{"slug":"metrics","title":"Metric registry","summary":"Every metric key Roiva ships: what it measures, its type and unit, and which kind of connection feeds it.","section":"Reference","url":"https://roiva-staging.com/docs/reference/metrics","generated":"Generated from what Roiva ships, on every deploy.","license":"https://roiva-staging.com/terms","tables":[{"title":"CRM","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"crm.contacts.lead_to_mql_rate","What it measures":"Percentage of new contacts that became Marketing Qualified Leads.","Type":"percentage","Unit":"%","Fed by":"HubSpot"},{"Key":"crm.contacts.mql_count","What it measures":"Contacts that reached MQL (Marketing Qualified Lead) status in the period.","Type":"count","Unit":"leads","Fed by":"HubSpot"},{"Key":"crm.contacts.mql_to_sql_rate","What it measures":"Percentage of the period's marketing-qualified contacts that also became sales-qualified.","Type":"percentage","Unit":"%","Fed by":"HubSpot"},{"Key":"crm.contacts.new_count","What it measures":"New contacts created in the CRM in the period.","Type":"count","Unit":"contacts","Fed by":"HubSpot"},{"Key":"crm.contacts.sql_count","What it measures":"Contacts that reached SQL (Sales Qualified Lead) status in the period.","Type":"count","Unit":"leads","Fed by":"HubSpot"},{"Key":"crm.contracts.avg_cycle_days","What it measures":"Average days from contract request to signature.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"crm.contracts.hours_per_contract","What it measures":"Combined legal, sales and operations hours to take one contract from request to signature.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"crm.contracts.processed_count","What it measures":"Contracts drafted, redlined and executed in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"crm.deals.at_risk_share","What it measures":"Percentage of open pipeline deals slipping or forecast to be lost.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"crm.deals.avg_demo_to_proposal_days","What it measures":"Average days from demo to proposal sent.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"crm.deals.avg_sales_cycle_days","What it measures":"Average days from deal creation to close across all closed deals in the period.","Type":"duration","Unit":"days","Fed by":"HubSpot, Pipedrive, Salesforce"},{"Key":"crm.deals.avg_value","What it measures":"Average closed-won deal size in the period.","Type":"currency","Unit":"USD","Fed by":"HubSpot, Pipedrive, Salesforce"},{"Key":"crm.deals.lost_count","What it measures":"Number of deals/opportunities closed-lost in the period.","Type":"count","Unit":"deals","Fed by":"HubSpot, Pipedrive, Salesforce"},{"Key":"crm.deals.pipeline_value","What it measures":"Value of the deals open in the period, taken as a snapshot when the CRM syncs.","Type":"currency","Unit":"USD","Fed by":"HubSpot, Pipedrive, Salesforce"},{"Key":"crm.deals.win_rate","What it measures":"Percentage of closed deals that were won. won_count / (won_count + lost_count).","Type":"percentage","Unit":"%","Fed by":"HubSpot, Pipedrive, Salesforce"},{"Key":"crm.deals.won_count","What it measures":"Number of deals/opportunities closed-won in the period.","Type":"count","Unit":"deals","Fed by":"HubSpot, Pipedrive, Salesforce"},{"Key":"crm.deals.won_value","What it measures":"Total value of all closed-won deals in the period.","Type":"currency","Unit":"USD","Fed by":"HubSpot, Pipedrive, Salesforce"},{"Key":"crm.leads.avg_lead_to_won_days","What it measures":"Average days from lead creation to closed-won.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"crm.leads.avg_qualify_minutes","What it measures":"Average staff minutes to qualify one inbound lead.","Type":"duration","Unit":"minutes","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"crm.leads.qualification_rate","What it measures":"Percentage of scored leads that pass the qualification threshold.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"crm.leads.qualified_count","What it measures":"Leads that met the qualification threshold and were routed to a rep.","Type":"count","Unit":"leads","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"crm.leads.scored_count","What it measures":"Inbound leads scored in the period.","Type":"count","Unit":"leads","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"crm.leads.sql_rate","What it measures":"Sales-qualified leads in the period as a percentage of the new leads created in it.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"crm.proposals.hours_per_proposal","What it measures":"Staff hours to assemble and send one proposal.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"crm.proposals.sent_count","What it measures":"Proposals sent to prospects in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"crm.reps.admin_hours_per_week","What it measures":"Sales rep hours per week on administrative work rather than selling.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"HubSpot","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"hubspot.contacts.lead_to_mql_rate","What it measures":"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.","Type":"percentage","Unit":"%","Fed by":"HubSpot"},{"Key":"hubspot.contacts.mql_count","What it measures":"HubSpot's marketing qualified leads for the month. Written to crm.contacts.mql_count instead when this connection is the primary source.","Type":"count","Unit":"leads","Fed by":"HubSpot"},{"Key":"hubspot.contacts.mql_to_sql_rate","What it measures":"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.","Type":"percentage","Unit":"%","Fed by":"HubSpot"},{"Key":"hubspot.contacts.new_count","What it measures":"HubSpot's new contacts for the month. Written to crm.contacts.new_count instead when this connection is the primary source.","Type":"count","Unit":"contacts","Fed by":"HubSpot"},{"Key":"hubspot.contacts.sql_count","What it measures":"HubSpot's sales qualified leads for the month. Written to crm.contacts.sql_count instead when this connection is the primary source.","Type":"count","Unit":"leads","Fed by":"HubSpot"},{"Key":"hubspot.deals.avg_sales_cycle_days","What it measures":"HubSpot's avg sales cycle for the month. Written to crm.deals.avg_sales_cycle_days instead when this connection is the primary source.","Type":"duration","Unit":"days","Fed by":"HubSpot"},{"Key":"hubspot.deals.avg_value","What it measures":"HubSpot's average won deal size for the month. Written to crm.deals.avg_value instead when this connection is the primary source.","Type":"currency","Unit":"USD","Fed by":"HubSpot"},{"Key":"hubspot.deals.lost_count","What it measures":"HubSpot's lost deals for the month. Written to crm.deals.lost_count instead when this connection is the primary source.","Type":"count","Unit":"deals","Fed by":"HubSpot"},{"Key":"hubspot.deals.open_count","What it measures":"Deals open in HubSpot at sync time, a snapshot rather than a period total, written once per month and rewritten on every sync.","Type":"count","Unit":null,"Fed by":"HubSpot"},{"Key":"hubspot.deals.open_value","What it measures":"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.","Type":"currency","Unit":"USD","Fed by":"HubSpot"},{"Key":"hubspot.deals.win_rate","What it measures":"HubSpot's deal win rate for the month. Written to crm.deals.win_rate instead when this connection is the primary source.","Type":"percentage","Unit":"%","Fed by":"HubSpot"},{"Key":"hubspot.deals.won_count","What it measures":"HubSpot's won deals for the month. Written to crm.deals.won_count instead when this connection is the primary source.","Type":"count","Unit":"deals","Fed by":"HubSpot"},{"Key":"hubspot.deals.won_value","What it measures":"HubSpot's won deal value for the month. Written to crm.deals.won_value instead when this connection is the primary source.","Type":"currency","Unit":"USD","Fed by":"HubSpot"},{"Key":"hubspot.marketing.campaigns_launched","What it measures":"HubSpot's campaigns launched for the month. Written to marketing.campaigns.launched_count instead when this connection is the primary source.","Type":"count","Unit":"campaigns","Fed by":"HubSpot"},{"Key":"hubspot.marketing.email_open_rate","What it measures":"HubSpot's marketing email open rate for the month. Written to marketing.emails.open_rate instead when this connection is the primary source.","Type":"percentage","Unit":"%","Fed by":"HubSpot"},{"Key":"hubspot.marketing.organic_sessions","What it measures":"HubSpot's organic search sessions for the month. Written to marketing.web.organic_sessions instead when this connection is the primary source.","Type":"count","Unit":"sessions","Fed by":"HubSpot"},{"Key":"hubspot.marketing.posts_published","What it measures":"HubSpot's content published for the month. Written to marketing.content.published_count instead when this connection is the primary source.","Type":"count","Unit":"posts","Fed by":"HubSpot"},{"Key":"hubspot.tickets.avg_resolution_days","What it measures":"HubSpot's avg ticket resolution time for the month. Written to support.tickets.avg_resolution_days instead when this connection is the primary source.","Type":"duration","Unit":"days","Fed by":"HubSpot"},{"Key":"hubspot.tickets.closed_count","What it measures":"HubSpot's support tickets closed for the month. Written to support.tickets.closed_count instead when this connection is the primary source.","Type":"count","Unit":"tickets","Fed by":"HubSpot"},{"Key":"hubspot.tickets.new_count","What it measures":"HubSpot's support tickets opened for the month. Written to support.tickets.volume instead when this connection is the primary source.","Type":"count","Unit":"tickets","Fed by":"HubSpot"},{"Key":"hubspot.tickets.open_count","What it measures":"Tickets open in HubSpot at sync time, a snapshot rather than a period total, written once per month and rewritten on every sync.","Type":"count","Unit":null,"Fed by":"HubSpot"},{"Key":"hubspot.tickets.resolution_rate","What it measures":"HubSpot's ticket resolution rate for the month. Written to support.tickets.resolution_rate instead when this connection is the primary source.","Type":"percentage","Unit":"%","Fed by":"HubSpot"}]},{"title":"Finance","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"finance.ap.data_entry_minutes_per_invoice","What it measures":"Staff minutes to key the data from one supplier invoice.","Type":"duration","Unit":"minutes","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"finance.ap.days_payable_outstanding","What it measures":"Average days between invoice receipt and payment.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"finance.ap.invoices_processed","What it measures":"Supplier invoices processed through the extraction pipeline in the period.","Type":"count","Unit":"invoices","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"finance.ap.minutes_per_invoice","What it measures":"Staff minutes to process one supplier invoice end to end.","Type":"duration","Unit":"minutes","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"finance.bills.count","What it measures":"Total vendor bills received in the period.","Type":"count","Unit":"bills","Fed by":"NetSuite, QuickBooks, Xero"},{"Key":"finance.bills.error_rate","What it measures":"Percentage of vendor bills entered with an error that needed correcting — amount, coding, vendor or a duplicate.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"finance.bills.volume","What it measures":"Total value of all vendor bills received in the period.","Type":"currency","Unit":"USD","Fed by":"NetSuite, QuickBooks, Xero"},{"Key":"finance.close.cycle_days","What it measures":"Calendar days from period end to final sign-off of the close.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"finance.disputes.avg_resolution_days","What it measures":"Average days to resolve a billing dispute.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"finance.invoices.count","What it measures":"Total outbound invoices created in the period.","Type":"count","Unit":"invoices","Fed by":"NetSuite, QuickBooks, Xero"},{"Key":"finance.invoices.error_rate","What it measures":"Percentage of invoices issued with an error requiring correction.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"finance.invoices.paid_count","What it measures":"Number of invoices marked paid in the period.","Type":"count","Unit":"invoices","Fed by":"NetSuite, QuickBooks, Xero"},{"Key":"finance.invoices.volume","What it measures":"Total billed on invoices created in the period, tax included.","Type":"currency","Unit":"USD","Fed by":"NetSuite, QuickBooks, Xero"},{"Key":"finance.pnl.expenses","What it measures":"Total operating expenses for the period from the accounting system.","Type":"currency","Unit":"USD","Fed by":"NetSuite, QuickBooks, Xero"},{"Key":"finance.pnl.net_income","What it measures":"Net income (revenue minus expenses) for the period.","Type":"currency","Unit":"USD","Fed by":"NetSuite, QuickBooks, Xero"},{"Key":"finance.pnl.revenue","What it measures":"Total revenue recognized in the period from the accounting system.","Type":"currency","Unit":"USD","Fed by":"NetSuite, QuickBooks, Xero"},{"Key":"finance.reconciliation.hours","What it measures":"Staff hours on manual reconciliations per close cycle.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"finance.revenue_recognition.adjustment_count","What it measures":"Revenue recognition adjustments or corrections required per period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"finance.revenue_recognition.manual_hours","What it measures":"Staff hours per period building revenue recognition schedules by hand.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"finance.treasury.avg_cash_balance","What it measures":"Average daily cash balance in the period.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"finance.treasury.manual_hours_per_cycle","What it measures":"Staff hours to build and update the cash flow model per forecast cycle.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"Professional services","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"professional_services.billing.realization_rate","What it measures":"Billed fees as a percentage of standard-rate value of hours worked.