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Nov

2015

Custom Indicators in Power BI using Chiclet Slicers

First of all, happy Friday! As we get ready to enjoy the weekend, I thought of noting down a quick tip on how to use the totally awesome Chiclet Slicer to display custom indicators in Power BI. If you are hearing about the Chiclet Slicer for the first time, please do check out the official Microsoft blog on this as it is a very useful viz. For people who follow my blogs, you would remember that I had already written down a technique to create Indicators in Power BI before. But the main drawback in that approach was that there was no way to color the indicators, and also we were limited by the set of Unicode characters that could be used as indicators. With the advent of the chiclet slicers, we can now dynamically display any image as our indicator and this post will precisely show you how to do it. For this demo, let’s say – I want to display a green up arrow or a red down arrow based on whether my measure is positive or negative. For that, follow the steps below:-

1) Open the Power BI desktop file where you want to add the indicator, and then go the data tab. Click on the New Table button.

2) It is important to understand that the chiclet slicer, just like the regular slicer, can only display table fields or calculated columns (and not measures). So we have to create a table with a list of all the “states” or possible values. In my case, we can have only 2 states – Up and Down. Use DAX to create a table with 2 rows – Up and Down. Also, add the image url for each of the state (in my case, an image url for the up and down arrows).

Indicator =
UNION (
    ROW ( “Indicator”, “Up”,
    “ImgURL”, “http://www.clipartbest.com/cliparts/nTX/EGB/nTXEGBLTB.png” ),
    ROW ( “Indicator”, “Down”,
    “ImgURL”, “http://www.clker.com/cliparts/D/8/S/c/z/3/red-down-arrow-md.png” )
)

Note that we are making use of the calculated table feature in Power BI to create a table with a list of states.

3) Let us say that I have a measure called Metric which shows either positive or negative value. Right now, I am just hardcoding it to -30.

4) Now create a new measure which will display 1 for Up if the measure Metric is >=0 or display 1 for Down if the measure Metric is < 0

LinkedMeasure =
SUMX (
    VALUES ( Indicator ),
    IF (
        (
            [Metric] >= 0
                && VALUES ( Indicator[Indicator] ) = “Up”
        )
            || (
                [Metric] < 0
                    && VALUES ( Indicator[Indicator] ) = “Down”
            ),
        1
    )
)

5) On the Report tab, add the Indicator column and the Linked Measure to the canvas, and then convert it into a chiclet slicer (make sure you download and import this custom visualization from the Power BI Visuals Gallery before this step). Also add the ImgURL field to the Image field. You can change the Image Split property under the Image section to 100 from the default 50, so that the Image occupies 100% of the space

6) Hide the borders and also turn off the headers, so that only the image is visible.

7) Now you can add a textbox besides the chiclet slicer to display the metric. Now go ahead and change the values of the metric, and you can see the chiclet slicer automatically update itself with the right indicator.

The chiclet slicer is pretty good on it’s own as a way to slice data, but the ability to display custom images takes it to the next level. You can use it for a lot of tips and tricks, and I hope this post gets you thinking on what all you can do with this. And there goes your weekend, BOOM!

Note

As usual, make sure you look at the date at which this post was published and the version of Power BI. Since Power BI has a rapid release cycle, I would expect some of the features to change. Hence, always check whether a new feature makes it more easier to implement your scenarios like this one. The version I used is given below.

Nov

2015

Performance Problems with IF statement execution in SSAS Tabular

Due to the high compression rates and stellar in-memory architecture of SSAS Tabular, most people with smaller models do not experience performance problems (in-spite of employing bad data modeling techniques and inefficient DAX measures). However, as the size of your models increase, you will start to see performance issues creep up, especially if you are not paying attention to data modeling and DAX measures. Last week, I gave a presentation at the PASS Summit 2015 on my experience of building a 150 GB Tabular model in SSAS 2012. During that, I shared my story on how some of the DAX measures with IF statements were causing performance issues and how to work-around that issue by rewriting your DAX measures. During that day, Microsoft also announced that they resolved this issue in SQL 2016, so I thought of demonstrating the issue, workaround and also the fix in SSAS 2016.

Issue in SSAS 2014 (& older versions)

For demonstrating the issue, I will be writing queries against the Adventure Works model in SSAS 2014 and using MDX Studio to show the server timings. Let me start with the below query

WITH MEASURE ‘Date'[test] = If ( 1 = 2, [Internet Total Sales], [Reseller Total Sales] )
SELECT NON EMPTY { [MEASURES].[Test] } ON COLUMNS,
NON EMPTY (
{ [Date].[Calendar Year].Children },
{ [Product].[Product ID].Children },
{ Geography.[Country Region Name].Children } ) ON ROWS
FROM [Model]

The above MDX query defines a DAX measure called Test, which depending on the condition displays either Internet Total Sales or the Reseller Total Sales (To make it simple, I just made a static condition 1=2 but that can be replaced by any dynamic condition also). The query results should display the Test measure for Year, Product ID and Country. Now, normally we would expect that the Test measure should only execute the true part of the IF statement. But let us execute this in MDX Studio and see what actually happens.

