Tag: Power Query

Working with Excel files in the Power BI Service

Working with Excel files in the Power BI Service

Published July 20, 2018

Power BI has been able to work with Excel files since it was first introduced. Indeed, it was born from the analytic capabilities in Excel. Users can connect directly to Excel files by using the Power BI service and nothing but a browser. However, depending on the content of the Excel file, and the method of connecting, the resulting products can be very different. In this post I will attempt to clarify this behavior. A subsequent post will detail the options available when working with Excel files in Power BI Desktop.

File Structure

Excel is a multi-purpose tool. It contains all the building blocks of Power BI, and as such, it is an excellent Business Intelligence client. Excel files are also often used (much to my chagrin) as a data storage container, or as a data transport medium. Understanding how the file is structured, and what you want to do with it is key to making the right choice when combining it with Power BI.

Originally Excel files (workbooks) were collections of worksheets. Analysts could import data into those worksheets and then analyze them with the tools that Excel provided. Although Excel was never intended to be a database, it’s ease of use and familiarity led many people to begin using it that was, and “spreadmarts” (spreadsheet data marts) quickly became a problem. The problems arose because the instant data was extracted from a source it became stale, and the fact that it was being stored in worksheets meant that it could be edited (changing history) and became subject to the data size limitations of a worksheet.

To take advantage of Excel’s analytic capabilities without being subject to the issues involved in worksheet data storage, the data model was introduced, initially through PowerPivot. The data model is a “miniaturized” version of the SQL Server Analysis Services tabular engine that runs in Excel. This data model is read only, refreshable, and highly compressed which importantly means that its only data limitation is the amount of available memory available on the machine running it. Importantly, this engine is the same engine that is used by Power BI – the advantages of which we’ll explore shortly.

Excel of course still needs to be able to use worksheets and be Excel, so we can’t just remove the worksheet capability (which incidentally is effectively what Power BI Desktop is – Excel without worksheets). Therefore, today from a data perspective, Excel files can have data in the data model, worksheets or both. From the Power BI service perspective, the important thing is whether the file contains a data model, as it treats the two cases differently.

Getting Excel Data

From the Power BI service, you click the Get Data button, and then the Get button in the Files tile. You are then presented with one of two dialogs depending on whether you are using a personal workspace, or an app workspace.

Personal workspace

Importing files into a Power BI Personal Workspace

Connecting file-based data to a personal workspace

When importing into a personal workspace, there are 4 possible data sources.

A local file is one that is stored on a file system local to the machine being used. Selecting this option will allow you to work with the Excel file stored in that location, but if the file is being used as a data source (data is in the worksheets), then a Data Gateway will be required for any data refreshes. Power BI will also connect to a file stored in OneDrive, either Personal or Business (through office 365). Finally, the service can work with files stored in any accessible SharePoint site (not simply Team sites as the name would indicate).

App workspace

Importing files into a Power BI App Workspace

Connecting file-based data to an App workspace

When importing into an App workspace, there are 3 possible data sources. The Local File and SharePoint – Team Sites options are precisely the same as when importing into a personal workspace. The difference is the OneDrive – Workspace name option replaces the two other OneDrive options. Choosing this option allows you to work with files stored in the “Group OneDrive”. Since every App workspace is backed by an Office 365 or “Modern” group, it also has access to the SharePoint site for that group. The “Group OneDrive” is the Documents library within that SharePoint site. Therefore, choosing SharePoint – TeamSites and navigating to the Documents library will render the same results in a few more mouse clicks, but also give access to all other document libraries within that site.

Connect vs Import

Once you navigate to the Excel file that you want to work with, you select it, and click connect. You will then be presented with two options for the file, Import or Connect.

This choice dictates how the file is brought into the Power BI service. The structure of the file determines exactly what is brought in to the service in both cases.