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"professional_services.billing.unbilled_hours_per_practitioner_weekly","What it measures":"Billable hours worked but not captured per practitioner per week.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"professional_services.knowledge.active_users","What it measures":"Practitioners actively using the knowledge system in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"professional_services.knowledge.search_minutes","What it measures":"Average minutes per search for prior work and precedent.","Type":"duration","Unit":"minutes","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"professional_services.knowledge.searches_per_practitioner","What it measures":"Times per period a practitioner searches for prior work, methods or precedent.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"professional_services.practitioners.available_hours","What it measures":"Working hours available per practitioner in the period.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"professional_services.practitioners.billable_count","What it measures":"Billable practitioners in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"professional_services.practitioners.billable_hours_worked","What it measures":"Hours worked by billable practitioners in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"professional_services.projects.at_risk_detection_lead_weeks","What it measures":"Average weeks before delivery failure that an at-risk project is identified.","Type":"duration","Unit":"weeks","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"professional_services.projects.at_risk_identified_count","What it measures":"At-risk projects identified in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"professional_services.projects.budget_overrun_rate","What it measures":"Percentage of projects that exceeded the approved budget.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"professional_services.projects.pm_count_using_automation","What it measures":"Project managers using the automated reporting system.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"professional_services.projects.recovery_hours_per_project","What it measures":"Senior PM hours spent recovering a late-detected at-risk project.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"professional_services.proposals.prep_hours","What it measures":"Staff hours to assemble one proposal.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"professional_services.proposals.submitted_count","What it measures":"Proposals or statement-of-work responses submitted in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"professional_services.reporting.client_reports_delivered","What it measures":"Client status reports delivered in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"professional_services.reporting.hours_per_client_report","What it measures":"Staff hours to compile, format and deliver one client report.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"professional_services.reporting.reports_per_pm","What it measures":"Client reports each project manager prepares in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"professional_services.utilization.bench_share","What it measures":"Percentage of available practitioner hours spent on the bench.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"professional_services.utilization.billable_rate","What it measures":"Billable hours as a percentage of available practitioner hours.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"Construction","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"construction.bids.awarded_value","What it measures":"Contract value of projects awarded in the period.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"construction.bids.submitted_count","What it measures":"Bids submitted in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"construction.documents.search_hours_per_week","What it measures":"Staff hours per week locating current drawing versions and document history.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"construction.estimating.hours_per_bid","What it measures":"Estimator hours per bid.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"construction.estimating.variance_rate","What it measures":"Average estimate-to-actual variance as a percentage of the estimate.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"construction.job_costing.review_cycle_days","What it measures":"Average days between formal job cost reviews on active projects.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"construction.job_costing.review_cycles","What it measures":"Formal job cost review cycles in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"construction.job_costing.review_hours_per_project","What it measures":"PM and controller hours per project per job cost review cycle.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"construction.labor.hours_worked","What it measures":"Field labor hours worked in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"construction.payroll.processing_hours_per_week","What it measures":"Staff hours per week collecting, reconciling and processing timecards.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"construction.payroll.timecard_error_rate","What it measures":"Percentage of timecards requiring correction.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"construction.payroll.timecards_processed","What it measures":"Timecards processed in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"construction.projects.active_count","What it measures":"Active projects in the portfolio during the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"construction.projects.cost_overrun_rate","What it measures":"Percentage of projects that exceed the approved budget.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"construction.rfis.response_days","What it measures":"Average calendar days from RFI submission to formal response.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"construction.rfis.submitted_count","What it measures":"Requests for information submitted in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"construction.safety.observation_hours_per_week","What it measures":"Staff hours per week on safety observation reporting and job hazard analysis distribution.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"construction.safety.recordable_incident_rate","What it measures":"OSHA recordable incident rate (incidents × 200,000 ÷ hours worked).","Type":"ratio","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"Healthcare","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"healthcare.appointments.avg_length","What it measures":"Scheduled minutes of a patient visit of the type recovered clinician time would be booked as.","Type":"duration","Unit":"minutes","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"healthcare.appointments.no_show_rate","What it measures":"Percentage of scheduled appointments the patient did not attend.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"healthcare.appointments.scheduled_count","What it measures":"Appointments scheduled in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"healthcare.audits.prep_hours_per_cycle","What it measures":"Staff hours preparing documentation for one quality audit or regulatory review.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"healthcare.claims.denial_rate","What it measures":"Percentage of submitted claims denied on first submission.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"healthcare.claims.denied_count","What it measures":"Claims denied on first submission in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"healthcare.coding.charts_coded","What it measures":"Charts coded in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"healthcare.coding.charts_per_coder_per_day","What it measures":"Charts coded per medical coder per day.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"healthcare.coding.coder_count","What it measures":"Medical coders using AI-assisted coding.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"healthcare.coding.error_rate","What it measures":"Percentage of coded charts with an error.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"healthcare.denials.appeal_success_rate","What it measures":"Share of appealed denials overturned.","Type":"ratio","Unit":"0–1","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"healthcare.documentation.after_hours_charting_hours_per_week","What it measures":"Hours per week clinicians spend on documentation outside clinical hours.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"healthcare.documentation.clinician_count","What it measures":"Clinicians using ambient documentation.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"healthcare.documentation.encounters_per_clinician_per_day","What it measures":"Patient encounters per clinician per clinical day.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"healthcare.documentation.minutes_per_encounter","What it measures":"Clinician documentation minutes per patient encounter.","Type":"duration","Unit":"minutes","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"healthcare.quality.care_gap_closure_rate","What it measures":"Percentage of identified care gaps closed.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"healthcare.quality.value_based_revenue_at_risk","What it measures":"Revenue at risk or bonus-eligible under value-based contracts, for a performance year.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"healthcare.revenue_cycle.days_in_ar","What it measures":"Average days from claim submission to payment.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"HR","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"hr.hiring.days_to_first_interview","What it measures":"Average days from job posting to first interview.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hr.hiring.hires_count","What it measures":"Positions filled in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hr.hiring.recruiter_hours_per_hire","What it measures":"Recruiter hours on resume review and screening per hire.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hr.inquiries.avg_response_hours","What it measures":"Average hours from employee inquiry to HR response for routine questions.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hr.inquiries.chatbot_deflection_rate","What it measures":"Share of employee inquiries fully resolved by the chatbot without HR involvement.","Type":"ratio","Unit":"0–1","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hr.inquiries.tier1_per_month","What it measures":"Routine HR inquiries received per month (policy, PTO, benefits, payroll).","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hr.onboarding.admin_hours_per_hire","What it measures":"HR and IT staff hours on onboarding tasks per new hire.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hr.onboarding.completion_rate","What it measures":"Percentage of required onboarding tasks completed within the target window.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hr.onboarding.days_to_productivity","What it measures":"Days from start date until a new hire is independently productive.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hr.onboarding.hires_count","What it measures":"New employees who completed onboarding in the period.","Type":"count","Unit":"hires","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hr.onboarding.tasks_automated","What it measures":"Onboarding checklist items (provisioning, welcome communications, acknowledgments) now handled automatically.","Type":"count","Unit":"tasks","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hr.reporting.hours_per_month","What it measures":"Staff hours per month building workforce reports.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hr.reviews.admin_hours_per_cycle","What it measures":"HR and manager hours coordinating, collecting and calibrating one performance review cycle.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hr.reviews.admin_hours_per_employee","What it measures":"HR and manager admin hours per employee per review cycle.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hr.reviews.completion_rate","What it measures":"Percentage of required performance reviews completed on time.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hr.reviews.employees_reviewed","What it measures":"Employees included in the performance review cycle.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hr.workforce.headcount","What it measures":"Total employee headcount in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hr.workforce.voluntary_attrition_rate","What it measures":"Annualized voluntary attrition rate.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"Supply chain","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"supply_chain.deliveries.on_time_rate","What it measures":"Percentage of orders delivered on or before the promised date.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"supply_chain.deliveries.revenue_at_risk","What it measures":"Revenue exposed to late deliveries in the period through penalties, cancellations and credits.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"supply_chain.forecasting.accuracy","What it measures":"Demand forecast accuracy against actual demand.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"supply_chain.inventory.avg_value","What it measures":"Average inventory value on hand during the period.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"supply_chain.inventory.excess_value","What it measures":"Value of inventory held above target levels.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"supply_chain.inventory.stockout_events","What it measures":"Stockout incidents in the period.","Type":"count","Unit":"events","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"supply_chain.inventory.stockout_rate","What it measures":"Percentage of SKUs or order lines that were out of stock.