Deriving Columns from What You Know in Power BI

Before I get started with the topic of the day, I want to remind you that Power BI is still being updated by Microsoft. In fact, there was an update just last week on October 20th that appears to have added some missing features that I mentioned before. So if you haven’t updated Power BI recently, be sure to do that before reading the rest of this blog.

A few weeks ago when I was looking at defining relations between tables in the Relationship dialog I was disappointed in the fact that when I display my tables in what I would have previously called a Dialog view in PowerPivot, I could not use drag and drop to define my relations. Well, my disappointment is over. The latest update, among other things, adds this capability.

Since last Wednesday, October 21, was ‘Back to the Future’ day, I want to go back to last week’s blog just after I added the DimProductCategory table. This time however, rather than using the Management Relationships dialog, I am just going to the Relationships view and drag the field ProductCategoryLabel to ProductCategoryKey as shown in the following image.

Now I must say that I am use to just identifying the fields from my two tables that I want to connect to form the relationship and expect PowerPivot to figure out which is the one and which is the many table. Unfortunately, Power Bi still is not quite this smart. Because when I attempt to define this relationship from the one site to the many side, the relationship definition fails as shown in the following figure.

Maybe the next update will automatically reverse the direction of the relationship for me. However, for now I can click the OK button in the message box shown above and then redefine the relationship correctly from the DimProductSubcategory to the DimProductCategory table.

Now the relation is created and I am ready to continue. By the way, when I click on the relationship, Power BI highlights the two tables as well as the relationship line. It also encloses the connecting fields in a box to make it easy to identify the relationship fields.

If instead of left clicking on the relationship, suppose I right click on the relationship. Now I get an expected option menu which in this case consists of only a single option, Delete. Clicking this option will of course delete the relationship.


On the other hand, double clicking on the relationship opens the Edit Relationship dialog shown below. I can use this dialog to view the relationship or a sampling of the data, or make changes to the relationship.

That’s great. But like an infomercial, there’s more! In the Relationship view (Diagram view) I can also now right click on a field and delete a field, hide a field from the Report view or rename the field.

As with PowerPivot, if I delete a field, it is gone for good. If I realize later that I need the field, I would need to delete the table and reload it to get the missing field. Of course there are other consequences to doing this like needing to redefine the relationships and possibly rebuilding reports (visualizations) that used that table. Because at this time, I cannot visually define the fields to include or exclude during my original data load from my data sources, it is important that I know my data and delete any fields that I know that I will not need before I begin creating new columns, measures, or reports. In PowerPivot, we called these columns Useless columns.

I can also hide some columns from the Report view. For example, I can often hide columns used to define relationships between tables because end users typically do not include these columns in reports. Hidden columns are referred to as Technical columns because they are required in the data model and cannot be deleted without destroying relationships or perhaps calculations of other fields. I never want to show more columns to a user than they know what to do with.

Finally, renaming columns can be very beneficial. Often column names in databases have cryptic or abbreviated names. End users may not be comfortable with these shorten names. Use the Rename feature to make names user-friendly and descriptive.

Not only can I delete, hide, or rename columns in a table, but by right clicking on the table header, I can perform these same actions on an entire table. For example, I may not like dimension tables that begin with the letters ‘Dim’ like many DBAs prefer but end users may have no idea why the table is Dim. Similar to columns, I don’t delete tables from my model unless I am positive that I do not need them. Hiding tables only makes sense if I want to use only one or two fields from a table. In this case, I may need a column from another table for a calculation, but I would never display those columns directly in reports.

Returning to the data view of my tables, I can also right click on any of the column headers and delete, hide or rename the column. There are also several other options ranging from sorting, to creating new columns or new measures.

I can also right click on the column names in the fields list along the right side of the Data view. The dropdown list of options shown below is similar to options in the context menu above. So I have several different ways to manage columns

Let’s try something new, a new column in fact. In my FactSales table, I can find several columns like SalesAmount, TotalCost, and several others, but there is not Total Profit column. I can easily calculate that value from other values in the table. To begin, I need to create a new column in my data model. That means clicking the New Column button in the Modeling ribbon.

