Everyone knows the standard Time Intelligence operate based mostly on years, quarters, months, and days. However generally, we have to carry out extra unique timer intelligence calculations. However we must always not overlook to contemplate efficiency whereas programming the measures.
Introduction
There are numerous Dax features in Energy BI for Time Intelligence Measures.
The commonest are:
You could find a complete checklist of Time Intelligence features right here: Time Intelligence – DAX Guide. These features cowl the commonest instances.
Nevertheless, some necessities can’t be simply lined with these features. And right here we’re.
I wish to cowl a few of these instances I encountered in my initiatives, which embrace:
- Final n Intervals and a few variants
- How to deal with Leap years
- Week-to-Date calculations
- Calculating Weekly sums
- Fiscal Week YTD
I’ll present you use an prolonged date desk to assist these situations and enhance effectivity and efficiency.
Most Time-Intelligence features work no matter whether or not the Fiscal 12 months is aligned with the calendar yr. One exception is 12 months-to-Date (YTD).
For such instances, take a look at the DATESYTD() operate talked about above. There, one can find the optionally available parameter to cross the final day of the Fiscal yr.
The final case will cowl calculations based mostly on weeks, whereas the Fiscal yr doesn’t align with the calendar yr.
Situation
I’ll use the well-known ContosoRetailDW information mannequin.
The Base Measure is Sum On-line Gross sales, which has the next code:
Sum On-line Gross sales = SUMX('On-line Gross sales',
( 'On-line Gross sales'[UnitPrice]
* 'On-line Gross sales'[SalesQuantity] )
- 'On-line Gross sales'[DiscountAmount] )I’ll work virtually solely in DAX-Studio, which gives the Server Timing operate to investigate the efficiency of the DAX code. Within the References part beneath, you could find a hyperlink to an article about acquire and interpret efficiency information in DAX Studio.
That is the bottom question utilized in my examples to get some information from the information mannequin:
EVALUATE
CALCULATETABLE(
SUMMARIZECOLUMNS('Date'[Year]
,'Date'[Month Short Name]
,'Date'[Week]
,'Date'[Date]
,"On-line Gross sales", [Sum Online Sales]
)
,'Product'[ProductCategoryName] = "Computer systems" ,'Product'[ProductSubcategoryName] = "Laptops"
,'Buyer'[Continent] = "North America"
,'Buyer'[Country] = "United States" ,'Buyer'[State/Province] = "Texas" )
In most examples, I’ll take away some filters to get extra full information (for every day).
Date desk
My date desk features a comparatively giant variety of extra columns.
Within the references part beneath, you could find some articles written by SQLBI, on constructing weekly associated calculations, together with making a date desk to assist these calculations.
As described in my article about date tables referenced beneath, I’ve added the next columns:
- Index or Offset columns to rely the times, weeks, months, quarters, semesters, and years from the present date.
- Flag columns to mark the present day, week, month, quarter, semester, and yr based mostly on the present date.
- This and the earlier columns require a every day recalculation to make sure the proper date is used because the reference date.
- Begin- and Finish-Dates of every week and month (Add extra if wanted).
- Begin- and Finish-Dates for the Fiscal 12 months.
- Earlier yr dates to incorporate the beginning and finish dates of the present interval. That is particularly attention-grabbing for weeks, because the start- and finish dates of the weeks will not be the identical from yr to yr.
As you will note, I’ll use these columns extensively to simplify my calculations.
As well as, we’ll use the Calendar Hierarchy to calculate the wanted outcomes at completely different ranges of the hierarchy.
A whole Calendar hierarchy incorporates both:
- 12 months
- Semester
- Quarter
- Month
- Day
Or
- 12 months
- Week
- Day
If the Fiscal 12 months doesn’t align with the Calendar yr, I constructed the Hierarchy with the Fiscal 12 months as a substitute of the Calendar 12 months.
Then, I added a separate FiscalMonthName column and a FiscalMonthSort column to make sure that the primary month of the fiscal yr was proven first.
OK, let’s begin with the primary case.
Final n intervals
This situation calculates the rolling sum of values over the previous n intervals.