You can see that both the branches of the IF statement are being executed, even though we expect only the true part to be executed. For smaller models, it might not make a difference but for large models with expensive measures, this might cause severe performance issues.

Workaround in SSAS 2014 (& older versions)

The workaround for this issue is to rewrite your DAX such that we ensure that the measures get executed only if the condition is true.

WITH MEASURE ‘Date'[test] = CALCULATE([Internet Total Sales], FILTER(Currency, 1=2)) + CALCULATE( [Reseller Total Sales], FILTER(Currency, 1<>2))
SELECT NON EMPTY{[MEASURES].[Test]} ON COLUMNS,
NON EMPTY({[Date].[Calendar Year].children}, {[Product].[Product ID].children},{Geography.[Country Region Name].children}) ON ROWS
FROM [Model]

Note that the measure has been rewritten as the sum of two CALCULATE functions. The key is to use a table in the filter clause within the CALCULATE that satisfies the below conditions

Is related to the fact table of the measure
Is low in cardinality (you can also use a low cardinality column instead of a table)
Is not being used in the calculations for the measure/condition. If yes, do some extra testing to make sure the performance is not worse

The reasoning behind the table being connected to fact table is because the calculate() with the false condition has to evaluate to zero / BLANK so that the result of the Test measure would only be the one appropriate measure. If the table is not related, you will end up with the sum of both the measures. A low cardinality table or column is preferred because in this technique, you will see that there are 2 additional queries being sent to the storage engine, which evaluates the FILTER part for the two measures. If the tables have high cardinality, the time for the FILTER queries will take more time. The reason why I said that the table or column should not be used in the measure calculations or condition is because I have seen that in certain conditions, this could actually make the performance worse and still execute both the branches. So just make sure you do some extra testing.

That said, let us look at the scans for the above query. You can see that only the Reseller Sales measure is executed. Also, if you notice carefully, there are 2 extra scans which basically check the filter condition for Currency. In large models, these scans for low cardinality dimensions will be almost negligible and the time for these extra scans will be much lesser than the time taken to execute the other measure also. In this case, the Adventure Works model is just 18 MB, so you won’t see much of a difference.

New Optimization in SSAS 2016

SSAS 2016 CTP2.3 (and newer versions) has a new optimization for this issue – Strict evaluation of IF / SWITCH statements. A branch whose condition is false will no longer result in storage engine queries. Previously, branches were eagerly evaluated but results discarded later on. To prove this, let us execute the original query against SSAS 2016 and see the results.

Now we can see only the relevant measure is being executed. Also, it is faster compared to SSAS 2014 versions of both the original query as well as the workaround. I hope this article will help people who are not on SSAS 2016 to optimize IF statements, and also help understand what the new optimization in SSAS 2016 – Strict evaluation of IF / SWITCH statements actually means. There are also a bunch of new features and optimizations in SSAS 2016 and you should check them out!

Oct

2015

Quick Intro to Power BI Visuals Gallery

I don’t usually look forward to Mondays (especially after spending a very exhausting though rewarding weekend organizing SQL Saturday Charlotte), but then today was different. Amir Netz had already spoiled my weekend by putting out a teaser on Power BI and I was actually waiting for Monday to come so that I could remind him of his promise.

And well, he didn’t disappoint … This is such a great news to the world of dataviz. Let me quote his announcement below

I’ll admit it. I am very excited… So deep breath. Here is exactly what we are introducing today:

Custom visuals in the Power BI service and Desktop: The ability to upload and incorporate a custom visual, whether a broadly useful visual from our community gallery or a completely bespoke visual tailored for the needs of a single user, into the report and then share it with others. This is available in the Power BI service today, and in the Desktop next week.

The Power BI visuals gallery: A community site (visuals.powerbi.com) that allows creators to upload new Power BI visuals and for users to browse, select and download those visuals.

Power BI developer tools: With our developer tools every web developer can code, test and package new visuals directly in the Power BI service to be loaded to the gallery.

You can read more on this in the official blog post here. Let me use this moment to give a quick intro to the Power BI visuals gallery and how you can use some of the community examples to enhance your visualizations.

