PowerPivotPro

PowerPivotPro is Coming to Phoenix

February 20 - 22, 2018

Registration for 2018 Public Training is now open!

AVAILABLE CLASSES

**Use the discount code “3ORMORE” when signing up 3 or more people.

FEBRUARY 20 - 21

Foundations: Power Pivot & Power BI

Instructor: Kellan Danielson

Super charge your analytics and reporting skills with Microsoft’s dynamic duo. Designed to handle huge volumes of data, these tools will transform the way you work! Two Days in our class and you are EMPOWERED!

  • Learn Microsoft’s secret weapon behind Power Pivot & Power BI: DAX
  • Taught by Kellan Danielson – PowerPivotPro Partner and Vice President of Client Services
  • You don’t need to be an IT professional – most of our students come from an Excel background

FEBRUARY 20 - 21

Level Up Series: Advanced DAX

Instructor: Ryan Sullivan

The Advanced DAX Course was such a hit in the first half of 2017 that we’ve expanded the course to 2 days!

Overview

  • This advanced DAX training class is taught completely in Power BI Desktop.
  • Students are encouraged to take our Foundations course and have hands on experience with the DAX language.
  • Taught by Ryan Sullivan – Principal Consultant.
  • Class material drawn from usage of Advanced DAX applications while consulting with hundreds of international firms.

FEBRUARY 22

Level Up Series: Power Query for Excel & Power BI

Instructor: Krissy Dyess

The second class in the series is our Level Up Series is Power Query for Excel & Power BI.

  • Students are encouraged to take our Foundations course and have hands on experience with Power Query in Excel or Power BI Desktop.
  • Taught by Krissy Dyess – PowerPivotPro Principal Consultant and Phoenix native!
  • We will cover common to unique business challenges made easy with Power Query’s data wrangling capabilities.
  • Intermediate to Advanced Level Power Query best practices distilled into easy to understand patterns to apply to your most common business challenges.
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Microsoft Excel DAX techniques for Business Intelligence Profressionals

Excel 2016: Ten Heartwarming Improvements

Post by Rob Collie

We Love Power Pivot in Excel 2016

Seriously, I Want to Hug the Computer and Every Software Engineer in Redmond

Tales from the Preview

I’m traditionally very slow to look at interim releases of software, but the Office 2016 public preview is out.  Everything listed below is now also available to you to look at as well.  Just go grab the preview and slap it on a “spare” computer.

***UPDATE Oct 2015:  The preview period is now closed, but this post will help you find a version of Office 2016 that includes Power Pivot.

Rundown of Improvements

The next release of Excel (2016) brings MAJOR improvements to our world.  Unlike 2013, which offered us little noticeable benefit over 2010 Power Pivot, I can’t wait for 2016 to become mainstream.  It’s a monstrous win.

Each of these improvements warrants its own in-depth blog post, but for now, let’s just run through the list of things that catch my eye…

Measure Icons and Search in the Field List!

Measure Icons and Field List Search are Back in Power Pivot / Excel 2016

We Had Both of these in 2010.  2013 Took Them Away.  2016 Puts them Back Smile

A bit cosmetic perhaps, but if you never used 2010 Power Pivot, you have NO IDEA how useful these are.  We welcome them back to our world with open arms.

Even Better:  Right Click and Edit Measures in Field List!

Read the Rest
RANKX With Ascending Order To Show Lowest Quotes By Vendors

RANKX with Ascending Order to Show Lowest Quotes by Vendors

By Avi Singh [Twitter]

This post is based on a query that I got in our monthly Q&A session held for our Online Class attendees.

Input = Multiple quotes for different Products from different Vendors
Desired Output = For each Product show the top three quotes, both price and the Vendor name


Go from a list of Price Quotes to showing the lowest Vendor Quotes for each Product

A bit more on the Q&A session before we dive in. All our Online Class attendees are invited to a monthly Q&A session, in order to support them in their Power BI journey. Often what you learn in class, you would only apply sometime later. With the Q&A session, if you run into issues or have any questions, you have the opportunity to bring it up and discuss with your instructor. You can sign up for our upcoming Online Live Class on August 3-4.

