connection is the primary source.	count	invoices	NetSuite
netsuite.invoices.volume	NetSuite's invoice volume for the month. Written to finance.invoices.volume instead when this connection is the primary source.	currency	USD	NetSuite
netsuite.vendor_bills.count	NetSuite's bills received for the month. Written to finance.bills.count instead when this connection is the primary source.	count	bills	NetSuite
netsuite.vendor_bills.volume	NetSuite's bill volume for the month. Written to finance.bills.volume instead when this connection is the primary source.	currency	USD	NetSuite

Pipedrive

Key	What it measures	Type	Unit	Fed by
pipedrive.deals.avg_sales_cycle_days	Pipedrive's avg sales cycle for the month. Written to crm.deals.avg_sales_cycle_days instead when this connection is the primary source.	duration	days	Pipedrive
pipedrive.deals.avg_value	Pipedrive's average won deal size for the month. Written to crm.deals.avg_value instead when this connection is the primary source.	currency	USD	Pipedrive
pipedrive.deals.lost_count	Pipedrive's lost deals for the month. Written to crm.deals.lost_count instead when this connection is the primary source.	count	deals	Pipedrive
pipedrive.deals.open_count	Deals open in Pipedrive at sync time, a snapshot rather than a period total, written once per month and rewritten on every sync.	count	—	Pipedrive
pipedrive.deals.open_value	Value of the deals open in Pipedrive at sync time, a snapshot rather than a period total, written once per month and rewritten on every sync.	currency	USD	Pipedrive
pipedrive.deals.win_rate	Pipedrive's deal win rate for the month. Written to crm.deals.win_rate instead when this connection is the primary source.	percentage	%	Pipedrive
pipedrive.deals.won_count	Pipedrive's won deals for the month. Written to crm.deals.won_count instead when this connection is the primary source.	count	deals	Pipedrive
pipedrive.deals.won_value	Pipedrive's won deal value for the month. Written to crm.deals.won_value instead when this connection is the primary source.	currency	USD	Pipedrive

QuickBooks

Key	What it measures	Type	Unit	Fed by
quickbooks.bills.count	QuickBooks's bills received for the month. Written to finance.bills.count instead when this connection is the primary source.	count	bills	QuickBooks
quickbooks.bills.volume	QuickBooks's bill volume for the month. Written to finance.bills.volume instead when this connection is the primary source.	currency	USD	QuickBooks
quickbooks.financials.expenses	QuickBooks's expenses for the month. Written to finance.pnl.expenses instead when this connection is the primary source.	currency	USD	QuickBooks
quickbooks.financials.net_income	QuickBooks's net income for the month. Written to finance.pnl.net_income instead when this connection is the primary source.	currency	USD	QuickBooks
quickbooks.financials.revenue	QuickBooks's revenue for the month. Written to finance.pnl.revenue instead when this connection is the primary source.	currency	USD	QuickBooks
quickbooks.invoices.count	QuickBooks's invoices created for the month. Written to finance.invoices.count instead when this connection is the primary source.	count	invoices	QuickBooks
quickbooks.invoices.paid_count	QuickBooks's invoices paid for the month. Written to finance.invoices.paid_count instead when this connection is the primary source.	count	invoices	QuickBooks
quickbooks.invoices.volume	QuickBooks's invoice volume for the month. Written to finance.invoices.volume instead when this connection is the primary source.	currency	USD	QuickBooks

Xero

Key	What it measures	Type	Unit	Fed by
xero.bills.count	Xero's bills received for the month. Written to finance.bills.count instead when this connection is the primary source.	count	bills	Xero
xero.bills.volume	Xero's bill volume for the month. Written to finance.bills.volume instead when this connection is the primary source.	currency	USD	Xero
xero.financials.expenses	Xero's expenses for the month. Written to finance.pnl.expenses instead when this connection is the primary source.	currency	USD	Xero
xero.financials.net_income	Xero's net income for the month. Written to finance.pnl.net_income instead when this connection is the primary source.	currency	USD	Xero
xero.financials.revenue	Xero's revenue for the month. Written to finance.pnl.revenue instead when this connection is the primary source.	currency	USD	Xero
xero.invoices.count	Xero's invoices created for the month. Written to finance.invoices.count instead when this connection is the primary source.	count	invoices	Xero
xero.invoices.paid_count	Xero's invoices paid for the month. Written to finance.invoices.paid_count instead when this connection is the primary source.	count	invoices	Xero
xero.invoices.volume	Xero's invoice volume for the month. Written to finance.invoices.volume instead when this connection is the primary source.	currency	USD	Xero