First of all, note that this functionality is only available in the Power BI service as of now and will be available in Power BI desktop next week. Also, the custom visuals can not be pinned to a dashboard as of now, but that feature should also be coming soon. That said, follow the steps below:- 1) Head over to https://app.powerbi.com/visuals and feel free to choose any of the awesome visualizations created by our community. For now, I am going to choose Hexbin Scatterplot (which was created by my colleague David Eldersveld and won the third prize in the Power BI custom viz contest– you might also want to check out his thoughts on Power BI Custom Visualization here) and KPI Indicator with status.

2) For each of the selected visuals, click on the visual icon and then you will be presented with the Download Visual window. Click on Download Visual button.

Read the terms of use in the next screen and then press the I agree button.

3) This will begin the download of your pbiviz files (power bi custom visualization files). Once the download is over, sign in to Power BI service and then open a new report. Click on the Ellipsis symbol (…) to import the two pbiviz files that you downloaded.

4) Now you can use those custom visualizations just like the existing ones. For e.g., I can create a hexbin scatter plot chart by selecting Sales Amount, Sales Quantity and Store name. Note how I change the default visualization to a regular scatterplot and then to a hexbin scatterplot. Also look at the benefits that a hexbin scatterplot gives over a regular scatterplot – you can easily see where the concentration is more, and you also have rug marks on your axis to show where the dots are. Feel free to explore the chart, you can watch how it works from the video in this link.

5) I added a regular bar chart for Sales Amount by Year on the bottom left to show the interactive features of the new charts. Then, I went ahead and added the Sales Amount and Sales Amount LY by Calendar Month and chose the KPI Indicator visual. Note how smoothly all the charts work with each other!

6) Feel free to explore further. For e.g., I added a slicer for ClassName also to check out the interactivity

7) You can save this report and share with others now. When you share a report that contains a custom visualization, you may be greeted with a warping that the report contains custom visuals. Click on the Enable custom visuals button to see the report.

You can see how easy it was for someone like me, who doesn’t know how to code, to incorporate these visualizations in my report. And for those who know to code, the possibilities are endless. As the community grows, we are going to get more and more of these awesome visualizations and this will greatly impact the lives of people in the data analytics industry. As for me, I can’t wait to see what all awesome stuff is going to come from the community and also what other surprises the Power BI team has in store for us (would be definitely tough to top this one though!)

Sep

2015

My Thoughts on Mold Filtering

Mold, mildew, and moisture problems are especially prevalent in states with hot, humid summers, such as Tennessee, Alabama, and Kentucky. Mold also presents a problem in the winter months under just-right conditions.

According to the EPA, our indoor environment is two to five times more toxic than our outdoor environment. In fact, in some cases the air measurements indoors have been found to be 100 times more polluted. One of the most insidious problems that can affect your home’s indoor air quality is mold.

Mold can grow undetected for months, even years, in areas high in moisture including:

Bathrooms
Kitchens
Basements
Crawlspaces

Leaks in ceilings, walls, and plumbing systems are ideal areas for mold to grow and proliferate. Mold can also develop in your HVAC system, which is one of the worst places for it to appear. Prevent most related negative conditions in your property by contracting the professional maintenance services from Massachusetts roofing and siding.

Mold and mold spores in the air can cause serious respiratory health effects including asthma exacerbation as well as coughing, wheezing, and upper respiratory symptoms in healthy individuals. The health effects of mold exposure are highly dependent on the type and amount of mold present in the home.

Mold & Mildew in the Home

What is mold? Mold is a fungus and a common component of household dust. In large quantities, it can present a significant health hazard, causing asthma and allergy attics, respiratory problems, and in some cases neurological problems and even death. For healthy lifestyle check these biofit probiotic reviews.

What does mold look like? Mold can be distinguished from mildew by its appearance. Mold color varies in shades of black, blue, red, and green. The texture is most often slimy or fuzzy.

What is mildew? Mildew is also a type of fungus. It usually grows flat on surfaces. The term is often used to refer to any type of mold growth. These are just some of Herpagreens benefits for health.

What does mildew look like? Mildew starts off as a downy or powdery white and often appears on organic materials, such as wood, paper, leather, textiles, walls and ceilings. It can turn to shades of yellow, brown, and black in its later stages.

Both mold and mildew produce distinct offensive odors, and both have been identified as the cause of certain human ailments, read about the benefits of one and done workout.

Ideal Conditions for Mold & Mildew

High heat (between 77 and 87 degrees), humidity (between 62 and 93 percent), and a food source (organic material) create the ideal environment for mold and mildew to thrive. That’s why June-August promote mold growth more than any other months. Warm temperatures and high humidity set the stage for mold and mildew.