New columns are created at the right end of the table. By default, the column name is cleverly called Column. Of course I can change that by entering a new name. Then after an equal sign which indicates that an expression will be used to define the column, I can begin entering the column definition using DAX expressions. Yes, DAX is still alive and well. If you need a review of using DAX, I’ve covered multiple DAX topics over the last couple of years of this blog.

As in PowerPivot, I can select a column from the current table by just typing the left square bracket. This action opens a dropdown of all column names in the current table listed alphabetically. I can scroll down through the list and select a column by double clicking on its name. I can also type a few characters of the column name to narrow down my list as shown below.

My full expression to calculate TotalProfit is shown below

When I click the Enter key, the entered expression is used to calculate the values for all the table rows.

Before moving off the column, I might want to define custom formatting for the values. The formatting definition here is carried forward to all reports generated with the data. In the above example, I might want to only display the dollar amounts to two decimal places. (Actually, because of the size of aggregated data, I might later decide to format the values with no decimal places.)

Suppose that I want to display some of this data using a standard table with Channel names for the rows and a few select columns from the table. Notice that the values displayed here obey the formatting definition set on the Data page.

That’s it for this week. Next time I cover creating New Measures and why you might want to do so.

C’ya.

Creating Relationships Manually in Power BI

Last time we loaded a BI model with data from several different data sources, but the data sources were carefully selected so that the columns that would relate one table to another table would already have the same name and data type before attempting to upload the data to Power BI Desktop. Because of this assumption, I was able to let Power BI auto detect the relations between the tables. Unfortunately, in the real world, that does not always happen. So this time I will show you how to create relationships manually and how you may need to massage the data a little first before defining those relationships.

I am going to use the Contoso data again and will begin with the following tables already loaded into the model from my SQL Server instance: FactSales, DimChannel, DimDate, DimProduct, and DimProductSubCategory. To complete my model, I need the Product Category data which I am going to load from an Access table in this case.

Since we covered how to load data from SQL Server last time, I am going to pick this discussion up after I have loaded the SQL data and am about to load the Product Category data. The image below shows that I have already click the Get Data button and have selected Access database from the list of possible data sources and have defined where my Access database could be found. As you can see in this figure, Power BI only finds a single table in my Access database named DimProductCategory. When I select this table, I see a preview of the data.

In the table preview (on the right), I see that there are only two columns named ProductCategoryLabel and ProductCategoryName. You might be able to guess from the way the data in the column is formatted that the ProductCategoryLabel data has been defined as text, not numbers because it is left justified.

After clicking the Load button to import this data into my data model, I switch to the relationships view and see the table diagram shown below.

I see that there is no relationship defined with the DimProductCategory table because there are no connecting lines leading into or from this table. Therefore, I first want to select the Manage Relationships button. I can see the four relationships that tie together the other five tables, but I am missing a relationship. I may first try to click the auto detect button which I talked about last time to see if Power BI can find the missing relationship

Unfortunately, Power BI very quickly responds back that it cannot. The problem is that while the DimProductSubcategory table has a field named ProductSubCategoryKey, the new table, DimProductCategory, does not have a field with that name.

So I might try to create the relationship manually by clicking the New button on the Manage Relationships screen (after closing the Autodetect message screen of course).

On this screen I have to specify the names of the tables and the linking columns to define the relationship. I can begin with either table on the top. After I select the table, I see a grid of the available fields along with a couple of rows of data. To select the field that I want to use in the relationship, I merely need to click the column header to select the field.

I then specify the name of the linking table along with selecting the column that I want to link to.