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"supply_chain.procurement.hours_per_po","What it measures":"Staff hours per purchase-order cycle, from creation through invoice matching.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"supply_chain.procurement.invoice_matching_hours_per_week","What it measures":"AP staff hours per week matching purchase orders to invoices.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"supply_chain.procurement.po_cycle_days","What it measures":"Average calendar days from purchase requisition to approved purchase order.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"supply_chain.procurement.purchase_orders_processed","What it measures":"Purchase orders processed in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"supply_chain.shipments.cost_per_shipment","What it measures":"Average total cost per shipment.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"supply_chain.shipments.count","What it measures":"Outbound shipments in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"supply_chain.suppliers.disruptions_per_year","What it measures":"Supply disruptions per year.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"supply_chain.suppliers.review_hours_per_month","What it measures":"Staff hours per month on supplier health reviews and risk assessments.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"supply_chain.warehouse.pick_error_rate","What it measures":"Percentage of picks resulting in an error.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"supply_chain.warehouse.picks_count","What it measures":"Units picked in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"supply_chain.warehouse.picks_per_labor_hour","What it measures":"Units picked per warehouse labor hour.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"Support","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"support.calls.avg_handle_time","What it measures":"Average minutes an agent spends on one inbound phone call — talk, hold and wrap-up.","Type":"duration","Unit":"minutes","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"support.knowledge_base.article_count","What it measures":"Knowledge base articles being maintained.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"support.knowledge_base.avg_article_age_days","What it measures":"Average days since knowledge base articles were last reviewed or updated.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"support.knowledge_base.update_minutes_per_article","What it measures":"Minutes to review and update one knowledge base article per cycle.","Type":"duration","Unit":"minutes","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"support.quality.review_minutes_per_interaction","What it measures":"Minutes for a QA analyst to review and score one interaction.","Type":"duration","Unit":"minutes","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"support.quality.sample_rate","What it measures":"Share of support interactions reviewed for quality.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"support.tickets.ai_resolution_rate","What it measures":"Percentage of the tickets resolved in the period that AI resolved, without human escalation.","Type":"percentage","Unit":"%","Fed by":"Freshdesk, Intercom, Zendesk"},{"Key":"support.tickets.ai_resolved_count","What it measures":"Support tickets fully resolved by AI without human escalation in the period.","Type":"count","Unit":"tickets","Fed by":"Freshdesk, Intercom, Zendesk"},{"Key":"support.tickets.avg_first_response_hours","What it measures":"Average hours from ticket creation to the first agent reply, across tickets opened in the period.","Type":"duration","Unit":"hours","Fed by":"Freshdesk, Intercom, Zendesk"},{"Key":"support.tickets.avg_handle_time","What it measures":"Average time agents spent handling each ticket in the period.","Type":"duration","Unit":"hours","Fed by":"Freshdesk, Zendesk"},{"Key":"support.tickets.avg_resolution_days","What it measures":"Average days from ticket creation to resolution (Intercom close time ÷ 24).","Type":"duration","Unit":"days","Fed by":"HubSpot, Intercom, Salesforce"},{"Key":"support.tickets.closed_count","What it measures":"Total support tickets closed/resolved in the period.","Type":"count","Unit":"tickets","Fed by":"Freshdesk, HubSpot, Intercom, Salesforce, Zendesk"},{"Key":"support.tickets.cost_per_ticket","What it measures":"Blended cost to resolve one support ticket (agent labor plus tooling).","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"support.tickets.csat_score","What it measures":"Average customer satisfaction on closed tickets, normalized to 0–1 (Zendesk good/bad → 0/1; Intercom 1–5 → (rating − 1) ÷ 4).","Type":"score","Unit":"0–1","Fed by":"Intercom, Zendesk"},{"Key":"support.tickets.deflected_count","What it measures":"Tickets resolved by self-service or AI without an agent in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"support.tickets.resolution_rate","What it measures":"Percentage of tickets opened in the period that were also closed.","Type":"percentage","Unit":"%","Fed by":"HubSpot, Salesforce"},{"Key":"support.tickets.self_serve_deflection_rate","What it measures":"Percentage of support inquiries resolved without agent involvement.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"support.tickets.volume","What it measures":"Total support tickets opened in the period (help-desk tickets, Intercom conversations, Salesforce cases).","Type":"count","Unit":"tickets","Fed by":"Freshdesk, HubSpot, Intercom, Salesforce, Zendesk"}]},{"title":"Financial services","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"financial_services.advisors.accounts_per_advisor","What it measures":"Client accounts managed per advisor.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"financial_services.advisors.aum_per_advisor","What it measures":"Average assets under management per advisor.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"financial_services.advisors.count","What it measures":"Advisors using the client intelligence platform.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"financial_services.advisors.proactive_outreach_rate","What it measures":"Percentage of clients contacted proactively per quarter.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"financial_services.fraud.false_positive_rate","What it measures":"Percentage of flagged transactions that were legitimate.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"financial_services.fraud.flagged_count","What it measures":"Transactions flagged by the fraud system in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"financial_services.fraud.loss_rate_bps","What it measures":"Fraud losses as basis points of transaction volume.","Type":"count","Unit":"bps","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"financial_services.kyc.review_hours_per_account","What it measures":"Staff hours to review and approve one account.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"financial_services.onboarding.accounts_count","What it measures":"New accounts onboarded through KYC/AML in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"financial_services.onboarding.days_to_active","What it measures":"Average days from application to active account.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"financial_services.regulatory_reporting.error_rate","What it measures":"Percentage of regulatory report line items requiring correction before submission.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"financial_services.regulatory_reporting.line_items_per_cycle","What it measures":"Line items across the regulatory reports filed in one reporting cycle.","Type":"average","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"financial_services.regulatory_reporting.prep_hours_per_report","What it measures":"Staff hours to prepare and validate one regulatory report.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"financial_services.transactions.volume","What it measures":"Total transaction volume processed in the period.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"financial_services.underwriting.decision_hours","What it measures":"Average hours from application submission to decision.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"financial_services.underwriting.manual_review_rate","What it measures":"Percentage of applications requiring manual underwriter review.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"financial_services.underwriting.review_hours_per_application","What it measures":"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.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"Real estate","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"real_estate.leasing.annual_renewal_rate","What it measures":"Percentage of expiring leases renewed.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"real_estate.leasing.days_to_lease","What it measures":"Average days from inquiry to signed lease.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"real_estate.leasing.staff_hours_per_lease","What it measures":"Leasing staff hours per signed lease (lead response, tours, application processing).","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"real_estate.leasing.units_leased","What it measures":"Units signed to new leases in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"real_estate.leasing.units_up_for_renewal","What it measures":"Units with lease expirations in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"real_estate.maintenance.emergency_repairs_per_month","What it measures":"Unplanned emergency repair incidents per month.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"real_estate.portfolio.aum","What it measures":"Portfolio value under management at the time of measurement.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"real_estate.portfolio.property_count","What it measures":"Properties in the portfolio.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"real_estate.portfolio.unit_count","What it measures":"Residential or commercial units in the portfolio.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"real_estate.reporting.hours_per_property_monthly","What it measures":"Staff hours per property per month compiling performance and financial reports.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"real_estate.reporting.review_cycle_days","What it measures":"Calendar days between formal portfolio performance reviews.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"real_estate.tenants.contacts_per_unit_monthly","What it measures":"Inbound phone and email contacts per unit per month requiring staff handling.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"real_estate.tenants.minutes_per_contact","What it measures":"Staff minutes to handle one inbound tenant contact.","Type":"duration","Unit":"minutes","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"real_estate.tenants.satisfaction_score","What it measures":"Average tenant satisfaction score from periodic surveys.","Type":"score","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"real_estate.work_orders.admin_minutes_per_order","What it measures":"Staff minutes per work order on creation, vendor calls and status follow-ups.","Type":"duration","Unit":"minutes","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"real_estate.work_orders.created_count","What it measures":"Work orders created in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"real_estate.work_orders.resolution_days","What it measures":"Average days from work order submission to resolution.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"Marketing","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"marketing.budget.destination_pipeline_per_dollar","What it measures":"Pipeline created per dollar of spend in the channels budget was moved to, from the attribution model.","Type":"ratio","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"marketing.budget.reallocated","What it measures":"The period's marketing spend that went to the channels attribution favored instead of the channels it was taken from.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"marketing.budget.source_pipeline_per_dollar","What it measures":"Pipeline created per dollar of spend in the channels budget was taken from, from the attribution model.","Type":"ratio","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"marketing.campaigns.avg_list_size","What it measures":"Average recipients per campaign send.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"marketing.campaigns.conversion_rate","What it measures":"Percentage of campaign recipients who completed the campaign's goal.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"marketing.campaigns.hours_per_launch","What it measures":"Staff hours to configure and launch one campaign.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"marketing.campaigns.launched_count","What it measures":"Marketing campaigns with a start date in the period.","Type":"count","Unit":"campaigns","Fed by":"HubSpot"},{"Key":"marketing.content.hours_per_piece","What it measures":"Staff hours from brief to published asset.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"marketing.content.pieces_per_month","What it measures":"Content pieces produced per month.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"marketing.content.published_count","What it measures":"Blog posts published in the period on the connected CMS.","Type":"count","Unit":"posts","Fed by":"HubSpot"},{"Key":"marketing.emails.open_rate","What it measures":"Opens as a percentage of deliveries across marketing emails published in the period.","Type":"percentage","Unit":"%","Fed by":"HubSpot"},{"Key":"marketing.nurture.manual_hours_per_week","What it measures":"Staff hours per week on lead follow-up and segmentation done by hand.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"marketing.pipeline.sourced_share","What it measures":"Percentage of total sales pipeline attributed to marketing-sourced leads.