Connect

Clicking the Connect button allows Power BI to connect to and work with the Excel file in place. The workbook is displayed as an Excel workbook in full fidelity in the Power BI interface using Excel Online. The file itself is shown in the Workbooks section in the Power BI interface, and it stands alone from other Power BI elements (except that regions of it can be pinned to a dashboard). Connecting to an Excel report will not create a Power BI Dataset, Report, or Dashboard. All operations, including refresh (see below) are controlled through the workbook.

At no point is the file moved, or “brought in” to the Power BI service. If the file is being stored in SharePoint, or OneDrive, anything done to the file in the Power BI service will be visible to anyone with access to the file itself, whether they are a Power BI user or not. This includes refresh, which will be discussed further below, but the important part to remember here is that if the data in the connected file is refreshed through the Power BI service, and it is being stored in SharePoint (or OneDrive), all users will be able to see updated data the next time that they open the file.

Connecting to an Excel file behaves the same way whether the file contains a data model or not, but the file must contain a data model in order to be refreshed by the Power BI service.

Connected Excel file within Power BI

Connected Excel file within Power BI

Import

Importing an Excel file behaves totally differently from connecting to it. When an Excel file is imported, it is treated as a data source to Power BI, and the assets within that file are brought into the Power BI service. Subsequent changes to the source file are not immediately reflected within the Power BI service, but are retrieved through the refresh process.

The way that the assets are brought into the service depends very much on the structure of the file, specifically whether it contains a data model or not. If the file does not contain a data model, then Power BI will use the data contained in the Excel worksheets to construct a new one. This is similar to what happens when a CSV file is imported into the service. If the file does contain a data model, then the worksheet data is imported, and that data model is brought into the service as-is. One important exception to this is if worksheet data uses the same query as an existing model, the worksheet data is ignored, and the data model is brought in as-is. This is important because Excel’s Power Pivot editor can be used to edit the model, creating calculated columns, calculated measures and relationships prior to import. The model that is automatically created when the file does not contain a model has no editing capabilities.

When an Excel file with a data model is imported, the data model (imported or created) is added to datasets, and a link to the dataset is added to the default dashboard for the workspace. If no default dashboard exists, one will be created. A report can then be authored in the service. If the workbook contains any PowerView reports, these will be converted to native Power BI reports and added to the service as well. Any embedded 3D maps are not brought in.

Imported Excel File showing calculated measures

Imported Excel File showing calculated measures

Refresh

Data refresh options, and behavior depend on both the Get Data choice (connect or import) and the structure of the Excel file.

Connected Workbooks

If the workbook is connected to the service, and it does not contain a data model, it cannot be refreshed. This is true even if the worksheets in the workbook contain data from Power Query queries. This is the only scenario that does not support refresh in any way.

If the workbook contains a data model refresh is supported. The interesting part is that refresh will be triggered not only for the data model itself, but for any worksheets that have Power Queries as a data source. Therefore, a workaround to the lack of refresh support for a worksheet with no data model is to add a blank data model.

For refresh to work, the data source must be available to the Power BI service. This means that the source must be available in the cloud or registered on an available On-Premises Data Gateway.

The important thing to note about connected workbooks is that the refresh options that are performed on them are permanent – refreshed data is stored with the workbook. This means that if the connected workbook is stored in SharePoint, or shared through OneDrive, updated data is available to all users with access regardless of whether they are Power BI users.

Imported Workbooks

Refresh options for imported workbooks are slightly more complicated. As mentioned above data is either imported from the worksheets, a data model imported into the service or both.

If data was imported from worksheets, then the Excel file is the data source from the standpoint of Power BI. If the file is stored in SharePoint or OneDrive, it will automatically be refreshed every hour by default. This means that changes to the underlying Excel file will be reflected back in the Power BI service within an hour. This feature can be disabled, but it is not possible to change to hourly schedule, nor precisely when it will occur.

Refresh options for workbooks in OneDrive/SharePoint

Refresh options for workbooks in OneDrive/SharePoint

If the file is stored on a file system, it can be scheduled more granularly, but you will need to connect to it through an On-Premises Data Gateway.

