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daez12
Frequent Visitor

Please Provide Direction on Creating KPI Dashboard

I'm working on startup and would like to take our current Excel manual entry KPI dashboard and replicate it in Power BI. My first thought was a pivot table but as I'm putting the date columns in, measures just aren't aligning with the date groups, and values keep showing up in the header, making it unreadable.

 

Essentially I'm just looking for some form of direction on how I can create this in Power BI.

 

daez12_0-1678295579526.png

 

5 REPLIES 5
sevenhills
Super User
Super User

I have some idea to do this.

 

Normally if it is of same type i.e., only dollars or only percentages, then we can use the option "Values" as "Show on rows". I see that you have multiple formats. 

 

It is possible and little complex for new folks. With little effort, you can do this.

What is your data model, purely with respect to measures and how deep are those calculations going to be in the matrix visual? 

 

 

 

 

 

I would literally pay to have help on solving this. It's critical for our company.

I am fairly new with Power BI so your help is massively appreciated. There will need to be a "calculation" or "measure" for each KPI. For instance, using the data table screenshotted below, I could write something like this for trailing 30 days % of total machines w/ trx

Trailing 30 days % of total machines w/ trx = divide((calculate(DISTINCTCOUNTNOBLANK(Sale[AtmId]),'Sale'[Fiat]>0)),calculate(DISTINCTCOUNTNOBLANK(Sale[AtmId])))
daez12_0-1678449055796.png



Again, thank you so much for your help.



Since you are new to Power BI, I would like to inform that it is definitely possible and may advise you to take an iterative approach.

 

I will recommend to start with one calculation group, with 5 Measures
which contains different formats and then add the remaining.

 

1. Cleanse the data and model to your needs


2. Create the measures you need


3. Get the Tabular Editor installed (Optional but recommend to ease your work)


4. Calculation Groups

 

I will recommend to go one after other these links, which makes you more comfortable.


Spend like 3 to 4 hours, as almost all links provide different examples using Calculation Groups.

 

 

5. You may need two or three Calculation Groups, as you have lagging, leading indicators set

 

 

Thank you so much for these helpful resources. I will be digging into this for the remainder of the day. If possible, do you mind providing an example of a calculation group for trailing 30 days average sales (Fiat column) per unique ATM ID? The table below is an example of the table.

SaleIDBTCFeeFiatUUIDStageAtmIdAddressBatchedBatchIdTXHashEnviadoCoinTypeAtmSerialCreatedAt
4397730.001601666.7135339c497314ac7e55a6a83b987f017bfbbcdab98002e1c1c28ac7cac7820ba476completed1256bc1qnjge3ww0hzppfpzlu5sw5atwd4j5r5lyeqwfqwTRUE365170df41a00e57389137e64523308285d4f1ad87ebc78a0c784e331aa9dce3bc70428.29bitcoin013191212/10/2022 6:09
4397750.0047806215.49100e07982a7bfb11fa3534c1d5138e4614c941d154f76d7c7136e28f83425f2f6f6completed2280bc1qa9qfehe72ckvzuv3sme74paj7x9qre763mpkdgTRUE365170df41a00e57389137e64523308285d4f1ad87ebc78a0c784e331aa9dce3bc70484.51bitcoin013371612/10/2022 6:28
4397840.0342754794.5700a2d3975b3c9feff12361d8d08f10b3a7dffa5bac945ec50167481d0ff04f38f6completed5513E8XCnVxJ7o2xeLUrm2M6byDMzZ2noH6v7FALSE 651bee07598b0e2c4c1155a855bdb72a96f220043694109879aa7e2a4e67cf59605.5bitcoin013001612/10/2022 8:23
4397870.001111895.3625d40c17d6d63865c2d4a2e4dfd5ebcc162388f6ff4343e50ebf074ce2f43056b3completed363118aZo7ngoMnL2hbU2VsZHKFJPoGAgxQ4PFTRUE36521854c5ec8db5de03f11ff8141f3e497d440ee598e437f18ace9c5294f578841f119.64bitcoinF011131412/10/2022 10:06
4397890.0073399720.251504548761022bec9d64be6d66f05c29d94cb85194172b866fc4de81d8a7da1a80ecompleted3019bc1qmfc7tegp7f2vwr08szuag6rs07x98fex7prdttTRUE365234927b3153c84eaaf696a9597f63c4cbfe99f357c50ec5ead4049183366c0962f129.75bitcoin013340912/10/2022 11:24
4397910.048922431351000908fcb822be0dbed7e82c7362be21d77b9a92368729b7a26a85bddf07b1a329acompleted3523381sJkzUUcinW42aYVazZcugiUjKCcZZcvFALSE 1b83997a49928bdcd6c023e81fda1fc1f1b742a6a11ef201cd8af73f07e708f8865bitcoinF011124512/10/2022 11:47
4397920.0244528667.550091ad420ae62d4de08f7d7cc5a0454ecede04360975511b37b2ad8bd8a7ec9ad9completed20623Bjm2HFJCUfDHPkGZZTvBkDRrmSuPVF2R2FALSE 8f8f922dad757420bacd8a0ad9540e177520d2b253e7a5e32c3d9e54b4be1927432.5bitcoin013286712/10/2022 11:54
4397930.0146701740.5300bf2aeb6a99d29f8f884224035f70435364b7598b7c3d62d3588fdc3a1605a131completed1341bc1qsllyv4x7x3zc8daxyv50h48s46nzafql8863r4FALSE ae396b0bb7c5bda7206e1d786413bffe4a2dfe6f40fff46f371264e03ce26706259.5bitcoin013194112/10/2022 12:07
4397950.001355196.0430a4e76547dcbee0cefa4779db3c34ea7f39d2563c5028a250e96e2f2e14edaab7completed962bc1qzr96rdv2xje8uz5yd9ses7mxfqrgyjq4u4l975TRUE365253b218e74548a81885ba3be540b0c034102f16ce8dae44761e95517824bc5028e23.96bitcoin013138712/10/2022 12:17
4397980.0489355213510000534d3ae969bfb9623952fc0d83060a467c1e0e857324ffe8e116517cb418cbdcompleted33941EnpNrA3Xjz92CYequM2HSpB415Vuy13ZVFALSE 9cdc798077a2b6312654c7a57567a8479945b6ce1d9bd2b9dc700616a6a83e14865bitcoinF011128312/10/2022 12:27
4397990.0176148348.6360be3de556bf727a8b18c0e7961449c1ed10eec3fb204ba1ef0909779edaacec71completed6431D79zJq5DSEapmtYaUy7u37uPFKh7ovmetFALSE acbf81f7125d998f235ba81751e15914261dfaf93957540a68901fac22dd7423311.4bitcoin013017412/10/2022 12:27

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