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A Power BI Technique Mined from the Power Pivot Archives

Below, you will find one of our all-time favorite Power BI techniques. In the ten (yes ten) years we’ve been operating this site, we’ve written over 1,000 articles on Power BI.  Hard to imagine - even for us - but true.

Years ago, we first wrote up this technique in the context of Power Pivot – Power BI’s cousin and predecessor, which we like to call “Power BI in Excel.” 

Since the two products share the same brains (DAX and M), this technique is more relevant today than ever. Everything below is 100% legit for Power BI – the same data model “shape,” the same DAX, etc. – but now you can use it to power up your Power BI visuals, and not just PivotTables.  Enjoy! Smile

With everyone getting their hands on CTP3, I decided to take a short break from the football project and show something else that may spark you to try things you otherwise might not.

So, let’s go behind the scenes of the Temperature Mashup example (that’s mentioned here and here).

Part One:


Part Two:



Briefly, here are the steps covered in the videos:

  1. Copying the temperature data from Excel and pasting as a new table in PowerPivot
  2. Using CONCATENATE to create “key” columns in both the Temperature and Sales tables
  3. Creating a relationship between those tables, using the key columns created in 2
  4. Demonstrating that the relationship enables slicing sales by temperature
  5. Using a nested IF formula to add a new column to the Temperature table, mapping granular average temperature values into the four buckets Cold, Cool, Warm, Hot
  6. Using that newly-calculated column to slice sales numbers instead

Next up…

Using DAX to create a “Sales per Day” measure! 🙂

Rob Collie

Rob Collie

One of the original engineering leaders behind Power BI and Power Pivot during his 14-year career at Microsoft, Rob Collie founded a consulting company in 2013 that is 100% devoted to “the new way forward” made possible by Power BI and its related technologies. Since 2013, PowerPivotPro has rapidly grown to become the leading firm in the industry, pioneering an agile, results-first methodology never before seen in the Business Intelligence space. A sought-after public speaker and author of the #1-selling Power BI book, Rob and his team would like to help you revolutionize your business and your career.

This Post Has 8 Comments
  1. Okay, now I finally see why you are so excited about this stuff. As somebody who doesn’t normally do BI… it was a bit hard to grasp the power, but that was crazy cool.

  2. Great video Rob, i can show it to my non BI colleagues and managers to let them see what PowerPivot is all about. One quick question, will PowerPivot be translated to other languages as excel is too? The IF excel function is called ALS in my native tongue.

  3. Why have you not removed the “Grand Total” from the pivot table as it is meaningless in the context of “Average” and may confuse as it implies “SUM” is valid for different “items”.

    1. Do you mean in this video or in another video? In the videos above, I think all of the measures are SUM, correct?

      And while the “Grand Total” label itself may be misleading when the measures are an Average, it is quite often still a valid and useful number to see – the average of the entire data set (minus slicer filters of course).

  4. Hi, powerpivotpro!

    I noticed your website from CIMA e-mails. I think it is quite amazing. One of my career goal is to build a BI for a company and PowerPivot is just the tool I need!.

    I have finished the two vedio above, however, where is the DAX? How can I find the 3 part?


    1. Hi there Aden, thank you for the kind words 🙂

      Not sure what you mean by part three, but: has all of my videos

      most posts on this site deal with DAX.

      i just finished a book on DAX, you might consider the eBook version (link in my most recent post)

  5. Hi Rob,
    Great resource. I didn’t realise that PowerPivot is such a great tool until three days ago.

    I have one question in regards to the way you set up your measure “Qty per Day”. Do you think it would be a bit more informative to divide quantity by a number of days in each temperature range? My thinking is that overall you may have a lot of cool days for example(let’s say 50), but only sold anything on five days, whereas in 15 warm days that you had you sold on four. You can get a result saying that cool days are better for you business, than warm day, when if fact that would be a lie in real life.

    Thanks for sharing your knowledge Rob!

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