If the file contained a data model that was imported into the service, then the original source of data for that data model (the query) is what the Power BI service will refresh from (NOT the Excel file itself). In this case, the refresh options are the same as with most other Power BI data sources – Excel is taken out of the picture completely, and any changes to the source Excel file will not be reflected into the service. The exception to this is if the file had both a data model, and worksheet data that was imported.

In the case of an Excel with both a data model and worksheet data, both cases above will apply. The workbook is used as a data source for the table that was created by Power BI on import, and the original data model’s source is updated independently. This means that changes to the worksheet data are reflected in the Power BI service when refreshed, but any model changes to the original Excel file are not. Both the OneDrive and regular refresh schedules are used for imported files of this type.

Refresh options for a combined data source

Refresh options for a combined data source

The following table summarizes the refresh options available for file structure and connection type.

File Structure	Get Data option
	Connect	Import
Worksheet data	None	Refresh from worksheet
Data model only	Refresh from model source	Refresh from model source
Data model plus worksheet data	Refresh from model source and worksheet source	Refresh from model source and worksheet

Summary

Both Excel and Power BI are powerful tools in their own rights, and the decision to use one does not preclude using the other and in fact there are many good reasons for doing so. Bringing refreshability to Excel files stored in SharePoint is just one of them. It is however important to understand how it all works in order to get the maximum impact.

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Using Power BI to Report on Lookup Fields in SharePoint

Published April 18, 2018

This post is the sixth and final post in a series exploring Power BI and complex data types in SharePoint. This post examines the various options in Power BI for working with lookup fields. The previous posts are:

A lookup field in SharePoint contains values looked up from another list in the same SharePoint site. Strictly speaking, the field contains only the ID from the item in the source list, and the value(s) is/are looked up whenever the field is displayed. The lookup field can also be used to display multiple field values from the target list items.

Defining a lookup field in SharePoint

The List

Consider the following list that contains a lookup field named “Neighbourhood”:

The lookup field neighbourhood in the SharePoint view

We can see from the screenshot above that the text value for neighbourhood is displayed in the view, although only the row identifier is stored in the column. We will be able to get both values and more if desired in a Power BI report, but first we need to build the report using Power BI Desktop.

Loading the Data

We first launch Power BI Desktop, select “Get Data” and then choose SharePoint Online list (if connecting to SharePoint Online) or SharePoint List (if using SharePoint Server). We are then prompted for the URL of the SharePoint Site. The dialog is titled SharePoint lists, but the value is the URL of the site, NOT the list itself. Once this is entered we are prompted for credentials if we haven’t connected to this site before. After entering credentials, we can select the list that we want to report on. In our case, it’s named “Listings”. We select it, and then click on the Edit button.

Loading data rom the Listings list

Once the data loads in, one of the first things that you’ll notice is that there are a lot of columns to choose from, and it’s a good idea to remove the columns that you don’t need. We can do this by right clicking on the desired column titles and selecting “Remove”. In this case, we can remove the ContentTypeId column and everything to the right of it, with two important exceptions. We want to keep the “FieldValuesAsText” in addition to the special “Neighbourhood” column at the far right of the columns, as we’ll be needing them for our options below.

Using FieldValuesAsText

Examining our columns, we can see that amongst the simpler text fields, we don’t have a “Neighbourhood” column, but instead, a “NeighbourhoodId” column, with numeric values. We do have a Neighbourhood column further off to the right, but it doesn’t display simple text (we’ll come back to this shortly). If we simply want the text value of our lookup target, we can use the “FieldValuesAsText” column quickly.

Scrolling right in the Query editor view, we find the “FieldValuesAsText” column. The record values represent a one to one relationship with the text values of the list row, so we can click on the column expander at the right of the column title. From there we can extract the text value of any column, including our lookup field, “Neighbourhood”.

Extracting text using FieldValuesAsText

With “Neighbourhood” checked, and nothing else, including the “Use original column name..” option, we can click OK, and the “FieldNameAsText” column is replaced by a new column, “Neighbourhood” that contains the text values for Neighbourhood.

