This is a completely random and anonymised data set
Analysis done through this dashboard
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No of calls
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Time between time of call and ambulance departure.
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Time taken for ambulance to arrive to patient.
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Time taken from patient house to hospital.
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No of jobs between specific period i.e over lunch times 12:00-14:00
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Does the despatch code affect the timings?
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Does the call handler have any impact?
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Do certain hospitals have issues in particular aspect
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What does the data say based upon the ambulance station?
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Does patient Sex affect handover times?
Report Snapshots
Data Model
Calculations
| MEASURE_NAME | EXPRESSION |
|---|---|
| Hospital Capacity | SUM ( dimHospital[Capacity] ) |
| No of Calls | COUNTROWS ( CallTimings ) |
| Hospital Name | SELECTEDVALUE ( dimHospital[Hospital Name] ) |
| Min Call Time | MIN (CallTimings[Length of Call (Mins)] ) |
| Max Call Time | MAX( (CallTimings[Length of Call (Mins)] )) |
| Average Dispatch Time (ADT) | AVERAGE ( CallTimings[Average Dispatch Time] ) |
| Min Date | FORMAT ( MIN ( DateTable[Date] ), "dd/mm/yyyy" ) |
| Max Date | FORMAT ( MAX ( DateTable[Date] ), "dd/mm/yyyy" ) |
| Female Calls | CALCULATE ( [No of Calls], dimPatient[Gender] = "Female" ) |
| Male Calls | CALCULATE ( [No of Calls], dimPatient[Gender] = "Male" ) |
| Average Ambulance Arrival Time (AAAT) | AVERAGE ( CallTimings[Average Ambulance Arrival Time] ) |
| Average Hospital Arrival Time (AHAT) | AVERAGE ( CallTimings[Average Hospital Arrival Time] ) |
| Patient Handover Time (PHT) | AVERAGE ( CallTimings[Average Hospital Handover Time] ) |
| Average Handling Time (AHT) | Average ( CallTimings[Length of Call (Mins)] ) |
This is a completely random and anonymised data set
Analysis done through this dashboard
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Time analysis exploration (Seasonality, Festive periods, weekday vs weekend etc)
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Agent’s performance, overview.
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Other insights providing further information on Customers type (family with children, single or couples visitors) etc.
Report Snapshots
Data Model
Calculations
| Name | Expression |
|---|---|
| Record Count | COUNTROWS( 'fct_Hotel Revenue' ) |
| Min Date | MIN( 'fct_Hotel Revenue'[Reservation Status Date] ) |
| Max Date | MAX( 'fct_Hotel Revenue'[Reservation Status Date] ) |
| Rev Rooms (Expected) | SUMX( 'fct_Hotel Revenue', 'fct_Hotel Revenue'[AVG Daily Rate] * 'fct_Hotel Revenue'[Nights (Tot)]) |
| Rev Meals (Expected) | SUMX( 'fct_Hotel Revenue', 'fct_Hotel Revenue'[Meal Cost]) |
| Total Revenue | [Rev Meals (Actual)] + [Rev Rooms (Actual)] |
| Total Nights Booked | sumx( 'fct_Hotel Revenue', 'fct_Hotel Revenue'[Nights (Tot)]) |
| Total Nights Stayed | CALCULATE( [Total Nights Booked] , FILTER( 'dim_Reservation Status', 'dim_Reservation Status'[Reservation Satus] = "Check-Out")) |
| % Cancellations/No Shows | ( [Total Nights Booked] - [Total Nights Stayed] ) / [Total Nights Booked] |
| Rev Rooms (Actual) | CALCULATE( [Rev Rooms (Expected)] , FILTER( 'fct_Hotel Revenue', OR( 'fct_Hotel Revenue'[Reservation Status Key] = 2 , 'fct_Hotel Revenue'[Deposit Type Key] = 2 ))) |
| Rev Meals (Actual) | CALCULATE( [Rev Meals (Expected)] , FILTER( 'fct_Hotel Revenue', OR( 'fct_Hotel Revenue'[Reservation Status Key] = 2 , 'fct_Hotel Revenue'[Deposit Type Key] = 2 ))) |