There are some advanced options which I am going to skip over for now and simply click the OK button. Power BI then attempts to create the relationship. But wait a minute, you might say, the column names are different and the data types of these two fields are different. That is true. I am not surprised that I can link two tables on fields with a different name, but different data types? In fact, I expected Power BI to reject this relationship because of the different data types, but it did not. It created the relationship as shown in the following image

To find out if this relationship is really valid, I next go to the report desktop and select the ProductCatgoryName field from the DimProductCategory table and the ProductSubcategoryName field from the DimProductSubcategory table. You can see from the figure below that the data makes sense. The general category Audio should include subcategories like Headphones, Radios, and Speakers, but not Camcorders, Cameras, or Cell Phones. Somehow, Power BI was able to transform automatically one of the data types to the other (I am thinking it converted the string field to an integer) and then formed the relationship.

Yes, I could prove that this relationship is correct another way by creating a simple table that lists ProductCategoryLabel from the DimProductCategory table and ProductCategoryKey from the DimProductSubcategory table. You can clearly see that the relationship links these two tables correctly.

Surprised by this, I wanted to try something else that may not be as easy for Power BI to automatically convert. I took another table, the Stores table, and modified the Excel version of the table to change StoresKey to prefix the store number with the letter ‘S’. I then loaded that table into my model as shown below.

Next I went to the Manage Relationships dialog as before and attempted to add a relationship between the FactSales table and the Stores table as shown in the following figure.

Again Power BI did not complain about creating the relationship. However, when I went to the reports page this time and attempted to display a report of sales amount (from FactSales) against the StoreID from the Stores table, I got the result shown below which indicates a total sales for each year, but no StoreID value at all. (I used the Matrix visualization here with StoreID for the rows and YearLabel for the columns and SalesAmount for the value.) So clearly this relationship did not work.

Therefore, I need to edit one table or the other to ‘fix’ this relationship. I choose to edit the Stores table. Remember from last time, to edit a table, I click the Edit Queries button to open a new window where I can edit the tables.

In this case, the solution is to remove the ‘S’ from the front of each of the StoreID values. I can do this with the Split Column transformation and specifically to split the column based on the number of characters from the left rather than splitting the column based on any specific character or character string like I did in an earlier blog example.

This action results in two columns, the first, StoreID.1, contains only the first character of the StoreID field, the ‘S’. The second field, StoreID.2, contains the numeric portion of the store ID. As you can see in the formatting of this column in the following figure, Power BI also treats StoreID.2 as a numeric value.

I then removed the StoreID.1 column as something I will not need. I also renamed StoreID.2 to just StoreID.

I then clicked Close and Apply.

Now I can create a new relationship between FactSales and Stores using StoreKey and StoreID respectively as shown below. Note that I did not change the field names to match. If I had, I could probably use the auto-detect feature to find and create the relationship for me.

This time when I attempt to create the same report on sales by store and year, I get reasonable results as shown in the table below.

Well, I hope you found that interesting. Next time I plan to probe a little deeper on creating calculated columns within a query.

Till next time, c’ya!

Power BI can Load Data from Multiple Data Sources

This week I’ll show you how to use Power BI to pull together data from multiple data sources that you can then use to create visualizations.

The first step is to open your copy of Power BI Desktop. Then click on the Get Data button. I’m going to begin with some data from SQL Server so I do not have to click the More… button to see all the available data sources, SQL Server is right on the main dropdown.

To work with a SQL Server database, I must first enter the name of the SQL Server that I want to connect to. Note that while the dialog shown below prompts for the name of the database, this information is optional. You might think, ‘How can the database name be optional?’ In a moment, I will show you that Power Bi first needs to connect to the server, but then it can retrieve the database names that you have access to and display them for you to select. This protects you from the possible misspelling of the database name in this dialog. Since I am running everything on a single machine, my SQL Server name is LocalHost.

Of course before you can connect to the server, you must provide credentials. I am running everything on a Surface Pro 3 so I will use my Windows credentials. However, if you need to connect to another server on your network, you may need separate credentials.

The following dialog may or may not appear depending on the database you are connecting to and your version of SQL Server. However, in most cases, clicking the OK button allows you to connect to your data as long as you have rights to the SQL Server box.