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"marketing.reporting.hours_per_week","What it measures":"Staff hours per week on marketing performance reports.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"marketing.seo.research_hours_per_month","What it measures":"Staff hours per month on keyword research and on-page optimization.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"marketing.web.organic_sessions","What it measures":"Website sessions from organic search in the period.","Type":"count","Unit":"sessions","Fed by":"HubSpot"}]},{"title":"Anthropic","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"anthropic.claude_code.acceptance_rate","What it measures":"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.","Type":"percentage","Unit":"%","Fed by":"Anthropic"},{"Key":"anthropic.claude_code.acceptances","What it measures":"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.","Type":"count","Unit":"acceptances","Fed by":"Anthropic"},{"Key":"anthropic.claude_code.active_users_avg","What it measures":"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.","Type":"average","Unit":"users","Fed by":"Anthropic"},{"Key":"anthropic.claude_code.lines_accepted","What it measures":"Lines of code Claude Code added in the month. Read as engineering.coding_assistant.lines_accepted when Anthropic is the primary coding-assistant source.","Type":"count","Unit":"lines","Fed by":"Anthropic"},{"Key":"anthropic.claude_code.proposals","What it measures":"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.","Type":"count","Unit":"suggestions","Fed by":"Anthropic"},{"Key":"anthropic.usage.cost_per_million_output_tokens","What it measures":"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.","Type":"currency","Unit":"USD","Fed by":"Anthropic"},{"Key":"anthropic.usage.input_tokens","What it measures":"Anthropic's ai input tokens for the month. Written to ai.usage.input_tokens instead when this connection is the primary source.","Type":"count","Unit":"tokens","Fed by":"Anthropic"},{"Key":"anthropic.usage.model_input_tokens","What it measures":"Input tokens sent to one Claude model in the month; the model is in the observation's dimensions.","Type":"count","Unit":"tokens","Fed by":"Anthropic"},{"Key":"anthropic.usage.model_output_tokens","What it measures":"Output tokens generated by one Claude model in the month; the model is in the observation's dimensions.","Type":"count","Unit":"tokens","Fed by":"Anthropic"},{"Key":"anthropic.usage.model_spend","What it measures":"Anthropic API spend on one model for the month; the model is in the observation's dimensions.","Type":"currency","Unit":"USD","Fed by":"Anthropic"},{"Key":"anthropic.usage.model_total_tokens","What it measures":"Input plus output tokens for one Claude model in the month; the model is in the observation's dimensions.","Type":"count","Unit":"tokens","Fed by":"Anthropic"},{"Key":"anthropic.usage.output_tokens","What it measures":"Anthropic's ai output tokens for the month. Written to ai.usage.output_tokens instead when this connection is the primary source.","Type":"count","Unit":"tokens","Fed by":"Anthropic"},{"Key":"anthropic.usage.spend","What it measures":"Anthropic's usage spend for the month.","Type":"currency","Unit":"USD","Fed by":"Anthropic"},{"Key":"anthropic.usage.total_tokens","What it measures":"Anthropic's ai total tokens for the month. Written to ai.usage.total_tokens instead when this connection is the primary source.","Type":"count","Unit":"tokens","Fed by":"Anthropic"}]},{"title":"IT","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"it.audits.hours_per_cycle","What it measures":"IT staff hours per software inventory and license reconciliation audit cycle.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"it.deployments.change_failure_rate","What it measures":"Percentage of deployments causing a rollback or incident.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"it.deployments.count","What it measures":"Production deployments in the period.","Type":"count","Unit":null,"Fed by":"GitHub"},{"Key":"it.deployments.hours_per_deployment","What it measures":"Engineer hours per production deployment, including manual steps, monitoring and validation.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"it.deployments.per_week","What it measures":"Production deployments per week.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"it.helpdesk.avg_users_per_incident","What it measures":"End-users affected or blocked by a typical IT incident.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"it.helpdesk.mean_time_to_resolution_hours","What it measures":"Mean hours to resolve a tier-1 IT issue.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"it.helpdesk.tickets_per_month","What it measures":"IT helpdesk tickets per month requiring agent handling.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"it.security.incident_count","What it measures":"Security incidents investigated in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"it.security.incidents_per_year","What it measures":"Security incidents per year.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"it.security.mean_time_to_contain_hours","What it measures":"Mean hours from detection of a security incident to its containment.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"it.security.mean_time_to_detect_hours","What it measures":"Mean hours from a security incident starting to its detection.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"it.software.annual_spend","What it measures":"Total annual software license spend.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"it.software.unused_license_share","What it measures":"Percentage of purchased licenses that are unassigned or unused.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"Legal","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"legal.compliance.change_response_days","What it measures":"Days from a regulatory change being published to controls updated and staff notified.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"legal.compliance.gaps_prevented","What it measures":"Compliance control gaps identified and closed before becoming findings, in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"legal.compliance.monitoring_hours_per_week","What it measures":"Staff hours per week tracking regulatory publications and updates.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"legal.contracts.missed_renewals_per_year","What it measures":"Contracts that auto-renewed or lapsed unintentionally per year.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"legal.contracts.search_hours_per_week","What it measures":"Staff hours per week locating and retrieving contracts.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"legal.matters.closed_count","What it measures":"Legal matters handled to completion in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"legal.matters.research_hours_per_matter","What it measures":"Legal research hours per matter.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"legal.outside_counsel.monthly_spend","What it measures":"Monthly spend on outside counsel for matters that could be handled in-house.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"legal.policies.attestation_completion_rate","What it measures":"Percentage of employees who complete required policy attestations by the deadline.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"legal.training.admin_hours_per_cycle","What it measures":"Staff hours per compliance training cycle on enrollment, reminders, tracking and attestation.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"legal.vendors.onboarded_count","What it measures":"New vendors onboarded through due diligence in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"legal.vendors.onboarding_days","What it measures":"Average days from vendor selection to fully approved and active.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"legal.vendors.portfolio_count","What it measures":"Active vendors in the third-party risk portfolio.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"legal.vendors.review_hours_per_vendor","What it measures":"Staff hours per vendor for a periodic risk reassessment.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"Manufacturing","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"manufacturing.data.reconciliation_hours_per_week","What it measures":"Staff hours per week reconciling data between shop floor systems and the ERP.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"manufacturing.equipment.oee","What it measures":"Overall equipment effectiveness.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"manufacturing.equipment.oee_reporting_hours_per_week","What it measures":"Staff hours per week collecting and compiling OEE data.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"manufacturing.equipment.unplanned_downtime_hours_per_month","What it measures":"Unplanned equipment downtime hours per month.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"manufacturing.maintenance.annual_cost_per_asset","What it measures":"Average annual maintenance cost per asset including reactive repairs and planned servicing.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"manufacturing.maintenance.annual_spend","What it measures":"Annual maintenance spend across monitored assets, planned and reactive.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"manufacturing.maintenance.cost_reduction_rate","What it measures":"Percentage reduction in total maintenance spend from condition-based servicing.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"manufacturing.materials.shortage_incidents_per_month","What it measures":"Production stoppages caused by material shortages per month.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"manufacturing.production.theoretical_capacity_units","What it measures":"Units producible in the period at nameplate capacity.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"manufacturing.production.units_produced","What it measures":"Units produced in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"manufacturing.quality.defect_escape_rate","What it measures":"Percentage of produced units with a defect that reaches the customer.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"manufacturing.quality.first_pass_yield","What it measures":"Percentage of units completing production without defect, rework or scrap.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"manufacturing.quality.scrap_cost_per_month","What it measures":"Monthly scrap and rework cost.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"manufacturing.scheduling.hours_per_week","What it measures":"Staff hours per week building and adjusting production schedules.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"Insurance","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"insurance.claims.adjuster_hours_per_claim","What it measures":"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.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"insurance.claims.auto_adjudication_rate","What it measures":"Share of claims processed straight-through without adjuster review.","Type":"ratio","Unit":"0–1","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"insurance.claims.avg_cycle_days","What it measures":"Average days from first notice of loss to claim closure.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"insurance.claims.incurred_losses","What it measures":"Incurred losses on the affected book in the period.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"insurance.claims.lae_ratio","What it measures":"Loss adjustment expense as a percentage of incurred losses.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"insurance.claims.leakage_rate","What it measures":"Paid loss attributable to fraud or excess adjustments as a percentage of claims paid.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"insurance.claims.processed_count","What it measures":"Claims submitted and processed in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"insurance.fraud.false_positive_siu_referrals","What it measures":"Special investigations unit referrals per month that do not substantiate fraud.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"insurance.policy_servicing.calls_per_month","What it measures":"Inbound policy-servicing call volume per month.","Type":"count","Unit":"calls","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"insurance.underwriting.earned_premium","What it measures":"Earned premium on the targeted book in the period.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"insurance.underwriting.loss_ratio","What it measures":"Incurred losses as a percentage of earned premium.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"insurance.underwriting.quote_hours","What it measures":"Average underwriter hours from application to bindable quote.