| Total Expected Revenue | [Rev Meals (Expected)] + [Rev Rooms (Expected)] |
| % Revenue Actual/Expected | [Total Revenue] / [Total Expected Revenue] |
| Revenue Wkly Moving Avg | VAR LastWeek = MAX( ) |
| Check-Ins | CALCULATE( [Record Count] , FILTER( 'dim_Reservation Status', 'dim_Reservation Status'[Reservation Satus] = "Check-Out")) |
| Total Guests | CALCULATE( SUMX('fct_Hotel Revenue', 'fct_Hotel Revenue'[Adults] + 'fct_Hotel Revenue'[Babies] + 'fct_Hotel Revenue'[Children] ), FILTER( 'dim_Reservation Status', 'dim_Reservation Status'[Reservation Satus] = "Check-Out")) |
| Average Daily Rate | DIVIDE( SUMX( 'fct_Hotel Revenue', 'fct_Hotel Revenue'[AVG Daily Rate] * 'fct_Hotel Revenue'[Nights (Tot)] ) , sumx( 'fct_Hotel Revenue', 'fct_Hotel Revenue'[Nights (Tot)] ) ) |
| % Rev from Meals | DIVIDE( [Rev Meals (Actual)] , [Total Revenue]) |
| Revenue 1W Moving Avg | AVERAGEX( DATESINPERIOD( Dates[Date], LASTDATE( Dates[Date] ), -7, DAY), [Total Revenue]) |
| Cancellations 1W Moving Avg | averageX(DATESINPERIOD( Dates[Date], LASTDATE( Dates[Date] ), -7, DAY), ( [Total Nights Booked] - [Total Nights Stayed] ) / [Total Nights Booked] ) |
| Bookings 1Wk Moving Tot | SUMX( DATESINPERIOD( Dates[Date], LASTDATE( Dates[Date] ), -7, DAY), [Total Nights Booked] ) |
| Cancellations 1M Moving Avg | VAR tot = sumX(DATESINPERIOD( Dates[Date], LASTDATE( Dates[Date] ), -1, MONTH), [Total Nights Booked] ) VAR stay = SUMX( DATESINPERIOD( Dates[Date], LASTDATE( Dates[Date] ), -1, MONTH), [Total Nights Stayed] ) RETURN (tot-stay)/tot |
| Check-Ins 1W Moving Avg | AVERAGEX( DATESINPERIOD( Dates[Date], LASTDATE( Dates[Date] ), -7, DAY), [Check-Ins]) |
| Guests 1W Moving Avg | AVERAGEX( DATESINPERIOD( Dates[Date], LASTDATE( Dates[Date] ), -7, DAY), [Total Guests]) |
| Daily Rate 1W Moving Avg | averageX(DATESINPERIOD( Dates[Date], LASTDATE( Dates[Date] ), -7, DAY), [Average Daily Rate] ) |
This is a completely random and anonymised data set
Analysis done through this dashboard
- Sales trends
- Cumulative Sales
- 7 day Moving Average
- Sales by Channel
- Sales by Sales teams
- Sales by Region and State
Report Snapshots
Data Model
Calculations
| MEASURE_NAME | EXPRESSION |
|---|---|
| Total Sales | SUMX( Sales , Sales[Unit Price] * Sales[Order Quantity] ) |
| Total Quantity Sold | SUM( Sales[Order Quantity] ) |
| Total Products Bought | DISTINCTCOUNT(Sales[Product Index]) |
| Total Costs | SUMX( Sales , Sales[Unit Cost] * Sales[Order Quantity] ) |
| Average Costs | AVERAGEX( Sales , Sales[Unit Cost] * Sales[Order Quantity] ) |
| Average Sales | AVERAGEX( Sales , Sales[Unit Price] * Sales[Order Quantity] ) |
| Total Profits | [Total Sales] - [Total Costs] |
| Profit Margin | DIVIDE( [Total Profits] , [Total Sales] , 0 ) |
| Total Transactions | COUNTROWS( Sales ) |
| Top 10 Cities by Profit | CALCULATE( [Total Profits] , FILTER( 'Store Locations' , 'Store Locations'[Top N Cities] = "Top 10" ) ) |
| Prev. Month Sales | CALCULATE( [Total Sales] , DATEADD( Dates[Date] , -1 , MONTH ) ) |
| Prev. Month Qty. Sold | CALCULATE( [Total Quantity Sold] , DATEADD( Dates[Date] , -1 , MONTH ) ) |
| Sales Target | [Prev. Month Sales] * 1.1 |
| Quantity Sold Target | [Prev. Month Qty. Sold] * 1.1 |
| Avg. Retail Price | AVERAGE( Sales[Unit Price] ) |
| Adjusted Retail Price | [Avg. Retail Price] * (1 + 'Price Adjustment (%)'[Price Adjustment (%) Value] ) |
| Adjusted Sales | SUMX( Sales , [Adjusted Retail Price] * Sales[Order Quantity] ) |
| Adjusted Profit | [Adjusted Sales] - [Total Costs] |
| % of Total Sales | DIVIDE( [Total Sales] , CALCULATE( [Total Sales] , ALL( Products[Product Name] ) ), 0 ) |
| Price Adjustment (%) Value | SELECTEDVALUE('Price Adjustment (%)'[Price Adjustment (%)], 0) |
This is a completely random and anonymised data set
Analysis done through this dashboard
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How many productive hours doyou work in a week?