There are a variety of molds found in the home including Alternaria, Aspergillus, Chaetomium, Cladosporium, Fusarium and many more. The toxic black mold associated with “sick house syndrome” is probably Stachybortrys chartarum. Click here for a list of common household mold types. Regardless of the mold type you have, it is important to remove it from any living spaces, including offices, and garages.

The good news is that there are a variety of ways to fight mold – or to keep it from developing in your home entirely. We’ve compiled a list of easy, proactive methods for keeping your home dry and your air free of mold and other airborne toxins.

Common Sources of Mold & Mildew

HVAC ductwork
under sinks and around tubs and faucets in bathroom and kitchen
in or around HVAC systems, dishwashers, clothes washers, and refrigerators
any area high in moisture

Sep

2015

My Thoughts on Calculated Tables in Power BI

Yesterday was a terrific day for all of Microsoft Power BI fans. Microsoft released updates for Power BI Service, Power BI Mobile and Power BI Desktop (with an unbelievable 44 new features) – which basically means no matter whether you are a developer, BI professional or an end user, all of you got something new to play with along with this release. The blogs give a lot of details on what those new features are, so I wouldn’t be going over them. But I wanted to take a moment to pen down a few moments on my thoughts on a new modeling feature within this release – Calculated Tables.

Chris Webb has already posted his thoughts on Calculated Tables in Power BI and I am pretty sure Marco Russo / Alberto Ferrari will post some on the same pretty soon (if not, this is an open request from my side for Marco & Alberto to post something on the topic, pretty please ) – [Update 9/28/2015 Read Transition Matrix using Calculated Tables]. As usual, a combination of posts from these folks are going to be the best resource for any topic in the Modeling side, and I learn most of my stuff from them. So you would be thinking – what exactly am I trying to achieve in this post? Well, I would like to add my 2 cents on the same and try to see if the community in general agrees with what I think and if not, to learn from the comments and the interaction this might generate.

I) How to build Calculated Tables in Power BI

Before I start on my thoughts on calculated tables, it might be a good idea to quickly see how we can create calculated tables.

1) First make sure that the version of Power BI is equal to or higher than 2.27.4163.351. (I am making a fair assumption that this feature will be enabled in all higher versions also released in the future). If not, download it from here

2) Now open any of your existing models in Power BI (or get some new data), and after that, click on the Data tab. You should be able to see the New Table icon in the Modeling tab on the top.

3) Click on the New Table icon, and then enter any DAX expression in the format that returns a table TableName = DAX Expression that returns a table Once you do that, then you should be able to see the resultant columns in the new table.

II) When is the data in a Calculated Table processed

The official blog says quite a few things on the same-

A Calculated Table is like a Calculated Column.

It is calculated from other tables and columns already in the model and takes up space in the model just like a calculated column.

Calculated Tables are re-calculated when the model is re-processed.

So based on this information, I am going to go a step further and assume that the data in a calculated table is processed during the ProcessRecalc phase of processing. Also, this means that every time any of the source tables changes (like a new calculated column or new data), the data in the calculated table will also change. To prove this, let us try a simple experiment-

1) Make a calculated table called Test which will be the same as the Date table (which currently has just the Year column).

Note that measures from the source table are not brought along to the calculated table, which is as expected.

2) Now go to the Date table (which is our source table in this case) and then add a new calculated column called TestColumn with 1 as the value.

Note that when we added a calculated column in the source table, the column was replicated in the calculated Table also with the same name. The only difference is that the source table shows an icon for calculated column. This shows that the ProcessRecalc that happens in the source table when a new calculated column is made, also recalculates the calculated table.

III) My thoughts on Calculated Tables

Based on my understanding so far, there are times when I think I should use calculated tables and times when I should not use calculated tables. So here it goes –

a) When NOT to use calculated tables

If you have a way of replicating the calculated table in some form of ETL or source query (even a SQL View), you should not use a Calculated table. Why? A couple of reasons

If done from ETL / source query, the engine will see the result as a regular table, which means parallel processing of the tables can be done (unlike now, where the ProcessData phase of the source tables have to finish first before the calculated tables can be processed). So calculated tables could lead to slower data processing time.
A ProcessRecalc happens every time you do a process full, and adding more calculated tables (as well as calculated columns) unnecessarily will increase the processing time. Also, during development of very large models, you might have to wait for a long time after each calculation is made for the data to appear, since all dependent tables will also have to be recalculated (unless you turn off the automatic calculation mode).