Billing

Key	What it measures	Type	Unit	Fed by
billing.charges.count	Charges in the period that kept revenue, in the currency the account settles in.	count	charges	Stripe
billing.charges.volume	What the period's charges kept, captured less refunded, tax included, in the currency the account settles in.	currency	USD	Stripe
billing.subscriptions.active_count	Subscriptions active at the end of the period.	count	subscriptions	Stripe
billing.subscriptions.churn_rate	Subscriptions canceled in the period as a percentage of those active at its start. Absent for a period with nothing active at the start.	percentage	%	Stripe
billing.subscriptions.churned_count	Subscriptions canceled in the period.	count	subscriptions	Stripe
billing.subscriptions.mrr	Monthly recurring revenue from subscriptions active at the end of the period.	currency	USD	Stripe
billing.subscriptions.new_count	Subscriptions created in the period.	count	subscriptions	Stripe

Engineering

Key	What it measures	Type	Unit	Fed by
engineering.coding_assistant.acceptance_rate	Accepted suggestions as a share of the suggestions offered in the period.	percentage	%	Anthropic, Claude Enterprise, GitHub
engineering.coding_assistant.acceptances	Suggestions developers accepted from an AI coding assistant in the period.	count	acceptances	Anthropic, Claude Enterprise, GitHub
engineering.coding_assistant.active_users	Developers using an AI coding assistant on an average day in the period. Summed across assistants when read combined.	average	users	Anthropic, Claude Enterprise, GitHub
engineering.coding_assistant.lines_accepted	Lines of code developers accepted from an AI coding assistant in the period.	count	lines	Anthropic, Claude Enterprise, GitHub, OpenAI Codex
engineering.coding_assistant.suggestions	Code suggestions an AI coding assistant offered developers in the period.	count	suggestions	Anthropic, Claude Enterprise, GitHub
engineering.pull_requests.cycle_time_avg	Average hours from a pull request being opened to being merged, over the pull requests merged in the period.	duration	hours	GitHub
engineering.pull_requests.merged_count	Pull requests merged in the period.	count	pull requests	GitHub

Government

Key	What it measures	Type	Unit	Fed by
government.cases.decision_days	Average days from case intake to eligibility decision.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
government.cases.processed_count	Benefit applications and cases processed in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
government.cases.staff_hours_per_case	Average caseworker hours of hands-on work per case decided — not the days it waits for a decision.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
government.constituent_services.calls_per_month	Inbound constituent contacts per month.	count	calls	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
government.permits.decision_days	Average days from permit intake to decision.	duration	days	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
government.permits.processed_count	Permit applications processed in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
government.permits.staff_hours_per_permit	Average staff hours of hands-on work per permit decided — not the days it waits for a decision.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

Stripe

Key	What it measures	Type	Unit	Fed by
stripe.charges.count	Stripe's charges for the month that kept revenue, in the currency the account settles in.	count	—	Stripe
stripe.charges.volume	What Stripe's charges kept for the month, captured less refunded, tax included, in the currency the account settles in. Charges in other currencies aren't counted.	currency	USD	Stripe
stripe.subscriptions.active_count	Stripe's active subscriptions for the month. Written to billing.subscriptions.active_count instead when this connection is the primary source.	count	subscriptions	Stripe
stripe.subscriptions.churn_rate	Stripe's subscription churn rate for the month. Written to billing.subscriptions.churn_rate instead when this connection is the primary source.	percentage	%	Stripe
stripe.subscriptions.churned_count	Stripe's churned subscriptions for the month. Written to billing.subscriptions.churned_count instead when this connection is the primary source.	count	subscriptions	Stripe
stripe.subscriptions.mrr	Stripe's monthly recurring revenue for the month. Written to billing.subscriptions.mrr instead when this connection is the primary source.	currency	USD	Stripe
stripe.subscriptions.new_count	Stripe's new subscriptions for the month. Written to billing.subscriptions.new_count instead when this connection is the primary source.	count	subscriptions	Stripe