Step 1: Structuring the Tables

We would clean things up and import the data into separate data and lookup tables. This may seem superfluous for the sample data set, but a real data set could have a lot more rows in the data table and a lot more columns (attributes) for the lookup table. Hence separating the data table and lookup tables is always a good approach.


Our Vendor Quote data loaded as separate Data and Lookup Tables

Step 2: Brainstorm Approach to Writing Measure

Read the Rest

Cycle Plots Show You Data Insights using Power Pivot

By Avi Singh [Twitter]

If you want to look for trends based on weekday (Sunday…Saturday) or month-of-the-year seasonality (January…December), Cycle Plots can be a potent visualization tool. Some clever folks thought of this back in 1978, but my education on cycle plots has been from Naomi Robbins’ excellent paper. This question was asked during one of our Q&A sessions for the Online Class (Next Class scheduled for Aug 3-4). In this post I’ll discuss the Cycle Plot and then we would build it step by step using Power Pivot. Here is the end result we will achieve (animated gif):


Cycle Plot showing Weekday values for each Week Number and the Average. A slicer also lets the user select the weekday chart should start at

Looking for Periodic Trends

We’ll use sample data showing eight weeks worth of web traffic and we are looking for trends based on the weekday (Sunday…Saturday).

Try 1: We might plot Visitors by Weekday. While this does show the overall pattern of visitors across weekdays, we have no information of how visitor count is changing over those eight weeks.


Try1: No information on how visitors trend changes over the eight weeks

Try 2: We can plot visitors by date. This shows a trend over time and the cyclical pattern is apparent. However, it is hard to track the performance of a single weekday, say Monday over the eight weeks.


Try 2: Hard to track a single weekday (say Monday) over the eight weeks

Try 3: We plot by weekday but add Weeknum as a series. This has a lot of information coded in the graph, but that is also the problem. Viewer is overwhelmed and it is hard to look for the patterns we want to see.


Try 3: A little too much information

Cycle Plot

A Cycle Plot would show data for each weekday broken by the Weeknum. With the same data now rendered as Cycle Plot, you can see the trend for each weekday and see them in relation with other weekdays. Insights just start hitting you on the head!


Cycle Plot: Click to enlarge, see numbered insights below

1) Friday is the peak traffic day and Mon/Tue are the low traffic days.
2) Thu shows strong growth, especially in the recent weeks
3) Fri shows steady growth
4) Sunday shows a gradual decline (except for last week) and may need closer monitoring

Building a Cycle Plot Using Power Pivot

Read the Rest

Counting Overlapping/Shared Twitter, Facebook, Instagram, etc. Followers

Post by Rob Collie

From Last Week’s Client Work

Last week a client asked us to solve a somewhat unusual problem:  given any two lists of Twitter followers, tell us how many followers “overlap” between the two lists.

Two Lists of Twitter Followers:  How Do We Find the Overlap Using Power Pivot / Power BI / DAX?

How Many of Han Solo’s Followers Also Follow Leia Organa, and Vice Versa?
(Randomly-generated Twitter handles are funny.  I particularly like “@Gommo” and “@Xxfok”)

Loading the Data:  Using Power Query

Let’s use Power Query to perform the import this time, both because we’re using PQ a lot more around here now that we have Power Update, and because we’re gonna need PQ for the more complex steps later.

Note that all of the steps below are performed using Excel 2013.  (I find Power Query to be a bit too clumsy in Excel 2010.)

Power Query, aka Power BI Data Import

Importing from a Table Using Power Query:  Step 1
(Unchecked “has headers” because of the “Han Solo’s Followers” Row)

Read the Rest

The Diabolical Genius of “SWITCH TRUE”

Post by Rob Collie

SWITCH TRUE Alternative to Nested IF's

Did Someone Say Deliberately “Misuse” a DAX Function for Our Benefit?  We’re IN!

An End to Nested IF’s?  Sign Us Up!

When we first saw the SWITCH function make its debut in Power Pivot a few years back, it was a “hallelujah” moment.