When Power BI connects to the server, it first begins with a list of the possible databases along the left side of the screen. By opening a database (clicking on the arrow to the left of the database symbol, I can see the tables in the database. I can select multiple tables from this database. I can even select multiple tables from multiple databases. With each table I select, I will see a sample of the data in the right panel. Note that unlike PowerPivot, there is no way to limit the columns or rows from the table at this point. Maybe in the next major release.

You’ll notice that this week I return to my favorite sample dataset, Contoso.

If I select multiple tables to load, they all appear in the following dialog that shows me the progress of loading my data. In the image below, I can see that I’ve selected five tables from the database. As the data load progresses, Power BI shows me the number of records loaded in the line beneath the table name.

After the data loads, I notice that the table names appears in the Fields column on the far right of the desktop. But you say that these are table names? If I click the arrow to the left of each table name, the table expands to show the names of the fields essentially grouped by table.

Next I can click on one of the icons on the left side of the desktop. The first icon is the report surface. I typically work there after I have loaded all my data and made any changes to the columns and/or rows. So some might think that this icon should be on the bottom. The second icon is the data icon. I can use this icon to edit the column definitions, add new columns, or remove columns that I no long need. I showed some of these techniques in prior weeks of this blog.

However, I usually start with the bottom icon, Relationships. Relationships are the heart of any multi-table analysis. If I do not define the relationships between the tables first, I really cannot do any analysis. I might not be able to create some column either. So let’s start there.

The diagram that Power BI creates shows the tables that have been loaded into the model and if relationships were defined in the source data, it tries to create those relationships for me automatically. Notice that the diagram clearly identifies the one side of the relations with a ‘1’ and the many side of the relationship with an asterisk.

Unlike the diagram view in Power BI, there is no ability to edit, delete, or create new relationships from this dialog. Nor can I delete columns, define hierarchies, or rename columns here. I can hide individual columns or entire tables from the report view of the model and I can maximize the view of the individual table diagram, but that’s it. Again, maybe in the next major release of Power BI these additional features will appear so that the functionality matches that of PowerPivot diagram views.

Next I’m going to load some additional data into my model beginning with some data from an Access database and then data from Excel. I’m not going to take the space here to show screen images of these processes because between the above description and prior week blog discussions, I believe that you can figure out how to load data from different data sources. The only problem you may encounter is the need for new or updated connectivity tools to access your other data sources.

Once I have loaded all my data, my first action is to go to the Relationships view. You can see in the following figure that Power BI automatically shows my additional tables that I’ve loaded, but they show no connection to the rest of the tables in the data model. I need to fix this before going forward.

Find the Manage Relationships button in the Relationships group in the ribbon and click on it.

A dialog appears that displays the current relationships between the tables. This view is similar to the manage relationships dialog in Power Pivot. I can add, edit and even delete relationships here. However, the interesting button is the one labelled Autodetect

When I click Autodetect, Power BI attempts to find and create relationships between the tables that are not currently connected to the data model. It does this by analyzing the field names in the new tables with names in the tables of the data model to find other fields with the same name and the same data type definition. Of course, one table must serve as the parent (one-side) and the other table must serve as the child (many-side). In most cases, you will start with a fact table and create branching child tables so they usually serve as the one side in each of the new relationships. If Power IB is successful, it will attempt to create that relationship and will first tell you in a dialog box such as the one shown below how many relationships it detected.

After closing the information dialog reporting the number of relationships found, the relationships are created and the results for this model appear in the updated Manage Relationships dialog shown here.

Note at this point, I could edit any of the relationships that were not correctly created, I could also add relationships between tables that perhaps had different field names that could not be automatically detected. It is a good practice to check the Relationships diagram to insure that every table is connected to the model through at least one relationship.

I am now ready to go to the Reports desktop to create some simple charts.

Creating a report is as simple. First click in a blank area of the Reports desktop. Then click in the checkbox to the left of the value field you want to count, sum, or average such as Sales Amount. Then click on the field or fields by which you want to report Sales Amount (or any other value field). For example, I might want to see sales by year or by Product category or by Channel. You can create as many visualization as you want on the desktop although you should keep in mind that they need to be large enough to be readable. Let’s start with the three shown below.