Telecom

Key	What it measures	Type	Unit	Fed by
telecom.customer_care.calls_per_month	Inbound customer care calls per month.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
telecom.customer_care.cost_per_subscriber	Customer care cost per subscriber per period.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
telecom.field_service.truck_rolls_per_month	Field dispatch events per month.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
telecom.network.incident_count	Network incidents in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
telecom.network.mttr_minutes	Mean minutes to recover from a network incident.	duration	minutes	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
telecom.retention.cost_per_retained_subscriber	Average cost of retention offers per subscriber retained.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
telecom.retention.retained_subscribers	Subscribers retained through save offers in the period.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

Claude Enterprise

Key	What it measures	Type	Unit	Fed by
claude_enterprise.claude_code.acceptance_rate	Accepted tool actions as a share of those proposed across the Claude Enterprise organization for the month. Read as engineering.coding_assistant.acceptance_rate when Claude Enterprise is the primary coding-assistant source.	percentage	%	Claude Enterprise
claude_enterprise.claude_code.acceptances	Edits and writes developers accepted in Claude Code across the Claude Enterprise organization for the month — one accepted tool action each, the same unit as Anthropic's Claude Code report. Read as engineering.coding_assistant.acceptances when Claude Enterprise is the primary coding-assistant source.	count	acceptances	Claude Enterprise
claude_enterprise.claude_code.active_users_avg	Members who used Claude Code on an average day in the month — members active only in other Claude products are not counted. Read as engineering.coding_assistant.active_users when Claude Enterprise is the primary coding-assistant source.	average	users	Claude Enterprise
claude_enterprise.claude_code.lines_accepted	Lines of code Claude Code added across the Claude Enterprise organization in the month. Read as engineering.coding_assistant.lines_accepted when Claude Enterprise is the primary coding-assistant source.	count	lines	Claude Enterprise
claude_enterprise.claude_code.proposals	Tool actions Claude Code proposed across the Claude Enterprise organization in the month, accepted and rejected together. Read as engineering.coding_assistant.suggestions when Claude Enterprise is the primary coding-assistant source.	count	suggestions	Claude Enterprise

Customer success

Key	What it measures	Type	Unit	Fed by
customer_success.accounts.csm_count	Customer success managers covering the accounts.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
customer_success.accounts.per_csm	Customer accounts managed per customer success manager.	count	—	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
customer_success.renewals.arr_up_for_renewal	Annual recurring revenue from accounts up for renewal in the period.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
customer_success.renewals.rate	Percentage of customers up for renewal who renewed.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
customer_success.revenue.net_retention_rate	Revenue retained from existing customers including expansion, as a percentage of the starting base.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

Intercom

Key	What it measures	Type	Unit	Fed by
intercom.conversations.avg_first_response_hours	Intercom's avg ticket first response time for the month. Written to support.tickets.avg_first_response_hours instead when this connection is the primary source.	duration	hours	Intercom
intercom.conversations.avg_resolution_days	Intercom's average time from a conversation opening to closing for the month, in days (Intercom reports hours; the computer converts). Read as support.tickets.avg_resolution_days when this connection is the primary source.	duration	days	Intercom
intercom.conversations.closed_count	Intercom's support tickets closed for the month. Written to support.tickets.closed_count instead when this connection is the primary source.	count	tickets	Intercom
intercom.conversations.csat_score	Intercom's average conversation rating for the month on the 0–1 scale the support keys use (Intercom rates 1–5; the computer converts). Read as support.tickets.csat_score when this connection is the primary source.	score	0–1	Intercom
intercom.conversations.volume	Intercom's support tickets opened for the month. Written to support.tickets.volume instead when this connection is the primary source.	count	tickets	Intercom

Snowflake

Key	What it measures	Type	Unit	Fed by
snowflake.compute.credits_used	Snowflake warehouse credits consumed in the month.	count	credits	Snowflake
snowflake.cost.service_spend	The month's Snowflake bill split by usage type — compute, storage, cloud services and the rest — the type in the observation's dimensions. What a cost link's service mappings match.	currency	USD	Snowflake
snowflake.cost.total_spend	What Snowflake billed the account for the month, at its own contract rates, excluding usage Snowflake did not charge for. Read as cloud.cost.total_spend when this connection is the primary cloud cost source.	currency	USD	Snowflake
snowflake.query.avg_execution_seconds	Mean execution time of Snowflake queries in the month.	duration	seconds	Snowflake
snowflake.query.count	Successful Snowflake queries in the month.	count	queries	Snowflake

