Time Intelligence Without Date Columns in DAX

One of the things that annoys me a little bit in DAX is the way all the built-in time intelligence functionality relies so much on having columns of type Date. What happens if you don’t have a Date column to use? Many Time dimension tables that I see in data warehouses don’t: they use integers to represent dates instead, often in the form YYYYMMDD (eg so 20100525 would represent the 25th of May 2010). And what happens if you need to something time-intelligence-like on a non time dimension? For example you might have a dimension representing different budgets, and you might want to show the difference between the current budget and the previous budget. There are no equivalents of the .PrevMember or .NextMember, or any hierarchical functions, because there are no hierarchies in PowerPivot.

Anyway, after some thought (and a lot of help from Tomislav and Jeffrey Wang from the dev team) I’ve found out it is possible to create time-intelligence calculations without dates. Let’s take a simple PowerPivot model based on Adventure Works DW, with the DimDate table joined to FactInternetSales on the OrderDateKey column. Now since there’s a column of type Date on DimDate, FullDateAlternateKey, we can use the time intelligence functionality to create a calculation that returns the previous year’s Sales Amount as follows:

    , DATEADD(DimDate[FullDateAlternateKey], -1, YEAR)
    , ALL(DimDate))

Here’s what a query using this calculation looks like:


The above calculation works by returning the sum of SalesAmount for all the dates that are one year before the dates in the current context. So if you have the year 2003 on rows in your query, the for that cell the row context would contain all of the dates in the year 2003; these then would be shifted back one year and you’d get the sum of SalesAmount for all the dates in the year 2002. Incidentally, this explains why in Adventure Works you get strange values for the year 2004: it’s because in DimDate you only have dates from January 1st 2004 to August 31st 2004, so the calculation only returns the sum of SalesAmount for January 1st 2003 to August 31st 2003.

Now, what would happen if we didn’t have the FullDateAlternateKey column and we wanted to do the same calculation? What we need to do is recreate this algorithm and shift our dates back one year without using the DateAdd function. The way to do it relies on the format of the keys used in the DimDate[DateKey] column, the YYYYMMDD format I mentioned before. If we have a date key like 20100525 and we want to find the equivalent date in the previous year, all we need to do is subtract 10000 from that key value. In our expression, then, what we need to do is to get the set of dates in the current row context, and for each of those dates return the date that has the key value that is 10000 less than the current date, then sum up SalesAmount for those dates. Here’s the expression:

, FILTER(VALUES(DimDate[DateKey]), DimDate[DateKey]-10000=EARLIER(DimDate[DateKey])))
, ALL(DimDate))

Notice the use of two nested Filter statements here, and the way that the Earlier function is used to retrieve the value of DimDate[DateKey] from the outer row context. The outer Filter gets the set of all values from DimDate[DateKey], regardless of year or any other selection made, using the All function; the inner Filter filters this set so it only contains the keys that are 10000 less than the dates that have currently been selected.

It works in almost the same way as the original calculation, although I’ve noticed a few differences. First of all, the grand total for the new calculation displays the total of the values for each year, in a way that the original version does not:


Secondly, on February 29th in a leap year, the DateAdd function returns February 28th in the previous year and our new approach (unsurprisingly) does not:


It’s probably a matter of taste what should happen here; either a null or the value from February 28th in the previous year make sense to me. Note, though, that as shown in the last but one screenshot the year totals for 2004 for both calculations are identical, so the value for February 28th is not counted twice.

Thirdly, keeping dates on rows and measures on columns, when you add English Month Name to a slicer and choose one month, the new expression works ok but you get the following error from the original expression:

ERROR – CALCULATION ABORTED: MdxScript(Sandbox) (22, 4) Function ‘DATEADD’ only works with contiguous date selections.


This particular error will be the subject of my next blog post, but in this case I think the DAX time intelligence functions are behaving a bit rubbishly when they should be returning results.

10 responses

  1. This is an important article. Looking at the differences you mentioned, I can conclude nothing but that your calculation is superior. History repeats itself with built-in time-intelligence, wouldn\’t you say? .-)

  2. I wouldn\’t say it\’s better – the thing I didn\’t test was performance. I don\’t know how my approach compares to the DateAdd approach, and I wouldn\’t be surprised if using DateAdd was faster.

  3. Yes, that crossed my mind. And you\’re right, it should be slower. But then again, we\’re talking about time dimension here (Values() and Earlier() refer to that). How many members must we have there to notice significant performance issues?Setting the context is probably faster with built-in approach, should be. I remember my experiments with Earlier() function were several times slower than the same calculations made different way, without it. Which means I\’ve waited for a second or two in the worst case. However, I used a fact table in my calculations, not a small time table. Nevertheless, the performance issue would be interesting to measure. Do you plan to perform tests?In contrast, while I might agree that the second difference is ok in both ways, differences number 1 and 3 signal questionable total and inability to slice the data. Weighting that against an unknown decrease in performance in already fast Vertipaq is a clear case for me. But then again, it wouldn\’t be the first time that I\’m wrong :-).

  4. Performance tests would be a good idea – which then raises the question of how to test the performance of a DAX calculation. I guess you\’d need to use the built-in trace functionality, and maybe run the query as soon as you opened the workbook to ensure it runs on a cold cache… I\’ll ask someone.Anyway, I agree that issue #3 is a big problem in my opinion, and is definitely something that should be fixed in a service pack.

  5. One thing you could also do in this particular case is add a column with the following formula: =DATEVALUE(FORMAT(VALUE(DimDate[DateKey]),"0000-00-00")) and then use DATEADD(). I haven\’t tested the performance impact though.

  6. Thanks very much for sharing this, its been massively helpful. Am struggling with a case though; In the case of 2004 having no sales recorded for example, the last year DAX returns nothing, despite there being sales for 2003. Could you point me in the right direction?

    I’ve simplified the problem in a very small Excel Power Pivot – let me know if you would like a OneDrive link.

      • This is a great example of why you need a separate Date table, even if it doesn’t contain dates. You don’t see any values for Last Year for 2003 because that year doesn’t exist in the FinYear column. Creating a separate table with all of the Financial Years you want and using that table in your calculation fixes the problem. I’ve emailed you an updated version of the workbook with the fix it; let me know if you don’t get it.

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