Whereas we used to have to write nested IF’s, such as this:

   IF([MyMeasure]=1,expr1,
      IF([MyMeasure]=2,expr2,
          IF([MyMeasure]=3,expr3,…)))

Now , with SWITCH, we could write that much more cleanly as:

   SWITCH([MyMeasure],1,expr1,2,expr2,3,expr3…)

Which do you prefer?  It’s easy to make a strong “case for SWITCH,” isn’t it?

But What About Cases Other than Equals?

Now, let’s consider the following nested IF:

   IF([MyMeasure]<1,expr1,
      IF([MyMeasure]<2,expr2,
         IF([MyMeasure]<3,expr3,…)))

Notice that we’ve swapped out “=” for “<”.

And we can’t do that as a SWITCH, because SWITCH checks for exact matches between [Measure1] and 1 (or 2, 3, etc.)

This is unfortunate, because in these cases, we’ve had to keep using nested IF’s.  And wow do I (Rob) *hate* nested IF’s.  I can never seem to match the parentheses up correctly on the first try.

But There’s a Sneaky Antidote!  We CAN Still Use SWITCH!

Read the Rest

Power Pivot and Basketball Superstars: Many-to-Many and USERELATIONSHIP

By Avi Singh [Twitter]

Friends at a company play pick-up basketball during their lunch hour. Since there are no established teams, players can be randomly matched up. But these folks happen to be engineers/data-nerds, so they keep detailed track of games, teams, players and win/loss. The diagram view of the data is shown further below.

Question: How can we determine which player pairing is the most successful?
Since players are randomly teamed up, are there combinations which when teamed up have an unusually high winning percentage?


Word Cloud of Player Nicknames: Size of text indicates number of games played

Application: This would naturally extend to other sports, but I believe may also apply in many non-sports scenarios, where items are paired up somewhat randomly (or by design) and we want to know how effective those pairings are.

Thanks to Kirill Perian (basketball nickname K-Real), an attendee of one of our past webinars, who sent us the dataset and posed this question. Dataset has been simplified to showcase this scenario and anonymized to protect the identity of the losers 🙂

File can be downloaded here.


Model Diagram: Showing Game, Team and Team Players

First we will address the easier scenario of creating metrics for individual players, using the many-to-many pattern. Next we will take on writing measures to compare performances of pairs of players.

Read the Rest

Schrodinger’s Pivot, or Why CALCULATE() Should Be Your Favorite Function EVER.

Post by Rob Collie

Schrodinger's Cat Has Relevance to Power Pivot and Power BI

In the Classic Physics Thought Experiment, Schrodinger Hypothesized a Cat That Was Simultaneously Dead AND Alive.
(But here we will use the more humane “Simultaneously Green AND Grey.”

Back to Basics!

PowerPivotPro.com celebrated its 5th birthday back in November.  Over 5+ years, we’ve progressively covered techniques with an increasing level of sophistication.  That’s pretty natural – we ourselves have become more skilled over time, AND there’s a tendency to not want to write the same post twice.

But today I want to drive home a basic point, one that will help “recruit” Excel pros into the Power BI camp, AND that will help “crystallize” a few things even for the longtime DAX practitioners.

Schrodinger’s Cat – A Classic Battle of the Nerds

imageIn 1935, physicist Erwin Schrodinger wanted to show Albert Einstein how wrong he was.  Einstein had recently published a paper that made an astounding claim about the nature of subatomic particles.  If those claims were true, said Schrodinger, even “big” everyday stuff, like cats, could also behave in that same outlandish way.  Which made Einstein look kinda silly, in Erwin’s mind.

He proposed the idea of a cat that was both simultaneously alive AND dead, and basically said “See, Albert?  Alive AND dead is clearly impossible, so your theory is junk.”  See here for details.

But modern quantum physicists actually think the cat experiment does NOT disprove Einstein’s claim.  In fact, they think Schrodinger’s Cat demonstrates that the universe is fundamentally a MUCH stranger place than we typically think.

In short, the concept of “impossible” is subject to re-evaluation.

This PivotTable is Simultaneously Filtered AND Unfiltered

Read the Rest