Zendesk

Key	What it measures	Type	Unit	Fed by
zendesk.tickets.avg_first_response_hours	Zendesk's avg ticket first response time for the month. Written to support.tickets.avg_first_response_hours instead when this connection is the primary source.	duration	hours	Zendesk
zendesk.tickets.avg_handle_time	Zendesk's avg ticket handle time for the month. Written to support.tickets.avg_handle_time instead when this connection is the primary source.	duration	hours	Zendesk
zendesk.tickets.csat_score	Zendesk's ticket csat for the month. Written to support.tickets.csat_score instead when this connection is the primary source.	score	0–1	Zendesk
zendesk.tickets.solved_count	Zendesk's support tickets closed for the month. Written to support.tickets.closed_count instead when this connection is the primary source.	count	tickets	Zendesk
zendesk.tickets.volume	Zendesk's support tickets opened for the month. Written to support.tickets.volume instead when this connection is the primary source.	count	tickets	Zendesk

Calendar

Key	What it measures	Type	Unit	Fed by
calendar.meetings.avg_attendees	Average attendees per meeting in the period on the calendars this connection syncs.	average	people	Google Workspace, Microsoft 365
calendar.meetings.avg_duration_mins	Average length in minutes of a meeting on the calendars this connection syncs.	duration	minutes	Google Workspace, Microsoft 365
calendar.meetings.count	Meetings in the period on the calendars this connection syncs — confirmed, timed events with two or more attendees. A meeting on more than one of those calendars counts once.	count	meetings	Google Workspace, Microsoft 365
calendar.meetings.hours	Hours of meetings in the period on the calendars this connection syncs, each meeting counted once however many attend and however many of those calendars it sits on.	duration	hours	Google Workspace, Microsoft 365

Cloud

Key	What it measures	Type	Unit	Fed by
cloud.cost.budget_amount	Cloud budget ceiling for the period, across providers.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
cloud.cost.forecasted_spend	Forecast end-of-period cloud spend based on the current usage trajectory.	currency	USD	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
cloud.cost.total_spend	Total cloud infrastructure spend for the period on the primary provider.	currency	USD	AWS, Azure, Databricks, Google Cloud, Snowflake
cloud.resources.idle_share	Percentage of provisioned cloud resources running at low or zero utilization.	percentage	%	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

Freshdesk

Key	What it measures	Type	Unit	Fed by
freshdesk.tickets.avg_first_response_hours	Freshdesk's avg ticket first response time for the month. Written to support.tickets.avg_first_response_hours instead when this connection is the primary source.	duration	hours	Freshdesk
freshdesk.tickets.avg_handle_time	Freshdesk's avg ticket handle time for the month. Written to support.tickets.avg_handle_time instead when this connection is the primary source.	duration	hours	Freshdesk
freshdesk.tickets.resolved_count	Freshdesk's support tickets closed for the month. Written to support.tickets.closed_count instead when this connection is the primary source.	count	tickets	Freshdesk
freshdesk.tickets.volume	Freshdesk's support tickets opened for the month. Written to support.tickets.volume instead when this connection is the primary source.	count	tickets	Freshdesk

Google Workspace

Key	What it measures	Type	Unit	Fed by
google_workspace.calendar.avg_attendees	Average attendees per meeting on the Google calendars this connection syncs, for the month. Read as calendar.meetings.avg_attendees when this connection is the primary calendar source.	average	people	Google Workspace
google_workspace.calendar.avg_meeting_duration_mins	Average meeting length in minutes on the Google calendars this connection syncs, for the month. Read as calendar.meetings.avg_duration_mins when this connection is the primary calendar source.	duration	minutes	Google Workspace
google_workspace.calendar.meeting_count	Meetings on the Google calendars this connection syncs, for the month, a meeting on more than one of them counted once. Read as calendar.meetings.count when this connection is the primary calendar source.	count	meetings	Google Workspace
google_workspace.calendar.meeting_hours	Hours of meetings on the Google calendars this connection syncs, for the month, each meeting counted once. Read as calendar.meetings.hours when this connection is the primary calendar source.	duration	hours	Google Workspace

