The Microsoft Power BI team was fast and furious in 2015, and there are no indications they are slowing down in 2016. If you haven’t checked out Power BI V2 since it was first released last summer, you might want to take another look. Many features have been added and updated since then. Based upon the release schedules since July, it seems there are 3 separate release cycles for Power BI:
The Power BI Service (PowerBI.com) gets weekly updates.
The Power BI Desktop tool gets monthly updates.
The Power BI mobile apps get monthly updates.
I expect no fewer than 6 updates per week from the Power BI team.
To my surprise, Power BI only lets you put multiple values on columns in a matrix. You can’t stack metrics vertically. Note: this is true as of 8 Jan 2016 but may change in the future. If you agree that this should be a feature in Power BI, please make your voice heard and vote for this idea on the Power BI forum and encourage others to vote for it as well.
The answer is a little complex. Considering how frequently Power BI gets updated, hopefully they’ll make this a bit easier in the near future.
Not only can we create and download custom visuals from PowerBI.com to extend the capabilities of Power BI, we can use R to create a ridiculous amount of powerful visualizations. If you can get the data into Power BI, you can use R to perform interesting statistical analysis and create some pretty cool, interactive visuals.
Dustin and Jan Mulkens are working on similar posts at the same time, so watch both of them.
Jan Mulkens has started a series on combining Power BI and R.
Fact is, R is here to stay. Even Microsoft has integrated R with SQL Server 2016 and it has made R scripting possible in it’s great Azure Machine Learning service.
So it was only a matter of time before we were going to see R integrated in Power BI.
From the previous point, it seems that R is just running in the background and that most of the functionality can be used.
Testing some basic functionality like importing and transforming data in the R visual worked fine.
I haven’t tried any predictive modelling yet but I assume that will just work as well.
So instead of printing “Hello world” to the screen, we’ll use a simple graph to say hello to the world.
First we need some data, Power BI enables us to enter some data in a familiar Excel style.
Just select “Enter Data” and start bashing out some data.
I’m looking forward to the rest of the series.
Using Azure ML and a free subscription to the Text Analytics API, I’m going to show you how to perform sentiment analysis and key phrase extraction on tweets with the hashtag #Colts (after this past Sunday’s 51-16 beat down of the Colts at the hands of the Jacksonville Jaguars, I’m bathing in the tears of Colts fans. Watch the highlights! ). Although my example here is somewhat humorous, the steps can be used to perform sentiment analysis and key phrase extraction on any text data as long as you can get the data into Power Query.
This is a fantastic example of how Azure ML can be used. Read the whole thing.
Our financials are the logical first place to start. And our financials are in the hands of our accounting firm. Specifically, they are stored in Quickbooks.
This, of course, poses a problem. Because like ALL accounting and ERP systems, Quickbooks is primarily focused on being a great accounting system. A system that collects, stores, organizes, and routes data. Quickbooks is NOT an analytics tool.
And being an analytics (or BI or reporting, whatever you call it) tool is a full-time job. ANY system whose job it is to collect/organize/route data will NEVER be sufficient for reporting and analysis. NEVER. I’m not kidding. We should never expect different, and that’s not a “knock” on these vendors. It’s just too many missions for any one company to execute.
This is a nice walkthrough of how you can apply visualization and analytics concepts, especially in a small business scenario.
Select the funnel from the visualizations (1), select track in the field list (2) and drag track to the values box (3). (Image 5 below) Now we need to customize this visualization. Select the paint brush to edit. (Image 6 below) I recommend giving each of the tracks a different color. Since Tracks are determined by the organizer the data maybe similar so you might want to use the same colors for more than one data point. You should also update the title Count of Tracks by Track sounds silly. Now we have a lovely display of session distribution by track.
She came up with a nice-looking set of information describing sessions and presenters for SQL Saturday Nashville 2016. I love seeing this kind of thing and hope it becomes mainstream among SQL Saturday organizers (maybe to the point where some of this is built into the SQL Saturday website).
For example, given a gzip file that contains a single csv file, here’s an example M query showing how the Binary.Decompress() function can be used to extract the csv file from the gzip file and then treat the contents of the csv file as a table:
He goes on ot show how Binary.Decompress is used to read Excel XLSX files.
Reza Rad has a three-part series on applying BI tools (specifically, Power BI) to Fitbit.
So for this post we are going to build that dashboard (not all of that obviously, because we don’t have the data required for all of that), but most part of it with Power BI. You will see how easy and powerful is Power BI in this kind of scenarios, and you will see how you can be the BI Developer of Fitbit in a few steps of building this demo.
Unfortunately Power Query or let’s say Power BI doesn’t have a loop structure, and that is because of the functional structure of this language. However there are data structures such as Table and List that can be easily used with each singleton function to work exactly as a loop structure does. Here in this post I will get you through the process of looping into files in a directory and processing them all, and finally combining them into a large big table. You will also learn some Power Query M functions through this process.
Fitbit calculates based on my current weight and age (I assume) how much calories I have to spend each day. I don’t know that calculation, So I create a static measure with the value of 2989 for the amount of calories I have to spend each day. I also create StepsCap measure with 12000 value showing that I have to walk 12000 steps a day, and another one for FloorCap with the value of 10. I created a Calories HighEnd measure with 5000 calories as value (I will die if I burn more than that!). You can create all these measures easily in Data tab.
This is a nice combination of work and play, building an interesting system with a data set interesting to the author and freely available.
And there you have it: a parameter table in PowerBI.com. To be honest, I think there are slightly too many fiddly steps for users to follow in this technique for me to be happy recommending its use unconditionally, but it should be useful in some scenarios. Hopefully there will be an easier way of accomplishing the same thing in Power BI in future…
Sounds like it’s not as easy to do as in Power Query, but Chris does provide nice step-by-step instructions.