This one is more an assumption and further research will be needed to validate this, but I am putting it forward anyways. Just like a calculated column is not that optimized for data storage compared to a regular column, I suspect that a calculated table will also not be optimized for storage compared to a regular table. If this is true, this might affect query performance.

b) When to use calculated tables

There are a lot of scenarios where you would want to use calculated tables and I have listed a few of the scenarios below

During debugging complex DAX expressions, it might be easier for us to store the intermediate results in a calculated table and see whether the expressions ate behaving as expected.
In a lot of self-service BI scenarios or Prototype scenarios, it is more important to get the results out there faster and hence, it might be difficult or not worth the effort to fiddle with the source queries. Calculated tables can be a quick and easy method to get the desired table structures.
A kind of scenario that I see very often during prototyping / PoCs is when there are 2 facts in an excel file which are given for making reports. As seasoned users of Power Pivot / Power BI, we know that we will have to create a proper data model with the dimensions. Now, I might need to create a dimension which gets me the unique values from both the tables. For eg, in the below example, I need the Country dimension to get values from both the tables (USA, Canada, Mexico). It might be easier to write an expression for a calculated table like shown below

Country = DISTINCT(UNION(DISTINCT(Table1[Country]), DISTINCT(Table2[Country])))

There are times (like in some role playing dimensions) where you will need to replicate the columns / calculated columns (or even delete) in another table also, even if the columns are built at a later phase in the project. Calculated tables are a perfect match for that, as you will only need to make the changes in one table, and the changes will flow down to the calculated table during ProcessRecalc phase (remember our previous example where we created a TestColumn in the Date table, and the change was reflected in our calculated table also).
Calculated tables can help in speeding up DAX expressions. This is very similar to the technique of using aggregate tables in SQL database to speed up the calculations in SQL.
Quick and easy way to create calendar tables (like Chris showed in his blog)

This is still a very early stage as far as Calculated tables are concerned, and I am pretty sure we are going to see some very innovative uses as well as benefits of calculated tables in the days to come. I might also learn that some of my assumptions are wrong, and if I do, I will come back and update this post to the best I can. Meanwhile, feel free to comment on your thoughts / concerns / feedback.

Update – 9/28/2015

Transition Matrix using Calculated Tables – Alberto Ferrari
Use Calculated Table to Figure Out Monthly Subscriber Churn – Jeffrey Wang

Sep

2015

SSRS Line Chart with Data Tables (Excel Style)

It takes a lot of discipline and dedication to run a blog properly and that is one of the main reasons why I admire bloggers and tech gurus like Chris Webb (who has been putting out high quality blogs for ages in a consistent manner!). Sadly, I am not very disciplined when it comes to writing blogs and it takes a significant external force to make me write nowadays. I had written a blog almost 3 years ago on how to create SSRS charts with data tables like in Excel and from then onwards, I have had a lot of readers ask me on how to do the same with line charts (both through comments as well as emails). I knew it was possible but was too lazy to write a blog on it, until I had 2 readers ask me the same question yesterday in the comments. Finally, I decided to check it out and write about it. The solution is not perfect and is more of a workaround but should be ok for most of you I guess.

For illustrating the solution, I am using a simple dataset which shows the sales by Product and Month.

To follow this solution, you must be familiar with the technique I mentioned in the previous article. If you have not read that, please the previous article first and then follow the steps below 1) A lot of readers already found out that if the technique described in the previous article was used, then we will only get points and not actual lines. So, the very first step here is to modify the source query such that for every actual point in the line chart, we get 2 more points which gives the start and end for that point. With this, now we will have a line joining 3 points for what would just have been one point before.

;WITH   Src
AS     (SELECT Product,
               MonthNo,
               Month,
               Sales,
               CASE
WHEN MonthNo = 12 THEN NULL ELSE (lead(Sales, 1, NULL) OVER (PARTITION BY Product ORDER BY MonthNo) + Sales) / 2
END AS LeadSales,
               CASE
WHEN MonthNo = 1 THEN NULL ELSE (lag(Sales, 1, NULL) OVER (PARTITION BY Product ORDER BY MonthNo) + Sales) / 2
END AS LagSales
        FROM   (<Source Query>) AS O)
SELECT Product,
       MonthNo,
       Month,
       ‘1’ AS Type,
       CAST (LagSales AS FLOAT) AS Sales
FROM   Src
UNION ALL
SELECT Product,
       MonthNo,
       Month,
       ‘2’ AS Type,
       CAST (Sales AS FLOAT) AS Sales
FROM   Src
UNION ALL
SELECT Product,
       MonthNo,
       Month,
       ‘3’ AS Type,
       CAST (LeadSales AS FLOAT) AS Sales
FROM   Src;

Note that LeadSales column is actually the (Sales for next point + Sales for Current Point) / 2 and LagSales column is actually the (Sales for previous point + Sales for Current Point) / 2. This will help us get a smooth line when we join our different lines. Also, we have to ensure that for the first and last points, NULL values are assigned. The bottom part of the query brings all three columns (Sales, LeadSales, LagSales) into a single column called Sales but each one is assigned a different Type.