Make

Key	What it measures	Type	Unit	Fed by
make.scenarios.credits	Make credits one scenario used in the month, which is what Make bills. Standard modules use 1 credit per operation; Make's built-in AI features add credits for tokens. The scenario is in the observation's dimensions.	count	credits	Make
make.scenarios.executions	Total runs of one Make scenario in the month; the scenario is in the observation's dimensions. Read as automation.runs.count, every scenario's row added, when this connection is the primary workflow-automation source.	count	runs	Make
make.scenarios.failed_executions	Failed runs of one Make scenario in the month; the scenario is in the observation's dimensions. Read as automation.runs.failed_count, every scenario's row added, when this connection is the primary workflow-automation source.	count	runs	Make
make.scenarios.successful_executions	Successful runs of one Make scenario in the month; the scenario is in the observation's dimensions. Read as automation.runs.successful_count, every scenario's row added, when this connection is the primary workflow-automation source.	count	runs	Make

Microsoft 365

Key	What it measures	Type	Unit	Fed by
microsoft_365.calendar.avg_attendees	Average attendees per meeting on the Outlook calendars this connection syncs, for the month. Read as calendar.meetings.avg_attendees when this connection is the primary calendar source.	average	people	Microsoft 365
microsoft_365.calendar.avg_meeting_duration_mins	Average meeting length in minutes on the Outlook calendars this connection syncs, for the month. Read as calendar.meetings.avg_duration_mins when this connection is the primary calendar source.	duration	minutes	Microsoft 365
microsoft_365.calendar.meeting_count	Meetings on the Outlook calendars this connection syncs, for the month, a meeting on more than one of them counted once. Read as calendar.meetings.count when this connection is the primary calendar source.	count	meetings	Microsoft 365
microsoft_365.calendar.meeting_hours	Hours of meetings on the Outlook calendars this connection syncs, for the month, each meeting counted once. Read as calendar.meetings.hours when this connection is the primary calendar source.	duration	hours	Microsoft 365

Workflow automation

Key	What it measures	Type	Unit	Fed by
automation.runs.count	Runs of automated workflows that finished in the period, successful and failed.	count	runs	Make, Zapier, n8n
automation.runs.failed_count	Runs of automated workflows that failed in the period.	count	runs	Make, Zapier, n8n
automation.runs.successful_count	Runs of automated workflows that succeeded in the period.	count	runs	Make, Zapier, n8n

BigQuery

Key	What it measures	Type	Unit	Fed by
bigquery.jobs.bytes_processed	Bytes processed by BigQuery jobs in the month.	count	bytes	BigQuery
bigquery.jobs.count	BigQuery jobs run in the month.	count	jobs	BigQuery
bigquery.jobs.slot_ms	Slot milliseconds consumed by BigQuery jobs in the month.	count	slot-ms	BigQuery

Google Cloud

Key	What it measures	Type	Unit	Fed by
gcp.billing.budget_amount	The amount of one GCP budget for the month; the budget is in the row's raw payload.	currency	USD	Google Cloud
gcp.billing.budget_spend	Spend against one GCP budget for the month; the budget is in the row's raw payload. Feeds cost entries through the initiative's budget mappings.	currency	USD	Google Cloud
gcp.billing.total_spend	Google Cloud's billing total spend for the month.	currency	USD	Google Cloud

Jira

Key	What it measures	Type	Unit	Fed by
jira.issues.cycle_time_avg	Jira's issues cycle time avg for the month. Read as work.issues.cycle_time_avg when Jira is the primary issue-tracking source.	duration	—	Jira
jira.issues.resolved_count	Jira's issues resolved count for the month. Read as work.issues.resolved_count when Jira is the primary issue-tracking source.	count	—	Jira
jira.issues.story_points_completed	Jira's issues story points completed for the month. Read as work.issues.story_points_completed when Jira is the primary issue-tracking source.	count	—	Jira

n8n

Key	What it measures	Type	Unit	Fed by
n8n.workflows.executions	Production executions of one n8n workflow that started in the month and finished, successful plus failed. Counted as n8n's Insights counts them, except evaluation runs: no manual test runs or sub-workflow runs, and a retry counts as an execution of its own. The workflow is in the observation's dimensions. Read as automation.runs.count, every workflow's row added, when this connection is the primary workflow-automation source.	count	executions	n8n
n8n.workflows.failed_executions	Production executions of one n8n workflow in the month that ended in error or crashed, including runs a later retry fixed; the workflow is in the observation's dimensions. Read as automation.runs.failed_count, every workflow's row added, when this connection is the primary workflow-automation source.	count	executions	n8n
n8n.workflows.successful_executions	Production executions of one n8n workflow in the month that succeeded, on the first try or a retry; the workflow is in the observation's dimensions. Read as automation.runs.successful_count, every workflow's row added, when this connection is the primary workflow-automation source.	count	executions	n8n

