Dashboard
Property performance dashboard
Occupancy, ADR, RevPAR, and channel mix from PMS + booking.
- Connected to PMS, POS, booking channels, labor systems, and finance/CRM
- Thresholds and owners
- Drill to operational detail
Industry analytics
Hotels lose mornings matching PMS occupancy, channel pickups, POS covers, and labor hours that disagree on PropertyId/BusinessDate. Power BI Import models (gateway to on-prem PMS SQL when required) put RevPAR and labor cost on one night grain for the ops huddle.
Typical friction when CRM, ERP, and operational tools are reported separately.
What changes when those systems feed a governed Power BI model.
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Commercial considerations
Hospitality organizations already manage the business in PMS, POS, booking channels, labor systems, and finance/CRM. The reporting problem is usually not “we need another chart”—it is that CRM, ERP, and operational extracts disagree on timing, filters, and owners before every meeting.
Property managers live in the PMS while labor and F&B sit elsewhere—daily flash reports are still spreadsheet builds. Power BI for Hospitality only works when those systems of record feed one governed semantic model with documented KPI definitions.
Power BI Analytics scopes the first release around the hospitality decisions that currently wait on manual packs—then connects the right source systems, validates totals, and lands the report in a real operating cadence.
Built for leaders who need dependable numbers in real operating conversations—not another unused dashboard.
We establish a baseline before delivery so ROI is discussed in operating terms, not slideware.
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Measure hours currently spent assembling hospitality reporting packs, reconciling totals, and answering “which number is correct?”
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Track how many days earlier leaders can see exceptions, variances, or forecast risk after the release.
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Count disputed metrics, failed refreshes, and unmanaged workbooks replaced by governed models.
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Confirm the intended audience actually uses the report in the meeting or process it was designed for.
Property managers live in the PMS while labor and F&B sit elsewhere—daily flash reports are still spreadsheet builds. Power BI consulting connects these systems of record into one governed reporting model.
rooms, occupancy, ADR, RevPAR
covers, spend, outlet margin
OTAs, direct, mix
hours, labor cost %
P&L, guest satisfaction, loyalty
Revenue per available room from PMS.
Rooms sold as a share of rooms available.
Labor cost relative to revenue.
Experience scores by property or segment.
Recommended dashboards
Power BI for Hospitality patterns we tailor to your systems, owners, and review cadence. Click any preview to expand.
Dashboard
Occupancy, ADR, RevPAR, and channel mix from PMS + booking.
Dashboard
Covers, spend, margin, labor, and waste from POS + workforce.
Reporting only creates value when it lands in a real review rhythm.
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Buyer questions answered
Connect the systems you already use and leave with a clear place to report—before another orphan dashboard appears.
Build 1–2 live dashboard pages that pull from multiple sources—not a screenshot of Excel pasted into Power BI.
Retire the Monday email of pasted workbooks: scheduled refresh and one pack finance can open without stitching tabs.
Data Warehouse Design starts by killing contradictory extracts—then publishes measures that data leaders, IT managers, and executives can defend from multiple operational systems and historical data.
Store margin and sell-through—POS, web, and inventory on one SKU day grain: connect POS, ecommerce, ERP inventory, merchandising, and loyalty CRM so leaders manage sales, margin, availability, and customer demand.
NOI, occupancy, and collections from PMS + leasing + GL: connect property management, leasing CRM, accounting, and maintenance systems so leaders manage asset performance, leasing, portfolio risk, and cash flow.
Power BI for Logistics: Dashboards, Analytics & Implementation: connect TMS, WMS, telematics, ERP, and customer-service CRM so leaders manage OTIF, freight spend, dwell, and lane cost.
Talk through priorities, data sources, and a practical first Power BI release.