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"insurance.underwriting.submission_count","What it measures":"New business submissions reviewed in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"Salesforce","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"salesforce.cases.avg_resolution_days","What it measures":"Salesforce's avg ticket resolution time for the month. Written to support.tickets.avg_resolution_days instead when this connection is the primary source.","Type":"duration","Unit":"days","Fed by":"Salesforce"},{"Key":"salesforce.cases.closed_count","What it measures":"Salesforce's support tickets closed for the month. Written to support.tickets.closed_count instead when this connection is the primary source.","Type":"count","Unit":"tickets","Fed by":"Salesforce"},{"Key":"salesforce.cases.open_count","What it measures":"Cases open in Salesforce at sync time, a snapshot rather than a period total, written once per month and rewritten on every sync.","Type":"count","Unit":null,"Fed by":"Salesforce"},{"Key":"salesforce.cases.resolution_rate","What it measures":"Salesforce's ticket resolution rate for the month. Written to support.tickets.resolution_rate instead when this connection is the primary source.","Type":"percentage","Unit":"%","Fed by":"Salesforce"},{"Key":"salesforce.cases.volume","What it measures":"Salesforce's support tickets opened for the month. Written to support.tickets.volume instead when this connection is the primary source.","Type":"count","Unit":"tickets","Fed by":"Salesforce"},{"Key":"salesforce.opportunities.avg_sales_cycle_days","What it measures":"Salesforce's avg sales cycle for the month. Written to crm.deals.avg_sales_cycle_days instead when this connection is the primary source.","Type":"duration","Unit":"days","Fed by":"Salesforce"},{"Key":"salesforce.opportunities.avg_value","What it measures":"Salesforce's average won opportunity size for the month. Written to crm.deals.avg_value instead when this connection is the primary source.","Type":"currency","Unit":"USD","Fed by":"Salesforce"},{"Key":"salesforce.opportunities.lost_count","What it measures":"Salesforce's lost deals for the month. Written to crm.deals.lost_count instead when this connection is the primary source.","Type":"count","Unit":"deals","Fed by":"Salesforce"},{"Key":"salesforce.opportunities.open_count","What it measures":"Opportunities open in Salesforce at sync time, a snapshot rather than a period total, written once per month and rewritten on every sync.","Type":"count","Unit":null,"Fed by":"Salesforce"},{"Key":"salesforce.opportunities.open_value","What it measures":"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.","Type":"currency","Unit":"USD","Fed by":"Salesforce"},{"Key":"salesforce.opportunities.win_rate","What it measures":"Salesforce's deal win rate for the month. Written to crm.deals.win_rate instead when this connection is the primary source.","Type":"percentage","Unit":"%","Fed by":"Salesforce"},{"Key":"salesforce.opportunities.won_count","What it measures":"Salesforce's won deals for the month. Written to crm.deals.won_count instead when this connection is the primary source.","Type":"count","Unit":"deals","Fed by":"Salesforce"},{"Key":"salesforce.opportunities.won_value","What it measures":"Salesforce's won deal value for the month. Written to crm.deals.won_value instead when this connection is the primary source.","Type":"currency","Unit":"USD","Fed by":"Salesforce"}]},{"title":"GitHub","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"github.copilot.acceptance_rate","What it measures":"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.","Type":"percentage","Unit":"%","Fed by":"GitHub"},{"Key":"github.copilot.active_users_avg","What it measures":"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.","Type":"average","Unit":null,"Fed by":"GitHub"},{"Key":"github.copilot.lines_accepted","What it measures":"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.","Type":"count","Unit":null,"Fed by":"GitHub"},{"Key":"github.copilot.suggestions_accepted","What it measures":"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.","Type":"count","Unit":null,"Fed by":"GitHub"},{"Key":"github.copilot.suggestions_shown","What it measures":"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.","Type":"count","Unit":"suggestions","Fed by":"GitHub"},{"Key":"github.cost.copilot_spend","What it measures":"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.","Type":"currency","Unit":"USD","Fed by":"GitHub"},{"Key":"github.cost.product_spend","What it measures":"GitHub spend on one product (Actions, Copilot, Packages, …) for the month; the product is in the observation's dimensions.","Type":"currency","Unit":"USD","Fed by":"GitHub"},{"Key":"github.cost.total_spend","What it measures":"GitHub's cost total spend for the month.","Type":"currency","Unit":"USD","Fed by":"GitHub"},{"Key":"github.deployments.count","What it measures":"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.","Type":"count","Unit":"deployments","Fed by":"GitHub"},{"Key":"github.pull_requests.additions_avg","What it measures":"GitHub's pull requests additions avg for the month.","Type":"average","Unit":null,"Fed by":"GitHub"},{"Key":"github.pull_requests.cycle_time_avg","What it measures":"GitHub's pull requests cycle time avg for the month.","Type":"duration","Unit":null,"Fed by":"GitHub"},{"Key":"github.pull_requests.merged_count","What it measures":"GitHub's pull requests merged count for the month.","Type":"count","Unit":null,"Fed by":"GitHub"}]},{"title":"Hospitality","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"hospitality.bookings.direct_share","What it measures":"Share of bookings made through direct channels.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hospitality.food_beverage.cost_of_goods","What it measures":"Food and beverage cost of goods in the period.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hospitality.food_beverage.prep_hours","What it measures":"Kitchen prep hours in the period.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hospitality.food_beverage.waste_share","What it measures":"Food cost lost to waste and spoilage as a percentage of food and beverage cost of goods.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hospitality.guests.ancillary_spend_per_guest","What it measures":"Average ancillary spend per guest.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hospitality.guests.count","What it measures":"Guests in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hospitality.housekeeping.minutes_per_room_turn","What it measures":"Housekeeping labor minutes per room turn.","Type":"duration","Unit":"minutes","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hospitality.housekeeping.rooms_turned","What it measures":"Room turns (checkouts and cleans) in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hospitality.revenue_management.hours_per_week","What it measures":"Revenue manager hours per week on rate setting and reporting.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hospitality.rooms.available_room_nights","What it measures":"Available room-nights in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hospitality.rooms.revenue","What it measures":"Total room revenue across all channels in the period.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"hospitality.rooms.revpar","What it measures":"Revenue per available room.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"Operations","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"operations.applications.processed_count","What it measures":"Applications reviewed and decided in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"operations.documents.data_entry_minutes_per_document","What it measures":"Staff minutes to key the data from one document.","Type":"duration","Unit":"minutes","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"operations.documents.extraction_accuracy","What it measures":"Percentage of extracted fields that are correct without human correction.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"operations.documents.processed_count","What it measures":"Documents (PDFs, forms, emails) processed by the extraction pipeline in the period.","Type":"count","Unit":"documents","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"operations.forecasting.accuracy","What it measures":"Accuracy of the operating forecast (cash flow, demand or pipeline) against actuals.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"operations.forecasting.cycles","What it measures":"Forecast cycles run in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"operations.forecasting.hours_per_cycle","What it measures":"Staff hours to produce one complete forecast cycle.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"operations.processing.error_rate","What it measures":"Percentage of processed items that contained an error or needed manual correction.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"operations.reporting.delivery_lag_days","What it measures":"Days from period close to delivery of the period's report.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"operations.reporting.distribution_lag_hours","What it measures":"Hours between data availability and report delivery to stakeholders.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"operations.reporting.hours_per_week","What it measures":"Staff hours per week spent compiling and distributing reports by hand.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"operations.reporting.reports_automated","What it measures":"Distinct report types fully automated and removed from manual production.","Type":"count","Unit":"reports","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"Retail","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"retail.customers.active_count","What it measures":"Active customers in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"retail.customers.repeat_purchase_rate","What it measures":"Percentage of customers making a second purchase within twelve months.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"retail.customers.retention_rate","What it measures":"Twelve-month customer retention rate.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"retail.inventory.accuracy","What it measures":"Percentage of inventory records matching physical stock.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"retail.inventory.markdown_eligible_sold_value","What it measures":"Full retail value of the seasonal or clearance-eligible stock that sold or cleared in the period, at target price or marked down.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"retail.inventory.sell_through_rate","What it measures":"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.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"retail.orders.avg_value","What it measures":"Average order value.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"retail.orders.count","What it measures":"Orders placed in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"retail.pricing.gross_margin","What it measures":"Gross margin as a percentage of revenue.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"retail.pricing.markdown_rate","What it measures":"Percentage of the period's sales, by full retail value, made at a markdown price.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"retail.web.conversion_rate","What it measures":"Orders as a percentage of website sessions.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"retail.web.session_count","What it measures":"Website sessions in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"Education","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"education.admissions.admitted_count","What it measures":"Applicants offered admission in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"education.admissions.applicant_count","What it measures":"Applicants in the enrollment cycle.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"education.admissions.decision_days","What it measures":"Average days from application received to decision.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"education.admissions.review_minutes_per_application","What it measures":"Average staff minutes of hands-on work to review and process one application — not the days it waits for a decision.","Type":"duration","Unit":"minutes","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"education.admissions.yield_rate","What it measures":"Percentage of admitted students who enroll.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"education.courses.dfw_rate","What it measures":"Percentage of students earning a D or F or withdrawing.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"education.enrollment.retention_rate","What it measures":"Year-over-year student retention rate.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"education.enrollment.student_count","What it measures":"Students enrolled in the affected courses or programs.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"education.faculty.admin_hours_per_week","What it measures":"Faculty hours per week on course administration.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"education.faculty.count","What it measures":"Faculty using AI course-operations tools.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"education.tutoring.cost_per_student","What it measures":"Cost of tutoring services per enrolled student in the period.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"Energy","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"energy.assets.deferred_capex","What it measures":"Asset replacement spend deferred by extending asset life.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"energy.assets.unplanned_outages_per_year","What it measures":"Unplanned critical-asset outage events per year.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"energy.grid.reserve_capacity_mw","What it measures":"Reserve generation capacity held, in megawatts.","Type":"count","Unit":"MW","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"energy.load_forecasting.mape","What it measures":"Mean absolute percentage error of the load forecast.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"energy.rate_cases.count","What it measures":"Rate cases and major regulatory filings prepared in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"energy.rate_cases.external_counsel_hours","What it measures":"Outside counsel hours billed for a rate case or major regulatory filing.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"energy.rate_cases.prep_hours","What it measures":"Internal staff hours to prepare a rate case or major regulatory filing.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"energy.reliability.saidi_minutes","What it measures":"System Average Interruption Duration Index, in outage minutes per customer per year.","Type":"duration","Unit":"minutes","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"energy.storms.call_volume_index","What it measures":"Inbound call volume during a typical major storm event.","Type":"count","Unit":"index","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"energy.storms.events_per_year","What it measures":"Major storm or high-call-volume outage events per year.