If this value is all that is needed, then this is a totally valid approach, and we can move on to report building. However, this is only one way to achieve this goal. If more information is needed, then other methods may be more suitable.

Retrieving all Lookup Field Values from the Extended Column

Given that the lookup target item is a SharePoint list item, all that item’s properties are available to us. We can access them from the extended column set up for the field. In our case, the original “Neighbourhood” column is the extended column. We can expand this column by selecting its column expander.

Extracting field values using the extended column

We then deselect all of the columns except the ones that we want to use in the report. The fields available are the fields available in the target list. In our case, we select the “Title” field, as it is the one being looked up. We can however retrieve any of the fields that we need from the target list.

Keep in mind that “Title” in our example is a simple text field, so no further action is necessary. The retrieved fields can be complex (person, MMS, etc), but keep in mind that if a complex field type is retrieved, it will need to be transformed just like any from the list in question.

The field name in the target list may not adequately describe its function for the report. In our case, “Title” actually means “Neighbourhood” in this report. It’s a good idea to rename it.

Finally, if multiple field values are to be retrieved, the data model could grow significantly. This is because the values for every field are repeated in every row of data. Given that the original lookup column adds a measure of relational behaviour to SharePoint, using this relationship is the most efficient way to work with this data. Power Query allows us to do just that.

Working with Related Tables

To work with related tables, we need not only the original data table (in our case, “Listings”) but also the table for the lookup list itself. To do this, from the Query Editor, we create a new data source like the one created above for “Listings”, but instead we select the lookup list (“Neighbourhoods”).

Once imported, we can remove any extraneous columns, and then set the data type for the ID field to be “Whole Number”.

Setting the ID column to whole number

We also need to set the data type of the “NeighbourhoodId” column in the Listings table to “Whole Number”. Once these options have been set, we are ready to work with the data model and the report. We select “Close and Apply” from the ribbon to load the data into the model.

Once loaded, we launch the relationship builder in the design pane in order to establish the relationship between the two tables.

Power BI relationship editor with default relationship defined

We can see that Power BI has already detected a relationship. However, it is not correct. The model designer assumes that because both tables contain an “id” column, then they must be related. However, the true relationship is between the “Id” column in our “Neighbourhoods” table, and the “NeighbourhoodId” column in our “Listings” table.

We must first delete the detected relationship by selecting the connector between the two tables and pressing “Delete”. We can then create the proper relationship by dropping one of our related columns onto the other. Once this is created, we also need to ensure that the “Cross filter direction” is set to “Both”. We do this by double clicking on the relationship connector and selecting the appropriate option.

Setting cross filter direction

Power BI relationship editor with correct relationship defined

Once the relationship has been established, we can return to the design pane and construct a rudimentary report. We drag a few fields from Listings into a table, create a calculated measure for the number of listings, and we add the “Title” field (renamed to “Neighbourhood”) to the canvas separately. Once we set the visualization for “Neighbourhood” to a slicer, we can easily slice our listings data by neighbourhood.

Slicing report with the values from a lookup field

We can therefore see that there are several options for accessing data for a lookup field, ranging from simple to complex. The trade-off for simplicity is flexibility. Which approach used will depend on your requirements but storing the lookup table separately is the most efficient as the data is only stored once and referenced.

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The Road Ahead for SQL Server Reporting Services and Power BI Report Server

Published March 20, 2018

Power BI has garnered a lot of attention in the past few years as the “place to be” for Microsoft business intelligence. Power BI is of course Microsoft’s cloud platform for dashboarding and reporting. On premises, Microsoft’s reporting platform has traditionally been SQL Server Reporting Services (SSRS) and is now joined by Power BI Reporting Server (PBIRS). While there are differences between the two in the way that they are licensed and distributed, the main technical difference is that PBIRS includes SSRS, plus the ability to render both Power BI reports (pbix) and Excel based reports through Excel Online.