For instance, for every day, we wish to get the Gross sales for the final 10 days:
Right here is the Measure I got here up with:
On-line Gross sales (Final 10 days) =
CALCULATE (
[Sum Online Sales]
,DATESINPERIOD (
'Date'[Date],
MAX ( 'Date'[Date] ),
-10,
DAY
)
) When executing the question filtering for Computer systems and North America, I get this end result:
If I take a look at the server timings, the end result isn’t dangerous:
As you’ll be able to see, the Storage engine performs greater than half of the work, which is an efficient signal. It’s not excellent, however because the execution time is lower than 100 ms, it’s nonetheless superb from the efficiency perspective.
This strategy has one essential challenge:
When calculating the rolling sum over a number of months, you could know that this strategy is date oriented.
Which means that whenever you take a look at a particular time, it goes again to the identical day of the given month. For instance:
We take a look at January 12. 2024, and we wish to calculate the rolling sum during the last three months. The beginning date for this calculation will probably be November 13. 2023.
When will we wish to get the rolling sum for the complete month?
Within the case above, I wish to have because the beginning date November 1, 2023.
For this case, we will use the MonthIndex column.
Every column has a singular index based mostly on the present date.
Due to this fact, we will use it to return three months and get the complete month.
That is the DAX Code for this:
On-line Gross sales rolling full 3 months =
VAR CurDate =
MAX ( 'Date'[Date] )
VAR CurMonthIndex =
MAX ( 'Date'[MonthIndex] )
VAR FirstDatePrevMonth =
CALCULATE (
MIN ( 'Date'[Date] ),
REMOVEFILTERS ( 'Date' ),
'Date'[MonthIndex] = CurMonthIndex - 2
)
RETURN
CALCULATE (
[Sum Online Sales],
DATESBETWEEN (
'Date'[Date],
FirstDatePrevMonth,
CurDate
)
)The execution remains to be fast, however it’s much less environment friendly, as a lot of the calculations can’t be carried out by the Storage engine:
I attempted different approaches (for instance, 'Date'[MonthIndex] >= CurMonthIndex – 2 && 'Date'[MonthIndex] <= CurMonthIndex), however these approaches have been worse than this one.
Right here is the end result for a similar logic, however for the final two months (To keep away from exhibiting too many rows):
Relating to Leap Years
The bissextile year drawback is odd, which is clear when calculating the earlier yr for every day. Let me clarify:
After I execute the next Question to get the final days of February for the years 2020 and 2021:
EVALUATE
CALCULATETABLE (
SUMMARIZECOLUMNS (
'Date'[Year],
'Date'[Month Short Name],
'Date'[MonthKey],
'Date'[Day Of Month],
"On-line Gross sales", [Sum Online Sales],
"On-line Gross sales (PY)", [Online Sales (PY)]
),
'Date'[Year] IN {2020, 2021},
'Date'[Month] = 2,
'Date'[Day Of Month] IN {27, 28, 29},
'Buyer'[Continent] = "North America",
'Buyer'[Country] = "United States"
)
ORDER BY 'Date'[MonthKey],
'Date'[Day Of Month]I get the next end result:
As you’ll be able to see above, the end result for February 28. 2020 is proven twice, and someday is lacking the February 2021 for On-line Gross sales (PY).
When wanting on the month, the sum is appropriate:
The issue is that there isn’t any February 29 in 2021. Due to this fact, there isn’t any manner that the gross sales for February 29, 2020 will probably be displayed when itemizing the Gross sales Quantity per day.
Whereas the result’s appropriate, will probably be unsuitable when the information is exported to Excel, and the values are summed. Then, the sum of the every day outcomes will differ from these proven for the complete month.
This could undermine the customers’ perceived reliability of the information.
My answer was so as to add a LeapYearDate desk. This desk is a duplicate of the Date desk however with no Date column. I added one row every year on February 29, even for non-leap years.
Then, I added a calculated column for every month and day (MonthDay):
MonthDay = ('LeapYearDate'[Month] * 100 ) + 'LeapYearDate'[Day Of Month]The Measure to calculate the earlier yr manually and utilizing the brand new desk is the next:
On-line Gross sales (PY Leap 12 months) =
VAR ActYear =
SELECTEDVALUE ( 'LeapYearDate'[Year] )
VAR ActDays =
VALUES ( 'LeapYearDate'[MonthDay] )
RETURN
CALCULATE (
[Sum Online Sales],
REMOVEFILTERS ( LeapYearDate ),
'LeapYearDate'[Year] = ActYear - 1,
ActDays
)As you’ll be able to see, I received the present yr, and by utilizing the VALUES() function, I received the checklist of all dates within the present filter context.