2) Repeat the steps 2 and 3 in previous article to make the matrix and the two rows above it.

Also add the Type column to the row group, delete the columns only and then filter the Type group for 2 only. The reason is that we only want the actual Sales to be shown in the data table, which is 2. Type 1 and 3 are used for the sole purpose of making the line chart.

3) Now you should be able to follow the rest of the steps in the previous article with the sole exception that you will be using a line chart and not a bar chart.

Make sure that you set the CustomInnerPlotPosition and CustomPosition appropriately like in step 10 in the previous article, so that graph appears continuous. I used the below settings for this line chart.

4) Instead of step 11 in the previous article, I chose to make a new column to the left for the vertical axis, and just made sure that the vertical axis for the line charts all have the same scale.

Note that the series expression for this column is just 0, and there are no category or series group. This ensures that we just get a dummy line for the axis. You can start hiding the orders to ensure that the graph looks continuous.

5) I also added an expression such that the markers and tooltips only show if the type is 2.

6) With all these changes and a bit of formatting, we can get the below result

This should be good for most people. However, there is one minor drawback which is that the lines do not join that smoothly. I have just zoomed in one part so that you can see the issue. Maybe, this could be solved by fiddling along with the properties some more, but I feel this is not that big of an issue.

Hopefully this will put to rest some of the questions I keep getting on data tables in SSRS, so that I can go back to my lazy self (just kidding)

Aug

2015

Power BI Tip: Making similar sized KPI Boxes / Charts

Recently, I got asked by one of my readers if there is an easier way to make similar sized KPI boxes or charts in Power BI, other than manually resizing each individual visualization. As you know, making similar sized KPI boxes and / or charts are a design technique to make your reports symmetric and more aesthetically pleasing. Currently Power BI does not offer us a way to key in the width / height of the visualizations and it might seem like manual resizing is the only option. This tip is a much more simpler and precise way to do the same.

I) Making Similar Sized Charts

Making similar sized charts are easy. For that, click on the completed chart that you want to copy and then press CTRL + C on the keyboard to copy the chart. After that, press CTRL + V to paste the chart.

Now, you can go ahead and change the dimensions and measures of the second (and also the chart time), but one at a time. You should have a same sized different chart now.

The reason why I said to do it one at a time is because you will lose the chart if you remove all the dimensions and measures.

II) Making Similar Sized KPI Boxes

Now this is a little bit more trickier. Let’s say I use the same CTRL + C, CTRL + V to copy paste the textbox.

Now when I try to change the measure, notice how I can’t just replace the existing measure with the new one. I tried putting it on the card as well as on the Fields section. And when I try to remove the measure from the Fields, the entire card disappears.

To get this to work, first change the chart type to something else, say a pie chart. Then drag and drop the new measure into the Values section. Make sure to remove the old measure and then revert back to the card visualization. Voila, now you have two KPI boxes of the same size.

Now that you know this trick, go forth and make some pretty dashboards in Power BI!

You can also make some cool indicators on your KPI box with this trick that I showed in a previous post.

Note : Created using Power BI Desktop version listed below

Aug

2015

Using DAX to make your Power BI DataViz more meaningful

One of the best features I like about Power BI Desktop is that the data acquisition, data modeling and data visualization functionalities are all integrated in a user friendly way. I don’t have to leave the Power BI desktop to perform any of these common operations when I am playing with my data, unlike so many other tools. Why is this important? Because you tend to be more productive when you have everything you need in one place and also, you tend to be more creative when you have the power to model the data along with making your visualizations. DAX has some really powerful data modeling capabilities, and when you couple that with Power BI, you can start giving more meaning to your visualizations and get faster insights. I recently made a video on my company’s blog site on how to analyze Promotion Effectiveness and for the same, I was using a dashboard made in Power BI Desktop. I am just highlighting two examples of how I used DAX to make my visualizations better.

I) Make better Sparklines by Highlighting

In my Promotion Effectiveness Analytics demo, the sparklines highlight (instead of just filtering) the value for the months where the selected promotions ran. This gives us a better understanding of what is happening before, during and after the promotion. Now, this is not possible out of the box in Power BI, but with just a little bit of DAX magic, we can make this work.

1) First let us see how to build a simple sparkline in Power BI. For that, select the month & sales and make it as a line chart.

2) Next, let us remove all the format options so that it looks like a sparkline. Also feel free to resize it

3) Make a new bar chart for Sales by Promotions, so that we can use it to filter the sparkline.