Not a bad start. Here is a fun feature that Power BI supports that Power Chart does not. Just click on the Year 2009 column in the chart in the upper left. Immediately, all charts on the page change to shows the sales related to the year 2009 in a darker color and sales from other years in a light color.

Note that I did not have to do anything special to get this to work. I did not have to create a filter, a slicer, or anything. Similarly, I could click on columns in other charts to slice and dice the data different ways.

Filtering the data on the page is actually done entirely differently. To filter the data that appears on the page, you have to define a page filter by dragging the field or fields that you want to filter by into the area beneath Page Level Filters found in the column to the immediate left of the column with all the table fields. Filter fields typically are string values and typically, there are not a large number of unique value. To filter the page, just click in the checkbox to the left of each value that you want to include on the page. You can filter on one or many values at a single time.

After selecting a page level filter, the visualizations on the page change automatically to represent only the filtered data rather the entire dataset. The following desktop image represents only the data from the year 2009.

Well, that’s all for this week, but if you have been following along, save your data from this week because I’ll pick up this sample data from this point next time.

C’ya.

Formatting Power BI Visualizations

Being able to mash together data from multiple sources to create meaning full analysis in Power BI or even PowerPivot can be lots of fun, at least to my data geek friends, but presenting visualizations of that data to management without the ability to customize the look would fall flat on its face at your next executive presentation. Therefore, I am taking a side trip this week to show you a little about formatting your visualization, enough to get you started exploring how to make individual visualizations look good.

I also want to introduce you to a set of simple data table that you can use for free while learning how to use Power BI. A team from the Paris Technical college put together some data and perform some basic clean-up of the data. The team includes: Petra Isenberg, Pierre Dragicevic and Yvonne Jansen and the data can be found at: http://www.perso.telecom-paristech.fr/~eagan/class/as2013/inf229/labs/datasets.

For today’s blog, I am going to use their CARS dataset. While it has over 400 cars with 8 characteristics, it does not have the new Tesla X model L, but it is a good dataset to show some basic visualization formatting technics.

Thus I start by importing the CARS.CSV file by using the Get Data option.

After loading the dataset (shown below), you can see a preview of the data in which the first two data rows are not cars. In fact, the first row looks like it could serve as column headers or in terms of our data set, column names. While the second row looks like it defines the preferred data type for that column. To begin loading the data, click the Load button in the bottom right of the screen.

After loading the data, the first thing I need to do is to make some quick changes to the dataset by clicking the Edit Queries button.

In the Transform group of the Home ribbon of the Query Editor, I can click on the option: Use First Row As Headers. This is similar to the PowerPivot load technique in which you could define whether your data included headers in the first row.

Next I want to get rid of the row with the data type (which is now the first row). But before going further, I take note of the data type of each column. I will need that information in just a moment. In the Reduce Rows option group, there is an option to Remove Rows.

Click the bottom half of the Remove Rows button. This action opens a dropdown letting you specify how you want to remove rows. I am going with Remove Top Rows because the data type row is now the first row in the dataset. This option now opens a dialog that lets me specify how many rows I want to remove from the top of the dataset (Do you see why I had to deal with the header row first?). In this case, I only need to remove 1 row, but you may encounter other datasets that require you to remove more than a single row.

Next I noticed the car column appears to be a concatenation of the car make and model.

Looking through the data, the car model appears to always be a single word. Therefore, there is an easy way to split this column by click on the Split Column button and then select By Delimiter.

The obvious delimiter here is the left-most space character.

After splitting this column and renaming the columns as Make and Model, my data table looks like the following image.

Next, I noticed that all the numeric data was loaded as text. This is a direct result of reading data from the CSV file. Remember the second data row which told us the preferred data type? We can use that information to change the data type (in the Formatting group of the Modeling ribbon) of each column as shown in the following image.

