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Easy Profit & Loss (and other account) statements in PowerBI and Excel – Part2

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Welcome to part 2 of my series of easy Profit & Loss and other account statements in Power BI and Excel. In the first part I introduced the general principle of creating asymmetric shaped reports who use just one measure per column (you should have read this article in order to understand this post here).

How the technique works

This technique capitalises the aggregation power of the Vertipaq engine and creates a bridge-table between your DimAccount-table and the ReportsAccountsLayout-table. In there for every line of your report, all accounts that belong to the (sub-)totals are matched (“AccountsAllocation”). This table can get very long, but the engine can handle this easily:

Different use case: Account-groups-tables

In the first example we’ve worked with a chart of accounts, which had a parent-chield-hierarchy defining all the subtotals of the report. In this example we’re working with a different setup, using the good old DimAccountsGroups-table. Just one row per account and the columns are coming in pairs, containing the group-criteria and the sort-order for the report:

Individual Report-Layout

We also need a second table (ReportsAccountsLayout) that holds the definitions of the report-layouts like this:

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Dynamic & bulk type transformation in Power Query, Power BI and M

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This is just a quick code-share-of-the-day of different scenarios for dynamic type transformation of multiple columns at once. The syntax to transform the format of 2 columns (“Column1” and “Column2”) in a table (“Source”) looks like this: Now if you would like to do one of the following: 1. Force all columns of the table to … Read more

Dynamic Power Query X-mas tree in Excel

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Wishing my faithful readers peaceful and happy holidays. Hope you enjoy my jumping Power Query X-mas tree: It changes his size or decoration randomly every time you press refresh: Totally auto-M-agically 😉 (No macros and no Excel formula in any cell ! ) Just 6 lines of code and 2 formatting conditions return a table with dynamic size. … Read more

How to store tables longer than 1,1 Mio rows in an Excel-sheet using Power Query and JSON

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If you are working Power Query, you might come into a situation where you would like to make tables accessible from outside that file, that are longer than the 1,1 Mio rows who fit into an Excel-sheet. You can do that using the JSON-format and compression. That way you create a table in Excel that contains a JSON-text string, that can then be read and decoded again by Excel (or Power BI), using Power Query (or Get&Transform in Excel 2016).

You can download the file where a table with 2 Mio rows is compressed into a table with just 229 rows here: JSON_BreakRefreshChain4.xlsx

Simple idea:

Convert your table into JSON using the Json.FromValue-function. If we could export that JSON-file from here, we would be done already. But I haven’t figured out yet, if/how one could export or copy that from within the query-editor (apart from manual copy& paste, but that has also a limit of our good 1 Mio characters here). So if you have any idea about this, please share.

Workaround:

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Incremental load in PowerBI

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Edit 2017-Jan-13: You’ll find a more recent version that doesn’t use hacks for incremental load here (but that also won’t work in the service): http://www.thebiccountant.com/2017/01/11/incremental-load-in-powerbi-using-dax-union/

If you want to perform incremental load of your data in PowerBI, you should vote for it here.

Please vote before you read on to see which hacky workaround to use until this is implemented.

This workaround is a compromise that’s mainly suited for use cases like:
– performing extensive transformations on imported data only once
– saving data in the model that will not be available at the source later on (like many web-imports)

It works on the principle of self-referencing-queries like I’ve described here in combination with the hack of your current PBI-datamodel which I’ve described here (that’s fully automated now). So it will just work in the Desktop version and not in the service (so no scheduled refresh possible) !!.

So the data that should “sit in the model” and just be merged with new incoming data will actually be imported into the model again. Just not from it’s original source, but instead from itself. As this is often slower than to import from the original source, you will have to look closely into your specific need and decide if this makes sense for you.

These are the steps to perform:

1) Create 1st query that imports and transforms the first chunk of data that shall not be refreshed afterwards and load it to the data model
2) Go back to the query designer and create a 2nd query that re-imports 1st query using the function below
3) Create a 3rd query that appends query2 to query1 and load it to the model
4) Go back to the query designer and choose 3d query to implement a filter or remove-duplicates step that avoids importing data that has already been imported
5) Change the source in the 2nd query from 1st to 3rd query (as you now want to reference everything that has been imported so far).
6) Disable load of the first two queries

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Easy Profit and Loss and other (account) scheme reports in Power BI and Power Pivot using DAX

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This is about an easy way to create typical finance reports like Profit and Loss using DAX that (unlike all other solutions I’ve come across so far) can be handled with very basic knowledge of this language like this:

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The trick

The trick that makes my solution so easy lies in the fact that it requires no aggregation functions of the output-mediums like:

  • pivot-tables: who struggle with asymetric logic and are not available in Power BI so far
  • cubefunctions: who are not available in PowerBI so far

So we have to build the details as well as all aggregations into the solution as it is and don’t rely on/use any aggregation functions (This means for Excel: We have to turn off subtotals as well as totals in our pivot tables. It means for Power BI: Hurray! Finally a solution where the lack of pivot-tables doesn’t matter).

How to

The aim is to create a table/matrix with (account) details and aggregations into the rows and different slices of time-Intervalls or comparisons into the column sections. As for the columns, this will be covered by measures like [Actuals], [Budget], [PreviousPeriods], [Differences in all shapes…]. And – as the values in the columns should be the same – I’d prefer to use only one measure per column – that is fully sliceable and works on all (sub-) totals of course. … Ok – so some dreams later I found it:

MyMagicMeasure := CALCULATE([StandardMeasure], AccountsAllocation)

So you just wrap simple measures like Act=SUM(Fact[Amount]), Plan=SUM(Plan[Amount]), DiffPlan_Act=[Plan]-[Act] … into the CALCULATE together with the bridge-table as the filter-argument:

This is the many2many-technique in it’s simplest form (PostFromMarcoRusso). It all goes via simple aggregation on all accounts found in the filter context:

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Our  bridge-table “AccountsAllocation” consists of one account number per simple account and has multiple rows for the (sub-)totals – being all accounts that belong to them:

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The ConsKey stands for the row in our report (1) and the AccountKey_ holds the account numbers that are going to be aggregated (many (for the sub-totals) and 1 for the account-rows). So all we need is this unique and simple aggregation on AccountKey for every row in the report – with a filter from the Reports-table via our bridge table to the DimAccounts, who then filters our FactTables: 1 -> many -> 1 -> many.

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