PBIRS is therefore able to render all four report types as defined by Microsoft:

Paginated	RDL (classic SSRS style reports)
Interactive	PBIX (Power BI Desktop)
Mobile	RDLX or PBIX (Datazen and Power BI Desktop)
Analytical	XLSX (Excel)

Given that PBIRS is the on-premises reporting platform, and Power BI is the cloud platform, they should theoretically be able to perform the same task. However, this isn’t the case. Currently, there is no way to render paginated (or as I like to refer to them, operational) reports in the cloud. PBIRS can render all report types, but today, the Power BI Service cannot.

Jason Himmelstein and I recently hosted Riccardo Muti, Microsoft Group Program Manager for Power BI Report Server on our BI Focal podcast (you can hear the whole show here) and we asked him a few questions about the future of paginated reports.

While it has been hinted at before, Riccardo explicitly stated that the ability to render paginated reports in the cloud was not only on the roadmap, but it was actively being worked on. This will complete the picture for BI in both the cloud and on premises. This is important because I the ideal world, the choice of on-prem, cloud, or hybrid should be related to the data in question, not the available features. No more standing up an Azure virtual machine to be able to get printable, paginated structured reports.

Riccardo sees this service rolling out in three distinct phases. Initially for the preview, RDL reports will be able to connect to cloud data sources like Azure SQL Database, Azure Data Warehouse, Azure Analysis Services, and Power BI data models. The next stage will leverage the On Premises Data Gateway in order to connect to on-premises data like SQL Server, Oracle, Teradata and more. The next phase will begin to leverage Power Query to connect to the world of data sources that Power Query Offers.

With paginated reports adopting Power Query, and Excel having moved to Power Query as a default, it is hard to argue with making an investment in learning Power Query. It all seems to be coming together.

One of the top requested features for SSRS on-premises for year has been the ability to authenticate to it with claims-based authentication. This will be the native authentication mechanism for paginated reports in the service. When this is combined with the fact that these reports will ultimately leverage Power Query, and the vast number of data connection options that it brings, it’s not hard to wonder when these features will be coming down to the on-premises product. When asked about this, Riccardo confirmed that this is certainly the vision for the product.

The future looks bright for both cloud based, and on-premises reporting.

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Using Power BI to Report on Multi-Value SharePoint Fields

Published January 24, 2018

Power BI has direct support for reporting on SharePoint lists and documents whether SharePoint is on-premises or in the cloud. Getting at your data is relatively straightforward if you know the URL, but as I’ve written about before, SharePoint data can be idiosyncratic. Text and number fields work right away, but more complex data types require a little more thought, and this is certainly true of multi value fields.

Multi value fields are really SharePoint’s way of approximating the behavior of a one-to-many relationship of a relational database. SharePoint can store list-based data, and even though it has the concept of a lookup field, it is not a relational database. Chances are however that if we’re reporting on this type of field, we want it to behave as though it were. Fortunately, Power BI gives us several options for doing this.

The List

Consider the following list that contains a multi-value choice field named Amenities:

In the view, the different values are flattened, and displayed as a single value using a comma to separate them. How do we go about reporting on this data in Power BI? There’s no right answer, as it depends on the report requirements, but there are several options. In all cases the data first needs to be brought into Power BI Desktop.

Loading the Data

We first launch Power BI Desktop, select “Get Data” and then choose SharePoint Online list (if connecting to SharePoint Online) or SharePoint List (if using SharePoint Server). We are then prompted for the URL of the SharePoint Site. The dialog is titled SharePoint lists, but the value is actually the URL of the site, NOT the list itself. Once this is entered we are prompted for credentials if we haven’t connected to this site before. After entering credentials, we can select the list that we want to report on. In our case, it’s named “Listings”. We select it, and then click on the Edit button.