I also want to change the year column to a four digit year. Because this data does not include cars from the 21st century, I can get away with simply concatenating all the values in the YEAR column with a prefix of ’19’.

After completing all my data preparation, I can click on Close and Apply so I can begin creating reports (visualizations) with this data.

Let’s begin with a simple table of average MPG for each year. On a new report page, I can drag the columns MPG and YEAR from my list of columns for the table. Note that by default, numeric values are summed. To change MPG to calculate the average MPG, open the dropdown for MPG in the Values section of the Visualizations column on the right and click on Average. If YEAR is being treated as a number, you can select ‘Do Not Summarize‘ from its dropdown. My table now looks like the following image.

I would rather see the year first, then the MPG so I need to change the order of the columns. You can do this by clicking on and dragging the field name in the Values section of the Visualizations (where I changed the aggregation types) and dragging the field up or down as appropriate to place the columns in the order I want.

While this table is interesting and shows a general increase in the average MPG of cars in the dataset, it is not as clear to see how the average rises and falls from one year to the next. Therefore, I might want to change to a column report.

While this is a nice basic chart, it needs labels to explain to someone viewing it for the first time what they are seeing. Let’s begin by clicking on the Format icon just below the Visualization selection grid.

Now instead of seeing the Values and Filters groups under the Visualizations, we see various chart components. Each of these components have a dropdown arrow on their left which will open that section to expose additional properties for that chart element. They also include a simple sliding switch to turn that element on or off within the chart. Note that currently the X-Axis and Y-Axis are turned on, but Data Labels, Title, and Background are not.

If we open the properties for the X-Axis, we see formatting options for this chart element. Although not needed here, I can change the axis type, the axis scale (linear or logarithmic), a start and end value for the axis, and a Title, Style, and Color. For the X-Axis, I only want to turn the Title property on by moving its slider to the On position.

Next I open the Y-Axis properties. I change my axis scale to begin at 10 and default to automatic for the end value. Often when data values are very similar, plotting their values with a y-axis that starts at zero de-emphasizes the variations from one value to the next. Setting the starting point of the y-axis to a value closer to the smallest y-axis value will emphasize those differences. Think of a stock that trades in the lower $200s range with a daily fluctuation of a single dollar or less. Those fluctuations are masked if the chart has a y-axis data range of 0 to $250. On the other hand, changing the y-axis to begin span only the range 200 to 250 emphasizes the daily fluctuations.

As you can see below, my chart is starting to look a little better.


Next I open the data colors group. I can change the default color which is an aqua shade to any of the other colors by selecting either from the Theme Colors provided in the Default Color dropdown by going to the Custom Color option and defining my own unique color for the chart.


However, this changes the color of all the columns to that one custom color. What if I want to display different columns using different colors? If I slide the Show All slider to On, the property dialog now shows a separate color selection for each column in my chart as shown below.


Did you notice that the years are not displayed in chronological order? Did you wonder why? The years are actually displayed in order by the size of the Average MPG so that you could, if you want, define a progressive shift in color shade as the value of Average MPG increases. That being said, I decided to customize the colors by year as shown in the following image.


Finally I went back to turn on the Y-Axis title to arrive at the visualization shown below.


To add detail to the chart, I also turned on the Data Labels group which causes the data value to display at the top of each bar.


Now my chart shows both the actual data values as well as the relative change from year to year.


Finally, I turn the title element on. Here, unlike the x-axis or y-axis, I can specify the text to display as the title. For the x-axis and y-axis labels, the text is taken directly from the column names. I can also specify the font color, a background color and the alignment of the title across the top of the chart.


My chart is starting to look pretty good but it is still missing something.


I open the last chart element group, Background, and turn on the background giving it a custom color.


Now I have a final version of the chart that I might want to publish and add to a dashboard (which I will talk about in a future blog.)


Okay, while I did not show everything that formatting can do, I did want to give you a start. Please note that if you use other visualizations, they have their own formatting options as well which work the same or similar to these. Have fun playing with visualizations until next time.

C’ya.