You can see that the sparkline automatically gets cross-filtered to only the months where the promotion ran. This is great but what would add more value is if we could see the months highlighted instead of just filtered. That would let us know how the sales are before, during and after the promotions instead of just during the promotions.

4) Make a new measure by clicking on the dropdown next to the table, and then use the formula below

Sales Amount Total = CALCULATE([Sales Amount], ALL(Promotion))

Basically, we are making a measure which will show the Sales irrespective of whether the Promotions are filtered.

5) Now add the new measure to our sparkline (make sure to give it a lighter color like light grey for a better effect).

Now you can see that the the grey line shows the sales for all the promotions while the green line highlights just for the selected promotion. You can also use this technique in other creative ways, for e.g., to highlight the max and min points of a sparkline.

II) Waterfall charts for Measures

In my Promotion Effectiveness Analytics demo, I created a Waterfall chart to show the breakdown of Customer Visits. Basically,

Customer Visits (This Year) = Customer Visits (Last Year) – Lost Customers + New Customers

I have individual measures for each of those, and in this case, a waterfall chart would be a great way to show the breakdown. However, we can only put columns in the category axis for waterfall chart in Power BI. But with some data modeling, we can get this done.

1) Make a dummy dimension called Customer Retention with just one column and 3 values – Last Year, New and Lost. I just made the dummy table in a text file and imported it to Power BI.

Note that this is a disconnected table and will have no relations to any other table.

2) Create a new measure called Customer Visits as shown below

Customer Visits = IF(HASONEVALUE(‘Customer Retention'[Customer Retention]),
                        SWITCH(VALUES(‘Customer Retention'[Customer Retention]),
                                “New”, [New Customers],
                                “Lost”, [Lost Customers],
                                “Last Year”, [Visits (LY)]))

Basically you are assigning the appropriate measures for New, Lost and Last Year based on the values for the disconnected Customer Retention table.

3) Now just make a Waterfall chart with the Customer Retention column and Customer Visits measure to clearly see the breakdown.

Hope you got some ideas on what all we can do when we combine DAX with dataviz. Stay tuned for more as we expect to see Microsoft release more functionalities around the tool.

Note : Created using Power BI Desktop version listed below

Feel free to watch my video on analyzing promotion effectiveness by clicking on the image below

Jul

2015

How to add an Indicator to Power BI Desktop

Yesterday was an exciting day – Microsoft released the GA version of Power BI and with it comes an impressive list of new features and functionalities. One of the new features that I was really interested in was – Rich control over visual coloring, including conditional formatting in Reports. This opens up a whole new possibility of tweaking Power BI even when the feature is not available out of the box. Today’s blog is going to be an example of that! (NOTE: This workaround is needed to add an indicator on the version that was released on 7/24/2015. The Power BI team moves really FAST with updates and there is every possibility that some features which make this workaround redundant, might be added sooner than later.)

Adding a KPI to a card is easy and helpful. But what makes it more useful is if there is an indicator which gives an idea on how the KPI is performing. Right now, there is no way to add an indicator out of the box in Power BI. But thanks to the new features, we can implement a workaround which is not that perfect, but will work for now. The finished dataviz looks like below

Note that there are 3 features here that look like it is not possible out of the box:-

1) There is an up / down arrow which changes based on the Year over Year (YoY) change.

2) There is a bar indicator on the top of the arrow which changes color (red or green) based on YoY

3) The background for the entire KPI box has a custom color (Neither the card data visualization nor the text box has formatting capabilities). So how is this done? Follow the steps below:-

Up / Down Arrow

Currently, there is no way to add a dynamic image in Power BI. You can’t import a binary type image data into Power BI neither add the image URL. A static image will not do for our purpose as we want the arrow to change based on data.

1) Let’s say that I have a measure called YoY which is basically the difference of Current Year sightings and Previous Year UFO sightings. Now make a new measure called YoY change which is YoY Change = [YoY] & ” ” & IF(SUM([Sightings]) >= [Prevyear], “⇧”, “⇩”) Yes, the entire trick is based on using the Unicode characters for arrows as the indicator. Unicode characters will work without any issue and can be used in regular DAX measure expressions. The above measure will show an up arrow if the current year is greater than or equal to previous year, else down arrow. You can also explore the other Unicode characters if you want to look for alternative symbols.

2) Now we can just use this new measure in a card. The measure expression will ensure that the Up arrow or Down arrow gets displayed accordingly.

Bar Indicator with Color on top of Arrow

Currently, there is no way to format a text box or a card viz. Else that would have been a good way to achieve this functionality.

1) For this demo, I have made a table with 1 column called Meaning with 2 rows – Up and Down. And then I made a simple measure that displays 1 for Up if the current year is greater than or equal to previous year or 1 for Down if the reverse is true.

