Everyone knows the same old 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 various Dax capabilities in Energy BI for Time Intelligence Measures.
The commonest are:
You could find a complete listing of Time Intelligence capabilities right here: Time Intelligence – DAX Guide. These capabilities cowl the most typical circumstances.
Nevertheless, some necessities can’t be simply coated with these capabilities. And right here we’re.
I need to cowl a few of these circumstances I encountered in my tasks, which embody:
- 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 how you can use an prolonged date desk to help these situations and enhance effectivity and efficiency.
Most Time-Intelligence capabilities 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 circumstances, have a look at the DATESYTD() operate talked about above. There, you will discover the elective 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 completely in DAX-Studio, which supplies the Server Timing operate to investigate the efficiency of the DAX code. Within the References part under, you will discover a hyperlink to an article about how you can gather and interpret efficiency information in DAX Studio.
That is the bottom question utilized in my examples to get some information from the info 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 massive variety of extra columns.
Within the references part under, you will discover some articles written by SQLBI, on constructing weekly associated calculations, together with making a date desk to help these calculations.
As described in my article about date tables referenced under, I’ve added the next columns:
- Index or Offset columns to depend 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 right 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 fascinating for weeks, because the start- and finish dates of the weeks will not be the identical from yr to yr.
As you will notice, 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.
An entire Calendar hierarchy comprises 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 an alternative 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 need 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 outcome:
If I have a look at the server timings, the outcome isn’t unhealthy:
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 excellent from the efficiency perspective.
This strategy has one essential subject:
When calculating the rolling sum over a number of months, you should know that this strategy is date oriented.
Which means that while you have a look at a particular time, it goes again to the identical day of the given month. For instance:
We have a look at January 12. 2024, and we need to calculate the rolling sum during the last three months. The beginning date for this calculation shall be November 13. 2023.
When will we need to get the rolling sum for the whole month?
Within the case above, I need to have because the beginning date November 1, 2023.
For this case, we are able to use the MonthIndex column.
Every column has a novel index based mostly on the present date.
Due to this fact, we are able to use it to return three months and get the whole 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, but it surely’s much less environment friendly, as many 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 outcome for a similar logic, however for the final two months (To keep away from exhibiting too many rows):
Relating to Leap Years
The intercalary year drawback is odd, which is clear when calculating the earlier yr for every day. Let me clarify:
Once 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 outcome:
As you’ll be able to see above, the outcome for February 28. 2020 is proven twice, and in the future is lacking the February 2021 for On-line Gross sales (PY).
When trying on the month, the sum is right:
The issue is that there isn’t any February 29 in 2021. Due to this fact, there isn’t any means that the gross sales for February 29, 2020 shall be displayed when itemizing the Gross sales Quantity per day.
Whereas the result’s right, it is going to be mistaken when the info is exported to Excel, and the values are summed. Then, the sum of the every day outcomes will differ from these proven for the whole month.
This may undermine the customers’ perceived reliability of the info.
My resolution was so as to add a LeapYearDate desk. This desk is a replica of the Date desk however and not using a 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 bought the present yr, and by utilizing the VALUES() function, I bought the listing 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 may be very environment friendly, as many 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 prevailing Time Intelligence capabilities.
We should additionally use the calendar columns from this desk within the visualizations. We can’t use the odd date desk.
Consequently, we should reinvent all Time Intelligence capabilities to make use of this desk.
I strongly advocate utilizing this strategy solely when obligatory.
Week to Date and PY
Some Enterprise areas focus on Weekly evaluation.
Sadly, the usual Time Intelligence capabilities don’t help 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 exploit the WEEKDAY() function to calculate the beginning date of the week. Then, I exploit the DATESBETWEEN() function to calculate the WTD.
Whenever you adapt this sample to your state of affairs, you should be certain that the second parameter in WEEKDAY() is ready to the right worth. Please learn the documentation to study 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 is determined by the present situation.
One other problem is to calculate the earlier yr.
Have a look at the next dates for a similar week in several weeks:
As you’ll be able to see, the dates are shifted. And as the usual time intelligence capabilities 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 outcome:
Because the logic is identical as within the WTD v2, the efficiency can also be the identical. Due to this fact, this Measure may be very environment friendly.
Weekly Sums for PY
Typically, the weekly view is sufficient, and we don’t must calculate the WTD on the Each 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 right 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 outcome:
The efficiency is sweet, however I can do higher.
What if I may retailer a novel Week Identifier within the Date column?
For instance, the Present Week is 9 of 2025..
The Identifier could be 202509.
Once 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 outcome 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, a great signal for elevated effectivity.
Fiscal Weeks YTD
This final one is hard.
The requirement is that the consumer needs 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 info.
Once more, realizing if the outcome should be proven on the day or week stage is important. When exhibiting the info on the day stage, the outcome might be complicated when deciding on a Fiscal 12 months:
As you’ll be able to see, Friday is the primary day of the Fiscal yr. And the YTD outcome doesn’t begin on July 1st however on Monday of that week.
The consequence is that the YTD doesn’t appear to start out appropriately. The customers should know what they’re taking a look at.
The identical is legitimate for the YTD PY outcomes.
To facilitate the calculations, I added extra columns to the Date desk:
- FiscalYearWeekYear—This area comprises 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 means to make use of this column might be to comply with 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 Each 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 Each day YTD PY:
On-line Gross sales (Fiscal Week YTD) (PY)] =
VAR FiscalYearWeekYear = MAX('Date'[FiscalYearWeekYear])
-- Get the Week/Weekday firstly 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 because the 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 comply with 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 Outcome 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 right YTD. That is obligatory 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 firstly 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 firstly 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 comply with 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 outcome based mostly on these values.
The outcome 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 good portion 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 outcome on the day stage or if it’s sufficient to see the outcomes on the week stage.
Conclusion
Whenever you begin writing Time Intelligence expressions, take into account whether or not extra calculated columns in your date desk might be useful.
A fastidiously crafted and prolonged date desk might 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 healthier effectivity and efficiency.
I’ll add increasingly columns to the template of my date desk as I encounter extra conditions during which they are often useful.
One query stays: The best way to construct it?
In my case, I used an Azure SQL database to create the desk utilized in my examples.
Nevertheless it’s attainable to create a date desk as a DAX desk or use Python or JavaScript in Material or no matter information platform you employ.
An alternative choice is to make use of the Bravo instrument from SQLBI, which lets you create a DAX desk containing extra columns to help unique Time Intelligence situations.
References
You could find extra details about my date-table here.
Learn this piece to learn to extract efficiency information in DAX-Studio and how you can interpret it.
An SQLBI article about constructing a date desk to help 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 exploit the Contoso pattern dataset. You possibly can obtain the ContosoRetailDW Dataset free of charge from Microsoft here.
The Contoso Information might be freely used beneath the MIT License, as described here.
I modified the dataset to shift the info to modern dates.
Source link