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"energy.storms.notification_hours_per_event","What it measures":"Staff hours spent communicating outage status to customers during one major storm event.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"OpenAI","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"openai.usage.cost_per_million_output_tokens","What it measures":"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.","Type":"currency","Unit":"USD","Fed by":"OpenAI"},{"Key":"openai.usage.cost_per_request","What it measures":"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.","Type":"currency","Unit":"USD","Fed by":"OpenAI"},{"Key":"openai.usage.input_tokens","What it measures":"OpenAI's ai input tokens for the month. Written to ai.usage.input_tokens instead when this connection is the primary source.","Type":"count","Unit":"tokens","Fed by":"OpenAI"},{"Key":"openai.usage.model_input_tokens","What it measures":"Input tokens sent to one OpenAI model in the month; the model is in the observation's dimensions.","Type":"count","Unit":"tokens","Fed by":"OpenAI"},{"Key":"openai.usage.model_output_tokens","What it measures":"Output tokens generated by one OpenAI model in the month; the model is in the observation's dimensions.","Type":"count","Unit":"tokens","Fed by":"OpenAI"},{"Key":"openai.usage.model_request_count","What it measures":"API requests to one OpenAI model in the month; the model is in the observation's dimensions.","Type":"count","Unit":"requests","Fed by":"OpenAI"},{"Key":"openai.usage.model_total_tokens","What it measures":"Input plus output tokens for one OpenAI model in the month; the model is in the observation's dimensions.","Type":"count","Unit":"tokens","Fed by":"OpenAI"},{"Key":"openai.usage.output_tokens","What it measures":"OpenAI's ai output tokens for the month. Written to ai.usage.output_tokens instead when this connection is the primary source.","Type":"count","Unit":"tokens","Fed by":"OpenAI"},{"Key":"openai.usage.request_count","What it measures":"OpenAI's ai api requests for the month. Written to ai.usage.request_count instead when this connection is the primary source.","Type":"count","Unit":"requests","Fed by":"OpenAI"},{"Key":"openai.usage.spend","What it measures":"OpenAI's usage spend for the month.","Type":"currency","Unit":"USD","Fed by":"OpenAI"},{"Key":"openai.usage.total_tokens","What it measures":"OpenAI's ai total tokens for the month. Written to ai.usage.total_tokens instead when this connection is the primary source.","Type":"count","Unit":"tokens","Fed by":"OpenAI"}]},{"title":"Logistics","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"logistics.customer_service.wismo_contacts_per_month","What it measures":"Where-is-my-order inbound contacts per month.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"logistics.deliveries.on_time_rate","What it measures":"Percentage of deliveries completed within the promised window.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"logistics.drivers.count","What it measures":"Last-mile drivers in the measured operation.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"logistics.drivers.operating_hours","What it measures":"Driver operating hours in the period.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"logistics.drivers.stops_per_hour","What it measures":"Completed delivery stops per driver per labor hour.","Type":"count","Unit":"stops","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"logistics.fleet.annual_insurance_premium","What it measures":"Annual commercial auto and cargo insurance premium across the fleet.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"logistics.fleet.maintenance_hours_per_month","What it measures":"Technician hours per month on scheduled and reactive fleet maintenance.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"logistics.fleet.miles","What it measures":"Total fleet miles in the period.","Type":"count","Unit":"miles","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"logistics.fleet.roadside_events_per_year","What it measures":"Unplanned roadside breakdown events per year.","Type":"count","Unit":"events","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"logistics.safety.preventable_accidents_per_million_miles","What it measures":"Preventable accident frequency per million miles driven.","Type":"count","Unit":"accidents","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"Media","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"media.audience.avg_active_users","What it measures":"Average monthly active users on the service.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"media.audience.monthly_watch_hours_per_user","What it measures":"Average monthly watch hours per active user.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"media.localization.cost_per_episode","What it measures":"Average cost to localize one episode (dubbing, subtitling and QC).","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"media.localization.episodes_localized","What it measures":"Episodes localized into additional languages in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"media.post_production.days_per_episode","What it measures":"Days from picture lock to delivery per episode.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"media.post_production.episode_count","What it measures":"Episodes entering post-production in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"media.rights.admin_hours_per_week","What it measures":"Staff hours per week on rights tracking, metadata correction and catalog reconciliation.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"media.royalties.dispute_rate","What it measures":"Share of royalty statements requiring dispute or correction.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"media.royalties.statement_count","What it measures":"Royalty statements issued in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"AI","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"ai.cost.per_million_output_tokens","What it measures":"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.","Type":"currency","Unit":"USD","Fed by":"Anthropic, OpenAI"},{"Key":"ai.cost.per_request","What it measures":"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.","Type":"currency","Unit":"USD","Fed by":"OpenAI"},{"Key":"ai.cost.spend","What it measures":"Total spend on the primary AI provider in the period.","Type":"currency","Unit":"USD","Fed by":"Anthropic, GitHub, OpenAI"},{"Key":"ai.outputs.human_review_rate","What it measures":"Percentage of AI outputs routed to a human reviewer before being acted on.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"ai.usage.input_tokens","What it measures":"Total prompt/input tokens consumed across the primary AI provider in the period.","Type":"count","Unit":"tokens","Fed by":"Anthropic, Google Gemini, OpenAI"},{"Key":"ai.usage.output_tokens","What it measures":"Total completion/output tokens generated by the primary AI provider in the period.","Type":"count","Unit":"tokens","Fed by":"Anthropic, Google Gemini, OpenAI"},{"Key":"ai.usage.request_count","What it measures":"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.","Type":"count","Unit":"requests","Fed by":"Google Gemini, OpenAI"},{"Key":"ai.usage.total_tokens","What it measures":"Total tokens (input + output) used through the primary AI provider in the period.","Type":"count","Unit":"tokens","Fed by":"Anthropic, Google Gemini, OpenAI"}]},{"title":"Google Gemini","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"gemini.usage.input_tokens","What it measures":"Google Gemini's ai input tokens for the month. Written to ai.usage.input_tokens instead when this connection is the primary source.","Type":"count","Unit":"tokens","Fed by":"Google Gemini"},{"Key":"gemini.usage.model_input_tokens","What it measures":"Input tokens sent to one Gemini model in the month; the model is in the observation's dimensions.","Type":"count","Unit":"tokens","Fed by":"Google Gemini"},{"Key":"gemini.usage.model_output_tokens","What it measures":"Output tokens generated by one Gemini model in the month; the model is in the observation's dimensions.","Type":"count","Unit":"tokens","Fed by":"Google Gemini"},{"Key":"gemini.usage.model_request_count","What it measures":"API requests to one Gemini model in the month; the model is in the observation's dimensions.","Type":"count","Unit":"requests","Fed by":"Google Gemini"},{"Key":"gemini.usage.model_total_tokens","What it measures":"Input plus output tokens for one Gemini model in the month; the model is in the observation's dimensions.","Type":"count","Unit":"tokens","Fed by":"Google Gemini"},{"Key":"gemini.usage.output_tokens","What it measures":"Google Gemini's ai output tokens for the month. Written to ai.usage.output_tokens instead when this connection is the primary source.","Type":"count","Unit":"tokens","Fed by":"Google Gemini"},{"Key":"gemini.usage.request_count","What it measures":"Google Gemini's ai api requests for the month. Written to ai.usage.request_count instead when this connection is the primary source.","Type":"count","Unit":"requests","Fed by":"Google Gemini"},{"Key":"gemini.usage.total_tokens","What it measures":"Google Gemini's ai total tokens for the month. Written to ai.usage.total_tokens instead when this connection is the primary source.","Type":"count","Unit":"tokens","Fed by":"Google Gemini"}]},{"title":"NetSuite","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"netsuite.financials.expenses","What it measures":"NetSuite's expenses for the month. Written to finance.pnl.expenses instead when this connection is the primary source.","Type":"currency","Unit":"USD","Fed by":"NetSuite"},{"Key":"netsuite.financials.net_income","What it measures":"NetSuite's net income for the month. Written to finance.pnl.net_income instead when this connection is the primary source.","Type":"currency","Unit":"USD","Fed by":"NetSuite"},{"Key":"netsuite.financials.revenue","What it measures":"NetSuite's revenue for the month. Written to finance.pnl.revenue instead when this connection is the primary source.","Type":"currency","Unit":"USD","Fed by":"NetSuite"},{"Key":"netsuite.invoices.count","What it measures":"NetSuite's invoices created for the month. Written to finance.invoices.count instead when this connection is the primary source.","Type":"count","Unit":"invoices","Fed by":"NetSuite"},{"Key":"netsuite.invoices.paid_count","What it measures":"NetSuite's invoices paid for the month. Written to finance.invoices.paid_count instead when this connection is the primary source.","Type":"count","Unit":"invoices","Fed by":"NetSuite"},{"Key":"netsuite.invoices.volume","What it measures":"NetSuite's invoice volume for the month. Written to finance.invoices.volume instead when this connection is the primary source.","Type":"currency","Unit":"USD","Fed by":"NetSuite"},{"Key":"netsuite.vendor_bills.count","What it measures":"NetSuite's bills received for the month. Written to finance.bills.count instead when this connection is the primary source.","Type":"count","Unit":"bills","Fed by":"NetSuite"},{"Key":"netsuite.vendor_bills.volume","What it measures":"NetSuite's bill volume for the month. Written to finance.bills.volume instead when this connection is the primary source.","Type":"currency","Unit":"USD","Fed by":"NetSuite"}]},{"title":"Pipedrive","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"pipedrive.deals.avg_sales_cycle_days","What it measures":"Pipedrive's avg sales cycle for the month. Written to crm.deals.avg_sales_cycle_days instead when this connection is the primary source.","Type":"duration","Unit":"days","Fed by":"Pipedrive"},{"Key":"pipedrive.deals.avg_value","What it measures":"Pipedrive's average won deal size for the month. Written to crm.deals.avg_value instead when this connection is the primary source.","Type":"currency","Unit":"USD","Fed by":"Pipedrive"},{"Key":"pipedrive.deals.lost_count","What it measures":"Pipedrive's lost deals for the month. Written to crm.deals.lost_count instead when this connection is the primary source.","Type":"count","Unit":"deals","Fed by":"Pipedrive"},{"Key":"pipedrive.deals.open_count","What it measures":"Deals open in Pipedrive at sync time, a snapshot rather than a period total, written once per month and rewritten on every sync.","Type":"count","Unit":null,"Fed by":"Pipedrive"},{"Key":"pipedrive.deals.open_value","What it measures":"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.","Type":"currency","Unit":"USD","Fed by":"Pipedrive"},{"Key":"pipedrive.deals.win_rate","What it measures":"Pipedrive's deal win rate for the month. Written to crm.deals.win_rate instead when this connection is the primary source.","Type":"percentage","Unit":"%","Fed by":"Pipedrive"},{"Key":"pipedrive.deals.won_count","What it measures":"Pipedrive's won deals for the month. Written to crm.deals.won_count instead when this connection is the primary source.","Type":"count","Unit":"deals","Fed by":"Pipedrive"},{"Key":"pipedrive.deals.won_value","What it measures":"Pipedrive's won deal value for the month. Written to crm.deals.won_value instead when this connection is the primary source.","Type":"currency","Unit":"USD","Fed by":"Pipedrive"}]},{"title":"QuickBooks","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"quickbooks.bills.count","What it measures":"QuickBooks's bills received for the month. Written to finance.bills.count instead when this connection is the primary source.","Type":"count","Unit":"bills","Fed by":"QuickBooks"},{"Key":"quickbooks.bills.volume","What it measures":"QuickBooks's bill volume for the month. Written to finance.bills.volume instead when this connection is the primary source.","Type":"currency","Unit":"USD","Fed by":"QuickBooks"},{"Key":"quickbooks.financials.expenses","What it measures":"QuickBooks's expenses for the month. Written to finance.pnl.expenses