All posts by Dustin Ryan

Dustin is a Senior Business Intelligence Consultant with Pragmatic Works. You can find Dustin blogging and speaking at events like SQL Saturday, Code Camp, and SQL Rally. Follow Dustin on Twitter @SQLDusty.

Tons of Updates to Power BI in October!

There were so many updates to Power BI this month I thought I’d put all the links to the information on the updates in one place so we can get caught up quickly on everything that changed with the tool this month. Down below you’ll find my commentary on which updates I’m most excited about and links to the Power BI blog to get all the information on all the changes!

New to Power BI? Start here.

The big one here, in my opinion, is the ability to use custom visuals! Not only can you create your own visualizations to meet your organization’s needs, you can download other custom visualizations that have been shared in the Power BI Visuals Gallery. Some of these custom visuals are just awesome. The capabilities of this tool are off the charts (see what I did there?)!

Continue reading Tons of Updates to Power BI in October!

10 DAX Calculations for your Tabular or Power Pivot Model (Part 2)

This is part two of my 10 DAX Calculations blog series. In this series I’ve blogged ten different DAX calculations you may find useful in your Power Pivot, Tabular or Power BI model. So if you missed part one, I would encourage you to check that out.

Read 10 DAX Calculations for your Tabular or Power Pivot Model (Part 1)

So here’s five more DAX calculations (in no particular order) that I hope you will find useful. Enjoy!

5. DAX Use an Inactive/Custom Relationships in a Calculation

In Tabular and Power Pivot models, you are limited to only one Active relationship between two tables. Often in a relational database you’ll have a role-playing table or a table that is related to another table with more than one foreign-key. A common example of a role-playing table is a Date dimension table since our fact tables often have multiple dates, which is what the following screenshot depicts. We can see that there are two relationships between the Sales and Date tables.


The solid line indicates that this is an Active relationship. The dotted line indicates that relationship is Inactive. In the Manage Relationships window, you can clearly see that one of the relationships is Inactive (highlighted in red). What this means for us is that during slicing, dicing and filtering the Active relationship is used.

The good news is that we can still build calculations that leverage Inactive or unspecified relationships in our model! To do this we can use the USERELATIONSHIP function. This function allows us to specify which columns to relate two tables together.

Here’s an example of a calculation for Total Quantity that uses the relationship highlighted in red in the above screenshot:

Total Quantity by Update Date:=CALCULATE([Total Quantity],USERELATIONSHIP(Sales[UpdateDate],'Date'[Datekey]))


So when we slice the Total Quantity by Update Date calculation by the Date table, the relationship used is the not the default Active relationship as is the case with the Total Quantity measure. We can clearly see this illustrated below in the pivot table:


4. DAX Semi-Additives Measures Opening Period and Closing Period Calculations

Sometimes we have a requirement to create a semi-additive type calculation. If you’re familiar with creating SSAS multidimensional cubes, you’re probably familiar with the concept of semi-additive measure. A semi-additive measure is a measure that is not fully aggregate-able meaning that to find the correct measure amount we cannot aggregate the measure across all time. Consider a measure for account balances or product inventory. If we wish to calculate the current product inventory for the year we must return the inventory on the last day of the year instead of summing the account balances for each day in the year.

There are a couple useful DAX functions we can use to find the opening period balance/inventory or the closing period balance/inventory: FIRSTNONBLANK and LASTNONBLANK.

Here is an example of an opening period type calculation. This calculation will return the first non-blank value in a given date range. Ideally, this will be the product inventory on the first day of the month or year.

Starting Inventory:=CALCULATE([Sum of Inventory], 
FIRSTNONBLANK('Date'[Datekey],[Sum of Inventory]))


And here’s a closing period type calculation. This calculation will return the last non-blank value in a given date range.
Closing Inventory:=CALCULATE([Sum of Inventory], 
LASTNONBLANK('Date'[Datekey],[Sum of Inventory]))


And here’s a sample of the query results:


Now I can easily calculate inventory growth rates!

Inventory Movement:=[Closing Inventory]-[Starting Inventory]


Inventory Movement Percentage:=DIVIDE([Inventory Movement],[Starting Inventory])

Pretty cool stuff!


3. DAX Previous Year, Previous Month, Previous Quarter Calculations

There’s a series of specific DAX functions that I think you’ll find useful for calculating the measures for a previous period:




In part 1 we looked briefly how to calculate the previous MTD calculation, but these functions can also just be used to calculate the previous period metrics and compare the previous period to the current period.

Here’s a couple of examples of using PREVIOUSMONTH and PREVIOUSYEAR to calculate the Starting Inventory for the previous periods:

Previous Month Starting Inventory:=CALCULATE([Starting Inventory],PREVIOUSMONTH('Date'[Date]))


Previous Year Starting Inventory:=CALCULATE([Starting Inventory],PREVIOUSYEAR('Date'[Date]))


In the query results we can now see that we’re able to calculate the previous month and previous year starting inventory amounts. Excellent!