PosNeg = SUMX(VALUES(temp[Meaning]), IF([Meaning]=”Up” && SUM([Sightings]) >= [Prevyear] || [Meaning]=”Down” && SUM([Sightings]) < [Prevyear],1))

2) Make a bar chart out of the Meaning column and the new PosNeg measure.

3) Click on the Format tab (on the right hand side panel) and then ensure that all properties except Y axis has been turned off. Then expand the data colors and choose the default color a Green. You can also click on the Show All property and manually make sure that the Up value is Green. (If the default value for your data is Down, then make sure that the color is Red).

4) Now choose a filter condition which will show the bar chart for Down value. After that, go to the Format option and choose the color for Down as Red.

Power BI remembers that Up has a color of Green and Down has a color of Red. You can test it by toggling the filters / slicers.

5) Make sure the transparency of the background is set to 100% (don’t turn it off as shown in the image below) and also turn off the Y axis now. Resize the bar chart and place it above the previous card viz for Up / Down arrow indicator.

Now you have got a nice color indicator also!

Background for KPI Box

Right now, there is no way to add a background color to the KPI box, as we cannot format the card data viz or the textbox viz. But what we can o as a workaround is to select any column, make it into a bar chart and then turn off all the options so that we get a blank rectangle. Then go to the background option and choose the color of your choice.

Feel free to drop in the previous two indicators that we made into the colored rectangle and now we have got a much prettier and useful KPI box. There is a lot of different ways in which you can mix and match this technique. For example, how about using a bubble indicator instead of the bar?

Note that these dataviz can not be pinned to a Dashboard, as we can not overlay visualizations in a dashboard, However, these will work just fine if you upload it to a normal Power BI Report. Having the control over formatting has greatly increased the ways in which you can tweak the reports and I am loving it. Looking forward to what all surprises the Power BI team has for us now!

May

2014

Ranking within Slicer Selection in Power Pivot

I know it’s been ages since I posted something, and I do have lots of lame excuses so maybe it deserves a post on it’s own. But for now, I want to jot down a workaround on how to rank a column based on the slicer selections for the same column.

The other day, I was working on an excel dashboard and I got a requirement to rank a field while the field values are there on the slicer. While I am used to getting requests on ranking a field based on a measure, this was the first time someone was asking me to rank a field based on a measure while the column was on the slicer. The user just wanted to rank within the selected values of the field in the slicer and not as a whole. To explain this, I just made a very simple example with the model given below

The FactLicense table contains data on new liquor licenses and I made a simple measure License Count to count the number of licenses. The FactLicense table is related to the Geo table through the Zip Code column. The requirements were that

the City field should be ranked by License Count
there should be a slicer displaying the values for City
the ranks should update based on the Slicer selections

Solution

1) To solve the first part of the requirement, I made a simple ranking measure by City as shown below.

LC Ranked by City:=RANKX(ALL(Geo[City]), [License Count])

You can see the results in the pivot table below.

2) For the second requirement, we can just add a slicer for the City field and then connect it to the Pivot Table.

3) Now the third requirement is the interesting one. When I try to filter by some cities, I get the result below.

You can see that the Ranks don’t get updated based on the slicer selections. The user wants the ranks to start from 1 but you can see that the ranks are still based on the entire city list and not just the selected city list. Now it is impossible (atleast as far as I know) to reset the ranking if the same field is on the rows as well as the slicer. The reason is that the Rank measure operates on ALL(Geo[City]) and hence removes any filter or selection that is already made. So what do we do?

4) A simple workaround is to make a calculated column (say City Slicer) and make the Slicer based on this new calculated column.

Make sure to remove the old slicer as well as connect this new slicer to the pivot table.

5) Now when we select the City in the slicer, you can see that the Ranks get reset.

You can further rename the Slicer such that it says only City and you can hide this calculated column so that it is not visible to the other users analyzing the model. This will help avoiding confusion among the end users (Eg – why do we have 2 city fields?) and at the same time, you can make your Excel dashboard with the fulfilled requirements.

Update

Within seconds of posting, Miguel Escobar (twitter) comes up with a much better way of doing this using the ALLSELECTED().

LC Ranked by City1:=RANKX(ALLSELECTED(Geo[City]), [License Count])

This would be the ideal way to do it as we don’t have to create a new calculated column and still achieve the same results. Thanks a lot for this Miguel!

Uncategorized

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Power BI Tip: Making similar sized KPI Boxes / Charts

Using DAX to make your Power BI DataViz more meaningful

How to add an Indicator to Power BI Desktop

Ranking within Slicer Selection in Power Pivot

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