instead when this connection is the primary source.","Type":"currency","Unit":"USD","Fed by":"QuickBooks"},{"Key":"quickbooks.financials.net_income","What it measures":"QuickBooks's net income for the month. Written to finance.pnl.net_income instead when this connection is the primary source.","Type":"currency","Unit":"USD","Fed by":"QuickBooks"},{"Key":"quickbooks.financials.revenue","What it measures":"QuickBooks's revenue for the month. Written to finance.pnl.revenue instead when this connection is the primary source.","Type":"currency","Unit":"USD","Fed by":"QuickBooks"},{"Key":"quickbooks.invoices.count","What it measures":"QuickBooks's invoices created for the month. Written to finance.invoices.count instead when this connection is the primary source.","Type":"count","Unit":"invoices","Fed by":"QuickBooks"},{"Key":"quickbooks.invoices.paid_count","What it measures":"QuickBooks's invoices paid for the month. Written to finance.invoices.paid_count instead when this connection is the primary source.","Type":"count","Unit":"invoices","Fed by":"QuickBooks"},{"Key":"quickbooks.invoices.volume","What it measures":"QuickBooks's invoice volume for the month. Written to finance.invoices.volume instead when this connection is the primary source.","Type":"currency","Unit":"USD","Fed by":"QuickBooks"}]},{"title":"Xero","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"xero.bills.count","What it measures":"Xero's bills received for the month. Written to finance.bills.count instead when this connection is the primary source.","Type":"count","Unit":"bills","Fed by":"Xero"},{"Key":"xero.bills.volume","What it measures":"Xero's bill volume for the month. Written to finance.bills.volume instead when this connection is the primary source.","Type":"currency","Unit":"USD","Fed by":"Xero"},{"Key":"xero.financials.expenses","What it measures":"Xero's expenses for the month. Written to finance.pnl.expenses instead when this connection is the primary source.","Type":"currency","Unit":"USD","Fed by":"Xero"},{"Key":"xero.financials.net_income","What it measures":"Xero's net income for the month. Written to finance.pnl.net_income instead when this connection is the primary source.","Type":"currency","Unit":"USD","Fed by":"Xero"},{"Key":"xero.financials.revenue","What it measures":"Xero's revenue for the month. Written to finance.pnl.revenue instead when this connection is the primary source.","Type":"currency","Unit":"USD","Fed by":"Xero"},{"Key":"xero.invoices.count","What it measures":"Xero's invoices created for the month. Written to finance.invoices.count instead when this connection is the primary source.","Type":"count","Unit":"invoices","Fed by":"Xero"},{"Key":"xero.invoices.paid_count","What it measures":"Xero's invoices paid for the month. Written to finance.invoices.paid_count instead when this connection is the primary source.","Type":"count","Unit":"invoices","Fed by":"Xero"},{"Key":"xero.invoices.volume","What it measures":"Xero's invoice volume for the month. Written to finance.invoices.volume instead when this connection is the primary source.","Type":"currency","Unit":"USD","Fed by":"Xero"}]},{"title":"Billing","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"billing.charges.count","What it measures":"Charges in the period that kept revenue, in the currency the account settles in.","Type":"count","Unit":"charges","Fed by":"Stripe"},{"Key":"billing.charges.volume","What it measures":"What the period's charges kept, captured less refunded, tax included, in the currency the account settles in.","Type":"currency","Unit":"USD","Fed by":"Stripe"},{"Key":"billing.subscriptions.active_count","What it measures":"Subscriptions active at the end of the period.","Type":"count","Unit":"subscriptions","Fed by":"Stripe"},{"Key":"billing.subscriptions.churn_rate","What it measures":"Subscriptions canceled in the period as a percentage of those active at its start. Absent for a period with nothing active at the start.","Type":"percentage","Unit":"%","Fed by":"Stripe"},{"Key":"billing.subscriptions.churned_count","What it measures":"Subscriptions canceled in the period.","Type":"count","Unit":"subscriptions","Fed by":"Stripe"},{"Key":"billing.subscriptions.mrr","What it measures":"Monthly recurring revenue from subscriptions active at the end of the period.","Type":"currency","Unit":"USD","Fed by":"Stripe"},{"Key":"billing.subscriptions.new_count","What it measures":"Subscriptions created in the period.","Type":"count","Unit":"subscriptions","Fed by":"Stripe"}]},{"title":"Engineering","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"engineering.coding_assistant.acceptance_rate","What it measures":"Accepted suggestions as a share of the suggestions offered in the period.","Type":"percentage","Unit":"%","Fed by":"Anthropic, Claude Enterprise, GitHub"},{"Key":"engineering.coding_assistant.acceptances","What it measures":"Suggestions developers accepted from an AI coding assistant in the period.","Type":"count","Unit":"acceptances","Fed by":"Anthropic, Claude Enterprise, GitHub"},{"Key":"engineering.coding_assistant.active_users","What it measures":"Developers using an AI coding assistant on an average day in the period. Summed across assistants when read combined.","Type":"average","Unit":"users","Fed by":"Anthropic, Claude Enterprise, GitHub"},{"Key":"engineering.coding_assistant.lines_accepted","What it measures":"Lines of code developers accepted from an AI coding assistant in the period.","Type":"count","Unit":"lines","Fed by":"Anthropic, Claude Enterprise, GitHub, OpenAI Codex"},{"Key":"engineering.coding_assistant.suggestions","What it measures":"Code suggestions an AI coding assistant offered developers in the period.","Type":"count","Unit":"suggestions","Fed by":"Anthropic, Claude Enterprise, GitHub"},{"Key":"engineering.pull_requests.cycle_time_avg","What it measures":"Average hours from a pull request being opened to being merged, over the pull requests merged in the period.","Type":"duration","Unit":"hours","Fed by":"GitHub"},{"Key":"engineering.pull_requests.merged_count","What it measures":"Pull requests merged in the period.","Type":"count","Unit":"pull requests","Fed by":"GitHub"}]},{"title":"Government","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"government.cases.decision_days","What it measures":"Average days from case intake to eligibility decision.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"government.cases.processed_count","What it measures":"Benefit applications and cases processed in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"government.cases.staff_hours_per_case","What it measures":"Average caseworker hours of hands-on work per case decided — not the days it waits for a decision.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"government.constituent_services.calls_per_month","What it measures":"Inbound constituent contacts per month.","Type":"count","Unit":"calls","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"government.permits.decision_days","What it measures":"Average days from permit intake to decision.","Type":"duration","Unit":"days","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"government.permits.processed_count","What it measures":"Permit applications processed in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"government.permits.staff_hours_per_permit","What it measures":"Average staff hours of hands-on work per permit decided — not the days it waits for a decision.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"Stripe","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"stripe.charges.count","What it measures":"Stripe's charges for the month that kept revenue, in the currency the account settles in.","Type":"count","Unit":null,"Fed by":"Stripe"},{"Key":"stripe.charges.volume","What it measures":"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.","Type":"currency","Unit":"USD","Fed by":"Stripe"},{"Key":"stripe.subscriptions.active_count","What it measures":"Stripe's active subscriptions for the month. Written to billing.subscriptions.active_count instead when this connection is the primary source.","Type":"count","Unit":"subscriptions","Fed by":"Stripe"},{"Key":"stripe.subscriptions.churn_rate","What it measures":"Stripe's subscription churn rate for the month. Written to billing.subscriptions.churn_rate instead when this connection is the primary source.","Type":"percentage","Unit":"%","Fed by":"Stripe"},{"Key":"stripe.subscriptions.churned_count","What it measures":"Stripe's churned subscriptions for the month. Written to billing.subscriptions.churned_count instead when this connection is the primary source.","Type":"count","Unit":"subscriptions","Fed by":"Stripe"},{"Key":"stripe.subscriptions.mrr","What it measures":"Stripe's monthly recurring revenue for the month. Written to billing.subscriptions.mrr instead when this connection is the primary source.","Type":"currency","Unit":"USD","Fed by":"Stripe"},{"Key":"stripe.subscriptions.new_count","What it measures":"Stripe's new subscriptions for the month. Written to billing.subscriptions.new_count instead when this connection is the primary source.","Type":"count","Unit":"subscriptions","Fed by":"Stripe"}]},{"title":"Telecom","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"telecom.customer_care.calls_per_month","What it measures":"Inbound customer care calls per month.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"telecom.customer_care.cost_per_subscriber","What it measures":"Customer care cost per subscriber per period.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"telecom.field_service.truck_rolls_per_month","What it measures":"Field dispatch events per month.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"telecom.network.incident_count","What it measures":"Network incidents in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"telecom.network.mttr_minutes","What it measures":"Mean minutes to recover from a network incident.","Type":"duration","Unit":"minutes","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"telecom.retention.cost_per_retained_subscriber","What it measures":"Average cost of retention offers per subscriber retained.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"telecom.retention.retained_subscribers","What it measures":"Subscribers retained through save offers in the period.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"Claude Enterprise","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"claude_enterprise.claude_code.acceptance_rate","What it measures":"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.","Type":"percentage","Unit":"%","Fed by":"Claude Enterprise"},{"Key":"claude_enterprise.claude_code.acceptances","What it measures":"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.","Type":"count","Unit":"acceptances","Fed by":"Claude Enterprise"},{"Key":"claude_enterprise.claude_code.active_users_avg","What it measures":"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.","Type":"average","Unit":"users","Fed by":"Claude Enterprise"},{"Key":"claude_enterprise.claude_code.lines_accepted","What it measures":"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.","Type":"count","Unit":"lines","Fed by":"Claude Enterprise"},{"Key":"claude_enterprise.claude_code.proposals","What it measures":"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.","Type":"count","Unit":"suggestions","Fed by":"Claude Enterprise"}]},{"title":"Customer success","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"customer_success.accounts.csm_count","What it measures":"Customer success managers covering the accounts.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"customer_success.accounts.per_csm","What it measures":"Customer accounts managed per customer success manager.","Type":"count","Unit":null,"Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"customer_success.renewals.arr_up_for_renewal","What it measures":"Annual recurring revenue from accounts up for renewal in the period.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"customer_success.renewals.rate","What it measures":"Percentage of customers up for renewal who renewed.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"customer_success.revenue.net_retention_rate","What it measures":"Revenue retained from existing customers including expansion, as a percentage of the starting base.