Once the data loads in, one of the first things that you’ll notice is that there are a lot of columns to choose from, and it’s a good idea to remove the data that you don’t need. In this case, we can remove the ContentTypeId column and everything to the right of it, with one important exception We want to keep the FieldValuesAsText column (we’ll come back to that shortly). Remember, for our purposes here, we want to report on the multi-value column, Amenities. The simplest way to represent this data is as a string of comma separated values, which is the same way it is shown in a SharePoint view. It is also possible to use this data in a more sliceable, or structured way. Let’s start with the simplest.

Extracting a Simple Text Field

If our report is to show all the amenities associated with a listing, then a simple text string will do the job. Examining the data in the Query Editor, you will notice that there are no values displayed for Amenity, the way that there is for simpler field types like “Sold” or “Asking Price”. Instead, each cell contains a “List Value”. Each of these Lists contains the Amenities values for each record and clicking on one will extract those values for that one item. However, of we want to extract the values for all items, so we need to expand the column by clicking on the expand icon in the column header.

We are then given two options, “Expand to New Rows” or “Extract Value”. To extract the values into a single value, we select “Extract Values”

We are then given the opportunity to choose a delimiter, and after that the values in the column are replace with simple text values.

Another way to accomplish this same thing is to use the FieldValuesAsText column (referenced above). The fields in this column contain a single record containing the textual values for all the complex data types in a SharePoint list. If you are doing reporting with SharePoint data in Power BI, FieldValuesAsText is your friend. To use it, we just click the expand icon in its column header, deselect all of the columns that we don’t need (which in our case is everything except Amenities) and click OK. We are returned the contents of the list in a comma separated string for each item.

At this point, we can report on the data.

We click on Close and Apply in the ribbon to return to the report canvas. We add a table visual, and add some of the fields like address, street number, and Amenities to it. You’ll see that the table looks much like it would in a SharePoint list. Next, we add a slicer to the report page, and use one of the other columns, like City. Clicking on a city will filter the table by that city as expected. Now, if we want to filter by Amenity, we can add the Amenity column to the slicer, but it won’t behave the way we want it to. Each combination of amenity values will be represented as a single value. We are unable to slice on a single value like “Garage”.

Flattened multi value field as slicer

To use discrete values for our multi-value field, we need to do a little more work in the Get Data (Power Query) interface.

Extracting Discrete Values from a Multi-Value Field

We follow the initial steps from the “Simple Text Value” section above, but when the expand icon is selected, we select “Expand to new rows” and NOT “Extract values”. When this is done, each row will be copied to accommodate all of the individual Amenity values. If there were 3 Amenity values, we wind up with three rows of identical values, but with each of the values for Amenity.

Expanding to new rows

We can now go to the design surface and add a table, and a slicer that uses the multi value column, and now we can filter on discrete values. This may be sufficient in many cases. However, if we create a measure that based on an aggregate value for the table (like COUNTROWS or SUM), the value will not be correct if there are no slicer values. It works with all of the duplicated rows.

Incorrect number of listings

Again, this approach may work in many situations, but for ultimate flexibility, we need a true one-to-many relationship. Luckily, Power Query gives us a few simple tools to do this.

Creating a One-to-Many Relationship

Again, we follow the initial steps from the “Simple Text Value” section above, but do not click on the expand icon in the Amenities column. A One-to-many relationship requires at least two tables, so now is the point where we need to add a second one. We do so by copying the first. We right click on the query in the Queries pane and select “Duplicate”.

This creates a second query identical to our first, which will load into another table. We can then give the new query a better name, like “Amenities” in our case. Next, we select the Id, and the Amenities columns, right click and select “Remove Other Columns”, and we are left with only the ID and Amenities columns. Now we select the expand icon and select “Expand to New Rows”.

We now go back to the original query and remove the Amenities column. The result is two tables, Listings and Amenities, with Listings containing the bulk of the information. We then click on Close and Apply in the ribbon to load the data into the model, and the relationships icon to take is to the relationship builder. We need to establish a relationship between the two tables, and we do so by clicking on the Id field in one table and dragging it to the Id field of the other. Finally, double click on the relationship line between the tables, and be sure to select “Both” for Cross Filter Direction – that way Amenity values can filter List values. If “Both” is selected, the directional arrow will point two ways.