Utilizing this technique, my Measure works for single Days, Months, Quarters, and Years. The results of this Measure is the next:
As you’ll be able to see right here, the Measure could be very environment friendly, as a lot of the work is completed by the Storage engine:
However, to be trustworthy, I don’t like this strategy, although it really works very effectively.
The reason being that the LeapYearDate desk doesn’t have a date column. Due to this fact, it can’t be used as a Date desk for the present Time Intelligence features.
We should additionally use the calendar columns from this desk within the visualizations. We can not use the abnormal date desk.
Consequently, we should reinvent all Time Intelligence features to make use of this desk.
I strongly advocate utilizing this strategy solely when needed.
Week to Date and PY
Some Enterprise areas focus on Weekly evaluation.
Sadly, the usual Time Intelligence features don’t assist weekly evaluation out of the field. Due to this fact, we should construct our Weekly Measures by ourselves.
The primary Measure is WTD.
The primary strategy is the next:
On-line Gross sales WTD v1 =
VAR MaxDate = MAX('Date'[Date])
VAR CurWeekday = WEEKDAY(MaxDate, 2)
RETURN
CALCULATE([Sum Online Sales]
,DATESBETWEEN('Date'[Date]
,MaxDate - CurWeekDay + 1 ,MaxDate)
)As you’ll be able to see, I take advantage of the WEEKDAY() function to calculate the beginning date of the week. Then, I take advantage of the DATESBETWEEN() function to calculate the WTD.
While you adapt this sample to your scenario, you could be sure that the second parameter in WEEKDAY() is ready to the proper worth. Please learn the documentation to be taught extra about it.
The result’s the next:
One other strategy is to retailer the primary date of every week within the Date desk and use this data within the Measure:
On-line Gross sales WTD PY v2 =
VAR DayOfWeek = MAX('Date'[Day Of Week])
VAR FirstDayOfWeek = MIN('Date'[FirstDayOfWeekDatePY])
RETURN
CALCULATE([Sum Online Sales]
,DATESBETWEEN('Date'[Date]
,FirstDayOfWeek
,FirstDayOfWeek + DayOfWeek - 1)
)
The result’s exactly the identical.
When analyzing the efficiency in DAX Studio, I see that each Measures are comparable to one another:
I have a tendency to make use of the second, because it has higher potential when mixed with different Measures. However ultimately, it will depend on the present situation.
One other problem is to calculate the earlier yr.
Have a look at the next dates for a similar week in numerous weeks:
As you’ll be able to see, the dates are shifted. And as the usual time intelligence features are based mostly on shifting dates, they won’t work.
I attempted completely different approaches, however ultimately, I saved the primary date of the identical week for the earlier yr within the date desk and used it like within the second model of WTD proven above:
On-line Gross sales WTD PY =
VAR DayOfWeek = MAX('Date'[Day Of Week])
VAR FirstDayOfWeek = MIN('Date'[FirstDayOfWeekDatePY])
RETURN
CALCULATE([Sum Online Sales]
,DATESBETWEEN('Date'[Date]
,FirstDayOfWeek
,FirstDayOfWeek + DayOfWeek - 1)
)
That is the end result:
Because the logic is identical as within the WTD v2, the efficiency can also be the identical. Due to this fact, this Measure could be very environment friendly.
Weekly Sums for PY
Typically, the weekly view is sufficient, and we don’t must calculate the WTD on the Every day stage.
We don’t want a WTD Measure for this situation for the present yr. The bottom Measure sliced by Week can cowl this. The result’s appropriate out of the field.
However, once more, it’s one other story for PY.
That is the primary model I got here up with:
On-line Gross sales (PY Weekly) v1] =
VAR ActYear = MAX('Date'[Year])
RETURN
CALCULATE([Sum Online Sales]
,ALLEXCEPT('Date'
,'Date'[Week]
)
,'Date'[Year] = ActYear - 1
)
Right here, I subtract one from the present yr whereas retaining the filter for the present week. That is the end result:
The efficiency is sweet, however I can do higher.
What if I may retailer a singular Week Identifier within the Date column?
For instance, the Present Week is 9 of 2025..
The Identifier could be 202509.