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"Intercom","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"intercom.conversations.avg_first_response_hours","What it measures":"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.","Type":"duration","Unit":"hours","Fed by":"Intercom"},{"Key":"intercom.conversations.avg_resolution_days","What it measures":"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.","Type":"duration","Unit":"days","Fed by":"Intercom"},{"Key":"intercom.conversations.closed_count","What it measures":"Intercom's support tickets closed for the month. Written to support.tickets.closed_count instead when this connection is the primary source.","Type":"count","Unit":"tickets","Fed by":"Intercom"},{"Key":"intercom.conversations.csat_score","What it measures":"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.","Type":"score","Unit":"0–1","Fed by":"Intercom"},{"Key":"intercom.conversations.volume","What it measures":"Intercom's support tickets opened for the month. Written to support.tickets.volume instead when this connection is the primary source.","Type":"count","Unit":"tickets","Fed by":"Intercom"}]},{"title":"Snowflake","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"snowflake.compute.credits_used","What it measures":"Snowflake warehouse credits consumed in the month.","Type":"count","Unit":"credits","Fed by":"Snowflake"},{"Key":"snowflake.cost.service_spend","What it measures":"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.","Type":"currency","Unit":"USD","Fed by":"Snowflake"},{"Key":"snowflake.cost.total_spend","What it measures":"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.","Type":"currency","Unit":"USD","Fed by":"Snowflake"},{"Key":"snowflake.query.avg_execution_seconds","What it measures":"Mean execution time of Snowflake queries in the month.","Type":"duration","Unit":"seconds","Fed by":"Snowflake"},{"Key":"snowflake.query.count","What it measures":"Successful Snowflake queries in the month.","Type":"count","Unit":"queries","Fed by":"Snowflake"}]},{"title":"Zendesk","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"zendesk.tickets.avg_first_response_hours","What it measures":"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.","Type":"duration","Unit":"hours","Fed by":"Zendesk"},{"Key":"zendesk.tickets.avg_handle_time","What it measures":"Zendesk's avg ticket handle time for the month. Written to support.tickets.avg_handle_time instead when this connection is the primary source.","Type":"duration","Unit":"hours","Fed by":"Zendesk"},{"Key":"zendesk.tickets.csat_score","What it measures":"Zendesk's ticket csat for the month. Written to support.tickets.csat_score instead when this connection is the primary source.","Type":"score","Unit":"0–1","Fed by":"Zendesk"},{"Key":"zendesk.tickets.solved_count","What it measures":"Zendesk's support tickets closed for the month. Written to support.tickets.closed_count instead when this connection is the primary source.","Type":"count","Unit":"tickets","Fed by":"Zendesk"},{"Key":"zendesk.tickets.volume","What it measures":"Zendesk's support tickets opened for the month. Written to support.tickets.volume instead when this connection is the primary source.","Type":"count","Unit":"tickets","Fed by":"Zendesk"}]},{"title":"Calendar","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"calendar.meetings.avg_attendees","What it measures":"Average attendees per meeting in the period on the calendars this connection syncs.","Type":"average","Unit":"people","Fed by":"Google Workspace, Microsoft 365"},{"Key":"calendar.meetings.avg_duration_mins","What it measures":"Average length in minutes of a meeting on the calendars this connection syncs.","Type":"duration","Unit":"minutes","Fed by":"Google Workspace, Microsoft 365"},{"Key":"calendar.meetings.count","What it measures":"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.","Type":"count","Unit":"meetings","Fed by":"Google Workspace, Microsoft 365"},{"Key":"calendar.meetings.hours","What it measures":"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.","Type":"duration","Unit":"hours","Fed by":"Google Workspace, Microsoft 365"}]},{"title":"Cloud","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"cloud.cost.budget_amount","What it measures":"Cloud budget ceiling for the period, across providers.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"cloud.cost.forecasted_spend","What it measures":"Forecast end-of-period cloud spend based on the current usage trajectory.","Type":"currency","Unit":"USD","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"cloud.cost.total_spend","What it measures":"Total cloud infrastructure spend for the period on the primary provider.","Type":"currency","Unit":"USD","Fed by":"AWS, Azure, Databricks, Google Cloud, Snowflake"},{"Key":"cloud.resources.idle_share","What it measures":"Percentage of provisioned cloud resources running at low or zero utilization.","Type":"percentage","Unit":"%","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"Freshdesk","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"freshdesk.tickets.avg_first_response_hours","What it measures":"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.","Type":"duration","Unit":"hours","Fed by":"Freshdesk"},{"Key":"freshdesk.tickets.avg_handle_time","What it measures":"Freshdesk's avg ticket handle time for the month. Written to support.tickets.avg_handle_time instead when this connection is the primary source.","Type":"duration","Unit":"hours","Fed by":"Freshdesk"},{"Key":"freshdesk.tickets.resolved_count","What it measures":"Freshdesk's support tickets closed for the month. Written to support.tickets.closed_count instead when this connection is the primary source.","Type":"count","Unit":"tickets","Fed by":"Freshdesk"},{"Key":"freshdesk.tickets.volume","What it measures":"Freshdesk's support tickets opened for the month. Written to support.tickets.volume instead when this connection is the primary source.","Type":"count","Unit":"tickets","Fed by":"Freshdesk"}]},{"title":"Google Workspace","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"google_workspace.calendar.avg_attendees","What it measures":"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.","Type":"average","Unit":"people","Fed by":"Google Workspace"},{"Key":"google_workspace.calendar.avg_meeting_duration_mins","What it measures":"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.","Type":"duration","Unit":"minutes","Fed by":"Google Workspace"},{"Key":"google_workspace.calendar.meeting_count","What it measures":"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.","Type":"count","Unit":"meetings","Fed by":"Google Workspace"},{"Key":"google_workspace.calendar.meeting_hours","What it measures":"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.","Type":"duration","Unit":"hours","Fed by":"Google Workspace"}]},{"title":"Make","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"make.scenarios.credits","What it measures":"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.","Type":"count","Unit":"credits","Fed by":"Make"},{"Key":"make.scenarios.executions","What it measures":"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.","Type":"count","Unit":"runs","Fed by":"Make"},{"Key":"make.scenarios.failed_executions","What it measures":"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.","Type":"count","Unit":"runs","Fed by":"Make"},{"Key":"make.scenarios.successful_executions","What it measures":"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.","Type":"count","Unit":"runs","Fed by":"Make"}]},{"title":"Microsoft 365","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"microsoft_365.calendar.avg_attendees","What it measures":"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.","Type":"average","Unit":"people","Fed by":"Microsoft 365"},{"Key":"microsoft_365.calendar.avg_meeting_duration_mins","What it measures":"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.","Type":"duration","Unit":"minutes","Fed by":"Microsoft 365"},{"Key":"microsoft_365.calendar.meeting_count","What it measures":"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.","Type":"count","Unit":"meetings","Fed by":"Microsoft 365"},{"Key":"microsoft_365.calendar.meeting_hours","What it measures":"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.","Type":"duration","Unit":"hours","Fed by":"Microsoft 365"}]},{"title":"Workflow automation","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"automation.runs.count","What it measures":"Runs of automated workflows that finished in the period, successful and failed.","Type":"count","Unit":"runs","Fed by":"Make, Zapier, n8n"},{"Key":"automation.runs.failed_count","What it measures":"Runs of automated workflows that failed in the period.","Type":"count","Unit":"runs","Fed by":"Make, Zapier, n8n"},{"Key":"automation.runs.successful_count","What it measures":"Runs of automated workflows that succeeded in the period.","Type":"count","Unit":"runs","Fed by":"Make, Zapier, n8n"}]},{"title":"BigQuery","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"bigquery.jobs.bytes_processed","What it measures":"Bytes processed by BigQuery jobs in the month.","Type":"count","Unit":"bytes","Fed by":"BigQuery"},{"Key":"bigquery.jobs.count","What it measures":"BigQuery jobs run in the month.","Type":"count","Unit":"jobs","Fed by":"BigQuery"},{"Key":"bigquery.jobs.slot_ms","What it measures":"Slot milliseconds consumed by BigQuery jobs in the month.","Type":"count","Unit":"slot-ms","Fed by":"BigQuery"}]},{"title":"Google Cloud","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"gcp.billing.budget_amount","What it measures":"The amount of one GCP budget for the month; the budget is in the row's raw payload.","Type":"currency","Unit":"USD","Fed by":"Google Cloud"},{"Key":"gcp.billing.budget_spend","What it measures":"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.","Type":"currency","Unit":"USD","Fed by":"Google Cloud"},{"Key":"gcp.billing.total_spend","What it measures":"Google Cloud's billing total spend for the month.","Type":"currency","Unit":"USD","Fed by":"Google Cloud"}]},{"title":"Jira","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"jira.issues.cycle_time_avg","What it measures":"Jira's issues cycle time avg for the month. Read as work.issues.cycle_time_avg when Jira is the primary issue-tracking source.","Type":"duration","Unit":null,"Fed by":"Jira"},{"Key":"jira.issues.resolved_count","What it measures":"Jira's issues resolved count for the month. Read as work.issues.resolved_count when Jira is the primary issue-tracking source.","Type":"count","Unit":null,"Fed by":"Jira"},{"Key":"jira.issues.story_points_completed","What it measures":"Jira's issues story points completed for the month. Read as work.issues.story_points_completed when Jira is the primary issue-tracking source.","Type":"count","Unit":null,"Fed by":"Jira"}]},{"title":"n8n","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"n8n.workflows.executions","What it measures":"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.","Type":"count","Unit":"executions","Fed by":"n8n"},{"Key":"n8n.workflows.failed_executions","What it measures":"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.","Type":"count","Unit":"executions","Fed by":"n8n"},{"Key":"n8n.workflows.successful_executions","What it measures":"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.","Type":"count","Unit":"executions","Fed by":"n8n"}]},{"title":"OpenAI Codex","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"openai_codex.usage.lines_accepted","What it measures":"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.","Type":"count","Unit":"lines","Fed by":"OpenAI Codex"},{"Key":"openai_codex.usage.lines_committed","What it measures":"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.","Type":"count","Unit":"lines","Fed by":"OpenAI Codex"},{"Key":"openai_codex.usage.turns","What it measures":"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.","Type":"count","Unit":"turns","Fed by":"OpenAI Codex"}]},{"title":"Work","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"work.issues.cycle_time_avg","What it measures":"Average hours from an issue's start to its resolution, over the issues resolved in the period.","Type":"duration","Unit":"hours","Fed by":"Jira"},{"Key":"work.issues.resolved_count","What it measures":"Issues resolved in the period in the team's issue tracker.","Type":"count","Unit":"issues","Fed by":"Jira"},{"Key":"work.issues.story_points_completed","What it measures":"Story points on the issues resolved in the period.","Type":"count","Unit":"points","Fed by":"Jira"}]},{"title":"Workforce","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"workforce.outreach.hours_per_week","What it measures":"Staff hours per week spent identifying at-risk people and reaching out to them.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"workforce.tasks.manual_per_week","What it measures":"Discrete manual tasks (data pulls, formatting, distribution) performed each week.","Type":"count","Unit":"tasks","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"},{"Key":"workforce.time.hours_saved_per_week","What it measures":"Net staff hours recovered per week as a direct result of the initiative.","Type":"duration","Unit":"hours","Fed by":"Recorded by hand, imported, or read from your warehouse (BigQuery, Databricks or Snowflake)"}]},{"title":"Zapier","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"zapier.zaps.failed_runs","What it measures":"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.","Type":"count","Unit":"runs","Fed by":"Zapier"},{"Key":"zapier.zaps.runs","What it measures":"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.","Type":"count","Unit":"runs","Fed by":"Zapier"},{"Key":"zapier.zaps.successful_runs","What it measures":"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.","Type":"count","Unit":"runs","Fed by":"Zapier"}]},{"title":"AWS","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"aws.cost.service_spend","What it measures":"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.","Type":"currency","Unit":"USD","Fed by":"AWS"},{"Key":"aws.cost.total_spend","What it measures":"AWS's cost total spend for the month.","Type":"currency","Unit":"USD","Fed by":"AWS"}]},{"title":"Azure","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"azure.cost.service_spend","What it measures":"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.","Type":"currency","Unit":"USD","Fed by":"Azure"},{"Key":"azure.cost.total_spend","What it measures":"Azure's cost total spend for the month.","Type":"currency","Unit":"USD","Fed by":"Azure"}]},{"title":"Databricks","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"databricks.cost.product_spend","What it measures":"Databricks list-price spend for the month on one product (billing_origin_product: MODEL_SERVING, JOBS, SQL, ...).","Type":"currency","Unit":"USD","Fed by":"Databricks"},{"Key":"databricks.cost.total_spend","What it measures":"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.","Type":"currency","Unit":"USD","Fed by":"Databricks"}]},{"title":"Brex","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"brex.card.spend","What it measures":"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.","Type":"currency","Unit":"USD","Fed by":"Brex"}]},{"title":"Looker","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"looker.looks.field_value","What it measures":"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.","Type":"count","Unit":null,"Fed by":"Looker"}]},{"title":"Ramp","columns":["Key","What it measures","Type","Unit","Fed by"],"rows":[{"Key":"ramp.card.spend","What it measures":"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.","Type":"currency","Unit":"USD","Fed by":"Ramp"}]}]}