Now, the same visuals from above can be added using the Amenity column from the second table as a slicer, and everything should work as expected. Listings will be filtered by the value, if no amenities are selected, the correct number of listings will be shown In the data card, and no duplication will appear in the table.

SharePoint multi-value columns are an attempt to replicate the capability of a relational database. With a few simple steps, Power BI can take SharePoint data, normalize it, and analyze it as if it were a true relational database.

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A Simplified Method of Working with SharePoint Data in Power BI

Published July 6, 2016

Although I typically advise against it, there are valid reasons to report on SharePoint list data directly. Power BI Desktop makes this data quite easy to access – you can use the built in connectors for SharePoint or SharePoint Online, or, due to the fact that any SharePoint list is available via OData, you can also use the OData data connector. Microsoft has recently made improvements to both methods, but the SharePoint connectors bring some significant usability advantages. One of these advantages is what I’m calling “summary columns”.

The Problem

Consider the following SharePoint list with different field types:

Connecting to this list with Power BI desktop and editing the query returns all of the list fields regardless of their visibility in the user interface. Assuming that we want to work with the above field values for analysis purposes, we can discard the fields that we don’t need, and reorder the remaining, leaving us with only these fields.

However, you can immediately see that we’ll need to do some work in order to get our data in a usable format.

The title field is simple enough – it’s value is immediately available, no issues there, and no changes are necessary. From the Name field, several columns are returned. Two are the ID of the name in the site collection’s user list. It would be possible to connect to the root of the site collection, retrieve the user list, and establish a relationship between the tables, but clicking the expand icon will allow Power Query to do a lookup for each ID and return the desired attribute, in this case, Name.

The same is true for the lookup field type – the column can be expanded in order to include any attribute of the source item. The field using managed metadata works in a similar fashion; it can be expanded in order to retrieve the text value of the managed metadata item. Link fields work the same way – the can be split into two columns, the link itself and the description. However, the field containing multiple values is a little different. The great news here is that it’s possible – previous versions of Power Query couldn’t work with multi value fields, but now the SharePoint data source supports it.

Multiple value field values are returned as a list. With list items, the expand icon will duplicate the entire record for each value on the list. This may or may not be the desired behaviour, but remember, this is Power BI. Everything can be aggregated.


Before	After

The conclusion to be drawn here is that in order to represent SharePoint list data that is using any sort of control more complex than text or number, we need to do some work. However, the good news is that someone (I’m not sure if it’s the Office Team of the Power BI team) has added a feature that makes this whole process much simpler.

The Solution

After connecting to your SharePoint list, edit the query. Instead of diving in and performing all of these manual transforms, select your multi value column(s) if you have any (this will make more sense momentarily). Select any rich text columns as well, and then scroll right to find a column named “FieldValuesAsText”. Select this column, then right click on it and select “Remove Other Columns”.

The FieldValuesAsText column is our magic bullet. It automatically converts most (rich text fields being the exception) of the more complex SharePoint data types to simple text that work well within Power BI. Simple click on its expand button, select the columns that you want to include in your analysis. I find it useful to deselect “Use original column name as prefix” as well. We are left with textual representations of our field data.

You will notice that the multiple value fields here have their values separated by commas. For multiple values, I tend to prefer the “raw” approach, which is why we retained the multiple value column above. We can still expand it and create a separate line for each value, and remove the column created by “FieldValuesAsText”.

Finally, you may have noted that the Rich Text field isn’t automatically converted. In order to extract useful text from it, we still need to use Power Queries transformation functions such as Replace Value, Trim, and Clean.

In a nutshell, if you’ve been frustrated by formatting or data type limitations when using SharePoint data in Power BI, have another look, and check out the FieldValuesAsText column. It will make everything a lot simpler.

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