Yeah, It’s Hardly an Original Pun So… the United States ran a massive numerical “experiment” last night, and it didn’t turn out the way any of the experts predicted. What does this tell us “numbers people” about our chosen fields?…
“I Want to Taste You But Your Lips are Venomous, PWAH-SAAHHHNNN!!!!”
(Get It? Poisson/Poison? OK, Read on for a Bell Biv Devoe Reference)
Intro from Rob
Um, wow. A few things:
- Brace yourselves for a dose of awesome.
- I don’t understand everything that’s happening in this guest post.
- So if you “get” all of this, fantastic.
- If you don’t, don’t sweat it – just bask in the power of our toolet – it can truly do anything.
- Our new friend Josh is absolutely killing it with his song references.
Take it Away, Josh…
Since taking on a role in Work Force Management about a year ago, I’ve learned one thing: Staffing a call center is expensive. What I mean is: the staffing software, it’s is rather pricey. So much so, that smaller call centers just can’t afford the tools needed to easily create an accurate staffing model.
But as someone raised to the mantra of: “if you are going to do something, do it right” I decided to learn me some DAX. (To be fair though, what my dad really said was: “Aim low, that way no one can tell when you fail.” But for the sake of this post, we’ll go with the first quote. )
Luckily, Rob was nice enough to teach us the core of using complex equations in his Experiments in Linear Regressing, Parts 1 & 2. So we won’t be entirely lost in new territory, it’ll be more of a: “lost with friends and colleagues, ‘Danger Will Robinson’” sort of excursion.
Using RankX and SumX to create a weighted moving average
The staffing model I use relies on a weighted average of the 4 most recent weeks of incoming calls. Often times however, a week’s data may have been inaccurate, causing us to go a week further back.
The way a weighted average works is that each number is multiplied by the given weight and then divided by the the sum of all weights. So the weights 40, 30, 20, and 10 are assigned to the weeks, giving us an average number of calls that is more heavily influenced by the most recent week.
I include it here because the interactions between the eight weight measures are really, really neat to watch.
Guest Post by Avichal Singh
Intro from Rob: Never fear, last week’s series is still slated for completion, and in a special way. Watch this space on Thursday for some fireworks. For now, please enjoy Avi’s thoughts on the new forecasting component of Power View / Power BI.
PASS Business Analytics conference saw the announcement of a pretty cool Power View feature: Forecasting. I felt lucky to have been there and also to have had the opportunity to attend both of Rob Collie’s sessions (Data Revolution, Industrial Strength Excel). The Data Revolution session, I must say, was unlike anything I expected. No DAX formulas, no bullet points; just a path to data nirvana
The Power View forecasting feature was cool enough that I just had to play with it! I wanted to try it out with a few real world data sets. I ended up using Climate Data and Stock Market performance.
– First a quick look at the Power View forecasting functionality
– Then I show you how I built the files using Power Query (The more I use that tool the more I like it)
You can find the link to the finished Excel file here. You can also watch me walk through the whole process in the video below:
Power View Forecasting in a Nutshell
In the ‘cloud first’ spirit that Microsoft has been following, the forecasting feature is only available in the online Power BI site (See microsoft.com/powerbi for more and to sign up for a free trial). To enable the forecasting feature, after opening your file on the Power BI site, you need to switch to the HTML5 mode by clicking on the icon at bottom right.
|Click on this icon to enable the HTML5 mode with forecasting functionality|