After I detract 100 from it, I get 202409, the identifier for a similar week within the earlier yr. After including this column to the date desk, I can change the Measure to this:
MEASURE 'All Measures'[Online Sales (PY Weekly) v2] =
VAR WeeksPY = VALUES('Date'[WeekKeyPY])
RETURN
CALCULATE([Sum Online Sales]
,REMOVEFILTERS('Date')
,'Date'[WeekKey] IN WeeksPY
)
This model is far less complicated than earlier than, and the end result remains to be the identical.
After we evaluate the execution statistics of the 2 variations, we see this:
As you’ll be able to see, the second model, with the precalculated column within the Date desk, is barely extra environment friendly. I’ve solely 4 SE queries, an excellent signal for elevated effectivity.
Fiscal Weeks YTD
This final one is hard.
The requirement is that the consumer desires to see a YTD ranging from the primary day of the primary week of the Fiscal yr.
For instance, the Fiscal yr begins on July 1.
In 2022, the week containing July the 1st begins on Monday, June 27.
Which means that the YTD calculation should begin on this date.
The identical applies to the YTD PY calculation beginning Monday, June 28, 2021.
This strategy has some penalties when visualizing the information.
Once more, figuring out if the end result should be proven on the day or week stage is important. When exhibiting the information on the day stage, the end result will be complicated when choosing a Fiscal 12 months:
As you’ll be able to see, Friday is the primary day of the Fiscal yr. And the YTD end result doesn’t begin on July 1st however on Monday of that week.
The consequence is that the YTD doesn’t appear to begin appropriately. The customers should know what they’re .
The identical is legitimate for the YTD PY outcomes.
To facilitate the calculations, I added extra columns to the Date desk:
- FiscalYearWeekYear—This subject incorporates the numerical illustration of the Fiscal yr (for 23/24, I get 2324), beginning with the primary week of the Fiscal yr.
- FiscalYearWeekYearPY – The identical as earlier than, however for the earlier yr (FiscalYearWeekYear – 101).
- FiscalWeekSort—This sorting column begins the week with the primary day of the fiscal yr. A extra elaborate manner to make use of this column might be to observe the ISO-Week definition, which I didn’t do to maintain it easier.
- FiscalYearWeekSort – The identical as earlier than however with the FiscalYearWeekYear in entrance (e. g. 232402).
- FirstDayOfWeekDate – The date of the Monday of the week during which the present date is in.
Right here is the Measure for the Every day YTD:
On-line Gross sales (Fiscal Week YTD) =
VAR FiscalYearWeekYear = MAX('Date'[FiscalYearWeekYear])
VAR StartFiscalYear = CALCULATE(MIN('Date'[Date])
,REMOVEFILTERS('Date')
,'Date'[FiscalYearWeekSort] =
FiscalYearWeekYear * 100 + 1
)
VAR FiscalYearStartWeekDate = CALCULATE(MIN('Date'[FirstDayOfWeekDate])
,ALLEXCEPT('Date'
,'Date'[FiscalYearWeekYear]
)
,'Date'[Date] = StartFiscalYear
)
VAR MaxDate = MAX('Date'[Date])
RETURN
CALCULATE([Sum Online Sales]
,REMOVEFILTERS('Date')
,DATESBETWEEN('Date'[Date]
,FiscalYearStartWeekDate
,MaxDate
)Right here is the DAX Code for the Every day YTD PY:
On-line Gross sales (Fiscal Week YTD) (PY)] =
VAR FiscalYearWeekYear = MAX('Date'[FiscalYearWeekYear])
-- Get the Week/Weekday at the beginning of the present Fiscal 12 months
VAR FiscalYearStart = CALCULATE(MIN('Date'[Date])
,REMOVEFILTERS('Date')
,'Date'[FiscalYearWeekSort] =
FiscalYearWeekYear * 100 + 1
)
VAR MaxDate = MAX('Date'[Date])
-- Get the variety of Days for the reason that begin of the FiscalYear
VAR DaysFromFiscalYearStart =
DATEDIFF( FiscalYearStart, MaxDate, DAY )
-- Get the PY Date of the Fiscal 12 months Week Begin date
VAR DateWeekStartPY = CALCULATE(MIN('Date'[Date])
,REMOVEFILTERS('Date')
,'Date'[FiscalYearWeekSort] =
(FiscalYearWeekYear - 101) * 100 + 1
)
RETURN
CALCULATE(
[Sum Online Sales],
DATESBETWEEN(
'Date'[Date],
DateWeekStartPY,
DateWeekStartPY + DaysFromFiscalYearStart
)
)As you’ll be able to see, each Measures observe the identical sample:
- Get the present Fiscal 12 months.