2. DAX First Day of Year, Quarter or Month Inventory/Balance Calculations

The previous examples in this post have all been calculations relative to the level of the Calendar Hierarchy being browsed, but sometimes we just need to calculate the measure on the first day of the year, quarter or month no matter what level we’re browsing. For those situations, we can use the following functions:




Here you can see the calculations for the Year Starting Inventory and Quarter Starting Inventory:

Year Starting Inventory:=CALCULATE([Starting Inventory],STARTOFYEAR('Date'[Date]))


Quarter Starting Inventory:=CALCULATE([Starting Inventory],STARTOFQUARTER('Date'[Date]))


And now we have the Inventory Amounts for the first day of the year or quarter:


1. DAX Rolling Sum and Average Calculations

This type of calculation is easier to set in a multidimensional cube in my opinion but with that said I am more of a cube guy. To get this working, I first created a calculation that sums the total Sales Amount for each day in the past 90 days.

To do this I used the following functions:





And here’s the calculation:

Rolling 90 Day Total Sales Amount:
    = CALCULATE([Total Sales Amount], 
            FIRSTDATE( /* FIRSTDATE gets the first date in this date range which will be 90 days ago */
                DATEADD( /* DATEADD lets me navigate back in time */
                    LASTDATE('Date'[Datekey]) /* This returns the last day no matter if I'm at the day, month or year level */
            LASTDATE('Date'[Datekey]) /* Use the max date for the end date */


Review the comments in the code above to get a better idea of what all the functions are being used for.

Then I created a measure that counts the days that exist within the past 90 days. If the day I’m counting is the first day in my calendar that I have data, I only want to count that one day. This calculation is very similar to the previous calculation except I’m using the COUNTX function to count the rows returned.

Day Count:
    = COUNTX(
    ,[Total Sales Amount])


Now that I have a calculation to get the rolling total sales amount and count the days all I need to do is simply divide the two numbers:

Rolling 90 Day Average Sales Amount:
    =DIVIDE([Rolling 90 Day Total Sales Amount],[Day Count])


And here’s the query results:


Notice that for January the Day Count measure shows only 31. This is because prior to January there’s not data so the Rolling Average is only considering days where data exists. Also notice that the numbers for March and Q1 match since both periods should have the same amount of sales and the same number of days.


10 DAX Calculations for your Tabular or Power Pivot Model (Part 1)

Watch this Power Pivot 101 webinar recording


Let me know your thoughts on these calculations! There’s 100 ways to skin a cat so if you have a different or better way to perform some of these calculations I’d love to see your solution! Thanks for reading! :)

Data Warehouse Design Challenge: Relating a Temporal Fact Table to a Date Dimension

This past week I ran into an interesting challenge with a client. The data warehouse is capturing testing data for an educational institution. In the screenshot below, you’ll see Continue reading Data Warehouse Design Challenge: Relating a Temporal Fact Table to a Date Dimension

Drill Down with Power BI Visualizations

A couple weeks ago in a recent update to Power BI, an enhancement was added to enable a drill-down action in Power BI. This has been something that customers have been clamoring for but now its finally here!

Not all visualizations can be used to invoke the drill-down action. Here are the visualizations that you can use the drill-down action in Power BI: Continue reading Drill Down with Power BI Visualizations

Power BI Tips, Tricks & Best Practices Webinar Recording & Materials Now Available


Thank you to everyone that attended my Power BI webinar last month, September 29th. Sorry its taken me a while to finally make the information available, but my schedule has been crazy lately! The good news is, however, the recording is available! So if you weren’t able to watch the webinar live, you can still catch the recording anytime you like.

View the Power BI webinar recording

Continue reading Power BI Tips, Tricks & Best Practices Webinar Recording & Materials Now Available

Thank You for Attending my #SQLSatOrlando Session! Slides, Resources, Recording

SQL Saturday #442 in Orlando, FL has come and gone but what a turn out! The event was excellent, we had a great turnout for our session and had a blast! And as a bonus, the BBQ lunch, baked beans, coleslaw, mac n cheese and dessert were amazing. Seriously one of the best lunches I’ve had a SQL Saturday event! Plus, the Lego name tags were epic! 100% without a doubt the coolest name tag ever.

Continue reading Thank You for Attending my #SQLSatOrlando Session! Slides, Resources, Recording

How I Got my Start at Pragmatic Works

October 4th of this month was my seven year anniversary as an employee of Pragmatic Works. Things have changed a lot over the past seven years. Working with the wonderful people at Pragmatic Works has been quite an amazing journey and incredible opportunity. With that in mind I thought that I should share my story of how I ended up working in the business intelligence field with the great team at Pragmatic Works. Continue reading How I Got my Start at Pragmatic Works