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How many should you be doing
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How many hours am I doing on a particular project
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How effective is the resource? How is it performing?
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Utilization
Report Snapshots
Data Model
Calculations
| MEASURE_NAME | EXPRESSION |
|---|---|
| __Default measure | 1 |
| Duration Daily Average | AVERAGEX('Date', [Duration Sum]) |
| Duration Hour Minute | VAR _Hour = HOUR( SELECTEDVALUE( atWorkData[Duration]) ) // Find minute as proportion of hour VAR _Minute = DIVIDE(MINUTE( SELECTEDVALUE( atWorkData[Duration] ) ), 60, 0 ) // Add together VAR _Result = _Hour + _Minute RETURN _Result |
| Duration Sum | SUM(atWorkData[Duration]) |
| Total Business Hours | [Business Days] * 7.5 |
| Total Earnings | SUM(atWorkData[Earnings, $]) |
| Total Number of Fiscal Months | DISTINCTCOUNT( atWorkData[Year] ) * 12 |
| Total Billed Hours | SUM ( atWorkData[Hours Billed] ) |
| Business Days | CALCULATE( COUNT( 'Date'[Date] ) , 'Date'[IsBusinessDay] = TRUE() ) |
| Working Days | DISTINCTCOUNT( atWorkData[Start] ) |
| Number of Months Worked | DISTINCTCOUNT( atWorkData[YYYY-MM] ) |
| Non Working Business Days | [Business Days] - [Working Days] |
| Total Non Working Fiscal Months | [Total Number of Fiscal Months] - [Number of Months Worked] |
| Non Working Business Hours | [Total Business Hours] - [Total Billed Hours] |
| Monthly Avg. Hours Billed | AVERAGEX ( VALUES ( 'Date'[Month & Year] ), [Total Billed Hours] ) |
| Monthly Avg. Business Hours | AVERAGEX ( VALUES ( 'Date'[Month & Year] ), [Total Business Hours] ) |
| Project Count | DISTINCTCOUNT( atWorkData[Project] ) |
| Client Count | DISTINCTCOUNT( atWorkData[Client] ) |
| Task Count | DISTINCTCOUNT( atWorkData[Task] ) |
| Monthly Avg. Earnings | AVERAGEX ( VALUES( 'Date'[Month & Year] ), [Total Earnings] ) |
| Hourly Billed Rate | DIVIDE( [Total Earnings] , [Total Billed Hours] ) |
| Weekly Avg. Hours Billed | AVERAGEX( VALUES( 'Date'[Week & Year] ), [Total Billed Hours] ) |
| Total Working Hours | [Working Days] * 7.5 |
| Working Hours vs Billed Hours | [Total Billed Hours] - [Total Working Hours] |
| Weekly Avg. Working Hours | AVERAGEX( VALUES( 'Date'[Week & Year] ), [Total Working Hours] ) |
| Working vs Business Hours | IF( [Total Working Hours] < [Total Business Hours], "Under Utilized Hours : " & [Total Business Hours] - [Total Working Hours], " Over Time Hours : " & [Total Working Hours] - [Total Business Hours] ) |
| Weekly Avg. Earnings | AVERAGEX ( VALUES( 'Date'[Week & Year] ), [Total Earnings] ) |
| Business Days without Work % | DIVIDE( [Non Working Business Days] , [Business Days] ) |
| Months Without Work % | DIVIDE( [Total Non Working Fiscal Months] , [Total Number of Fiscal Months] ) |
| Hours without Work % | DIVIDE( [Non Working Business Hours], [Total Business Hours] ) |
| Utilization % | Business Hours | DIVIDE( [Total Billed Hours] , [Total Business Hours] ) |
| Monthly Avg. Utilization % | DIVIDE ( [Monthly Avg. Hours Billed] , [Monthly Avg. Business Hours] ) |
| Utilization % | Working Hours | DIVIDE( [Total Billed Hours] , [Total Working Hours] ) |
| Weekly Avg. Utilization % (Working Days) | DIVIDE ( [Weekly Avg. Hours Billed] , [Weekly Avg. Working Hours] ) |
| Working Hours vs Billed Hours % | [Working Hours vs Billed Hours] / [Total Working Hours] |
| Monthly Earning Tooltip Title | Var SelectedFY = Selectedvalue( 'Date'[Fiscal Year] ) Return " Monthly Earnings | For : " & SelectedFY |
| Month & Year | Var SelectedMonthNYear = Selectedvalue( 'Date'[Month & Year] ) Return SelectedMonthNYear |
| Tooltip Target | Var SelectedTarget = Selectedvalue( 'Utilization Target'[Target] ) Return SelectedTarget |