- Get the Beginning Date of the present Fiscal 12 months.
- Get the Beginning date of the week beginning the Fiscal 12 months.
- Calculate the End result based mostly on the Distinction between these two dates
For the PY Measure, one extra step is required:
- Calculate the times between the beginning and present dates to calculate the proper YTD. That is needed due to the date shift between the years.
And right here is the DAX code for the weekly base YTD:
On-line Gross sales (Fiscal Week YTD) =
VAR FiscalWeekSort = MAX( 'Date'[FiscalWeekSort] )
-- Get the Week/Weekday at the beginning of the present Fiscal 12 months
VAR FiscalYearNumber = MAX( 'Date'[FiscalYearWeekYear] )
RETURN
CALCULATE(
[Sum Online Sales],
REMOVEFILTERS('Date'),
'Date'[FiscalYearWeekSort] >= (FiscalYearNumber * 100 ) + 1
&& 'Date'[FiscalYearWeekSort] <= (FiscalYearNumber * 100 ) +
FiscalWeekSort
)For the weekly YTD PY, the DAX code is the next:
On-line Gross sales (Fiscal Week YTD) (PY) =
VAR FiscalWeekSort = MAX( 'Date'[FiscalWeekSort] )
-- Get the Week/Weekday at the beginning of the present Fiscal 12 months
VAR FiscalYearNumberPY = MAX( 'Date'[FiscalYearWeekYearPY] )
RETURN
CALCULATE(
[Sum Online Sales],
REMOVEFILTERS('Date'),
'Date'[FiscalYearWeekSort] >= (FiscalYearNumberPY * 100) + 1
&& 'Date'[FiscalYearWeekSort] <= (FiscalYearNumberPY * 100) +
FiscalWeekSort
)Once more, each Measures observe the identical sample:
- Get the present (Type-) variety of the week within the Fiscal yr.
- Get the beginning date for the fiscal yr’s first week.
- Calculate the end result based mostly on these values.
The end result for the weekly based mostly Measure is the next (On the weekly stage, as the worth is the identical for every day of the identical week):
When evaluating the 2 Approaches, the Measure for the weekly calculation is extra environment friendly than the one for the every day calculation:
As you’ll be able to see, the Measure for the weekly result’s sooner, has a extra significant slice executed within the Storage Engine (SE), and has fewer SE queries.
Due to this fact, it may be a good suggestion to ask the customers in the event that they want a WTD end result on the day stage or if it’s sufficient to see the outcomes on the week stage.
Conclusion
While you begin writing Time Intelligence expressions, take into account whether or not extra calculated columns in your date desk will be useful.
A fastidiously crafted and prolonged date desk will be useful for 2 causes:
- Make Measures simpler to put in writing
- Enhance the efficiency of the Measures
They are going to be simpler to put in writing as I don’t must carry out the calculations to get the middleman outcomes to calculate the required outcomes.
The consequence of shorter and less complicated Measures is best effectivity and efficiency.
I’ll add increasingly more columns to the template of my date desk as I encounter extra conditions during which they are often useful.
One query stays: Find out how to construct it?
In my case, I used an Azure SQL database to create the desk utilized in my examples.
However it’s doable to create a date desk as a DAX desk or use Python or JavaScript in Material or no matter information platform you utilize.
An alternative choice is to make use of the Bravo software from SQLBI, which lets you create a DAX desk containing extra columns to assist unique Time Intelligence situations.
References
You could find extra details about my date-table here.
Learn this piece to discover ways to extract efficiency information in DAX-Studio and interpret it.
An SQLBI article about constructing a date desk to assist weekly calculations: Utilizing weekly calendars in Power Bi – SQLBI
SQLBI Sample to carry out additional weekly calculations:
Week-related calculations – DAX Patterns
Like in my earlier articles, I take advantage of the Contoso pattern dataset. You’ll be able to obtain the ContosoRetailDW Dataset at no cost from Microsoft here.
The Contoso Knowledge will be freely used underneath the MIT License, as described here.
I modified the dataset to shift the information to modern dates.
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