Oops, I did it again. It’s that time of the year. A day later than last year (sorry about that ;))….
Back by popular demand: the Top 2000 visualized in Power BI. Read all about it in last year’s post.
Enjoy and happy holidays! See you next year.
Esri is a leader in the GIS industry and ArcGIS is a very popular product to build great maps. Now, you can use ArcGIS maps in Power BI (in preview). See the official information here: https://powerbi.microsoft.com/en-us/blog/announcing-arcgis-maps-for-power-bi-by-esri-preview/. This is really cool, I know a lot of you have been asking for this for a long time!
You will find the option to enable this preview in PowerBI.com, not in the Power BI Desktop. Log in to PowerBI and open the settings. You can find the ArcGIS preview there and enable it by simply selecting the checkbox:
With that enabled, create a report with some geographical information (or edit an existing one). I used the Google Analytics data that keeps track of my blog. Google Analytics data can be loaded into Power BI simply by using the content pack. In edit mode in the report you will find the ArcGIS component in the Visualizations list:
Click it and create your map as you would with the normal map. I noticed it needs some time to build the map (probably due to the preview) but once it is done it is fully interactive with the other items on your report as you would expect:
You can change a lot of the ArcGIS options, such as switching out maps, changing symbol styles, adding reference layers, etc.
I love this – the awesome power of ArcGIS and Power BI combined! I cannot wait to see what you will create with this.
Just a quick post: Inviting all to visit Experts Live 2015, 19th of November in Ede, the Netherlands. I will be speaking on BI and Big Data.
See http://www.expertslive.nl/ for more info on the event.
Hope to see you there!
I used to spend quite some time on building and re-building Microsoft BI demo machines. As you can imagine this manual process takes a lot of time and effort. Therefore (and also for my own education on PowerShell) I decided to look into automating the whole process. I will explain this in this series of posts.
In the end, we want to have a virtual machine that is configured as follows: Windows Server 2012 R2, with Active Directory Domain Controller role. Additionally, SQL Server 2014 is installed and configured as well as SharePoint 2013. Finally, the BI tools like Power Pivot and Power View are configured.
Ok, but how do we build such a machine?
Here are the steps to take. I always do them in this order, partly because there are some dependencies and partly because it stops me from going insane.
In this blog series I will share my PowerShell code to accomplish this. Please note that I am not a developer so things can probably be done a lot smarter J
Next step is preparation: the install files.
Azure Machine Learning is Microsoft’s cloud data mining and machine learning solution. It features a studio that is fully web based. One of the best features is integration with R through the ‘Execute R Script’ component. One of the best things of R is the plotting capability and I recently decided to try to make R plots from Azure ML studio. It is amazing how easy this works and it really brings the power of Azure ML together with the great exploration, plotting and data manipulation capabilities of R.
Here is a very simple sample I made:
I used to Flight Delays sample dataset from Azure ML to make this. In the ML Studio you will need to create a new experiment and drag the ‘Flight Delays Data’ component to the canvas. The only other component you will need to drop on the canvas is ‘Execute R Script’ (I told you this was a very simple example). Drag a line from the data to the left most input port of the R script container like so:
Click on the R script component and edit the R script on the right. Here is my script:
dataset1 <- maml.mapInputPort(1);
data.set = rbind(dataset1);
with(data.set,plot(DepDelay ~ DayOfWeek, col = "blue", pch = 20));
This script gets the data from the input port and rbinds it into data.set. Then I executed a very simple plot using the plot base R package to create the plot shown above. The last line of this code is not even necessary but it was there by default.
After running the experiment the plot can be seen by selecting the right output port of the ‘Execute R Script’ container and selecting ‘Visualize’:
The plot will be at the bottom of this page.
Pretty cool huh? Stay tuned for more as I will continue experimenting with R integration in Azure ML as well as other ML things.
I would like to take this opportunity to wish you all a very happy and data-filled 2015. Thanks for visiting my blog this past year!
Data nerd as I am, I could not resist sharing with you this blogs annual stats report:
Frequently, partners ask me about getting access to Power BI for a little longer than a trial period. Up until recently the only answer I could give was there was nothing available. However, there is news! Partners can now get access to Power BI through the Partner benefits portal. This blog explains it all: http://blogs.technet.com/b/uspartner_ts2team/archive/2014/07/10/power-bi-for-office-365-now-available-to-competency-partners.aspx
Hope this helps!
Just a quick Power BI Pro Tip this time: if you use linked tables to add data to your data model in Excel, before you press the ‘Add To Data Model’ button be sure to go to the table properties in Excel and give your table a better name (better than Table X). This makes figuring out which data you are looking at so much easier. You will thank me later J
One of the more frequent scenarios is listing the top X results, such as most profitable products, biggest customers, top 10 best selling stores, etc. Also doing a top X selection helps reduce clutter in charts: a lot of data points can work as noise and obscure the data points that really matter and make the biggest impact.
In this post I describe an approach to implementing these scenarios using Power Pivot’s RANKX() function.
Let’s start with a simple dataset consisting of products (P1…P20 in my sample), Cities, Sales Amount and Number of products sold:
After adding this table to the Power Pivot data model, we can use the RANKX() function to get the best selling products / cities etc. I added the following measures to my table:
Sum of Sales Amount:=SUM([Sales Amount])
Sum of Number Sold:=SUM([Number Sold])
Rank of products by sales amount:=RANKX(ALL(Sales[Product]);[Sum of Sales Amount])
Rank of city by number sold:=RANKX(ALL(Sales[City]);[Sum of Number Sold])
These measures allow me to determine the top selling products by sales amount and best cities by number of products sold.
Only thing left to do is to use a Pivot Table / Pivot Graph or Power View / Power Map visualization and display the results.
If you create a new Pivot Table and add the Product column and the ‘Rank of product by sales amount’ measure you get the following:
So how do we get the top 10 selling products by sales amount is a nice ordered fashion? Very easy, just a matter of the right sorting and filtering. Click on the little downwards pointing triangle button at Row Labels and choose ‘More Sort Options’. There I chose Ascending and then selected the rank measure:
Now the Pivot Table is sorted by rank with the highest ranking product at the top. Now, to filter out only the top ten, we press the same button again and choose Value Filters and then Top 10. Here I made the following selections:
This seems maybe a bit counter intuitive, but what this does is return the lowest ten ranks (which would be 1 to 10 or the highest ranking products). Alternatively I could have used a Lower Than or Equal To Value Filter with these settings to produce the same result:
And here it is: a top 10 of products by sales amount.
Of course, you can also use Power View or Power Map to visualize these results. Here is a Power View based on the same information:
The trick here is to create the visualization just as normal (as above). Above displays the sales amount by product and the number sold by city. However, the catch here is that both the graph as well as the map have a filter on them that utilizes the rank measures I created. Here is the filter for the chart. The ‘Rank of products by sales amount’ measure is filtered to showing only values less than or equal to 10, i.e. the top 10.
What’s best about this is that it is very easy to change from top 10 to top 15 to top 5 or anything you desire. Also, the Power View is fully interactive. For example, clicking on one of the cities on the right shows which products are sold in that city. Note that it does not show the top 10 products in that city however.
Hope you liked this Power BI Pro Tip!
If you want to keep track of what is happening on Office 365 and what updates are rolling out and coming down the line check this site:
Trust me, a lot is happening on Office 365!