OpenAI Codex

Key	What it measures	Type	Unit	Fed by
openai_codex.usage.lines_accepted	Lines of code Codex added that developers kept, for the month. Read as engineering.coding_assistant.lines_accepted when Codex is the primary coding-assistant source. Codex reports no count of accepted suggestions, so it feeds none of the other coding-assistant keys.	count	lines	OpenAI Codex
openai_codex.usage.lines_committed	Lines attributed to Codex that reached a commit, for the month — the stronger of the two line counts, but one the other assistants do not report, so it stays a platform key.	count	lines	OpenAI Codex
openai_codex.usage.turns	Codex turns in the workspace for the month. Stands in for an active-developer count, which exists only in per-developer rows Roiva deliberately does not request.	count	turns	OpenAI Codex

Work

Key	What it measures	Type	Unit	Fed by
work.issues.cycle_time_avg	Average hours from an issue's start to its resolution, over the issues resolved in the period.	duration	hours	Jira
work.issues.resolved_count	Issues resolved in the period in the team's issue tracker.	count	issues	Jira
work.issues.story_points_completed	Story points on the issues resolved in the period.	count	points	Jira

Workforce

Key	What it measures	Type	Unit	Fed by
workforce.outreach.hours_per_week	Staff hours per week spent identifying at-risk people and reaching out to them.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
workforce.tasks.manual_per_week	Discrete manual tasks (data pulls, formatting, distribution) performed each week.	count	tasks	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)
workforce.time.hours_saved_per_week	Net staff hours recovered per week as a direct result of the initiative.	duration	hours	Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)

Zapier

Key	What it measures	Type	Unit	Fed by
zapier.zaps.failed_runs	Failed Zap runs reported to Roiva's Zapier webhook in the month. Read as automation.runs.failed_count when this connection is the primary workflow-automation source.	count	runs	Zapier
zapier.zaps.runs	Total Zap runs reported to Roiva's Zapier webhook in the month. Read as automation.runs.count when this connection is the primary workflow-automation source.	count	runs	Zapier
zapier.zaps.successful_runs	Successful Zap runs reported to Roiva's Zapier webhook in the month. Read as automation.runs.successful_count when this connection is the primary workflow-automation source.	count	runs	Zapier

AWS

Key	What it measures	Type	Unit	Fed by
aws.cost.service_spend	One AWS service's unblended cost for the month; the service is in the observation's dimensions. Feeds cost entries through the initiative's service mappings.	currency	USD	AWS
aws.cost.total_spend	AWS's cost total spend for the month.	currency	USD	AWS

Azure

Key	What it measures	Type	Unit	Fed by
azure.cost.service_spend	One Azure service's cost for the month; the service is in the observation's dimensions. Feeds cost entries through the initiative's service mappings.	currency	USD	Azure
azure.cost.total_spend	Azure's cost total spend for the month.	currency	USD	Azure

Databricks

Key	What it measures	Type	Unit	Fed by
databricks.cost.product_spend	Databricks list-price spend for the month on one product (billing_origin_product: MODEL_SERVING, JOBS, SQL, ...).	currency	USD	Databricks
databricks.cost.total_spend	Databricks list-price spend for the month, from system.billing.usage priced at system.billing.list_prices. Read as cloud.cost.total_spend when this connection is the primary cloud cost source.	currency	USD	Databricks

Brex

Key	What it measures	Type	Unit	Fed by
brex.card.spend	What the Brex card was charged in the month, in USD. A card is a payment rail rather than a provider billing for its own service, so this is not counted in provider-billed AI spend - a charge to a connected provider is already in that provider's own bill.	currency	USD	Brex

Looker

Key	What it measures	Type	Unit	Fed by
looker.looks.field_value	The monthly value of one numeric column of one Look; the Look and the column are in the observation's dimensions. Roiva cannot infer a unit from a Look, so the type is count unless a person overrides the definition.	count	—	Looker

Ramp

Key	What it measures	Type	Unit	Fed by
ramp.card.spend	What the Ramp card was charged in the month, in USD. A card is a payment rail rather than a provider billing for its own service, so this is not counted in provider-billed AI spend - a charge to a connected provider is already in that provider's own bill.	currency	USD	Ramp

Metric registry

CRM