Background

Excel is the most used spreadsheet software in today’s era, used by every level of organization. Quite a huge amount of unorganized data is maintained in Excel workbooks, owing to ease of quick creation, storage & sharing of Excel files over database. Resultant many of the Power BI reports / dashboards are based on Excel as data-source.

Excel design enables it to act as a quasi-DBMS, with individual worksheet acting as a table and workbook as a database. But Excel, being a spreadsheet genre software, lacks enforceability making it vulnerable to breaking-down entire Power BI report in certain scenario. This blog showcases few scenarios, which developer needs to take care while using Excel as data-source for Power BI report.

Scenario: Data Type mismatch

Excel supports datatypes like text / number / date / time / logical. Unfortunately, it does not support strong enforcement of datatypes in respective columns. For e.g., users are free to type text into date datatype columns & so on. Data Validation rules of Excel can enforce this, but these rules can easily be de-activated or deleted in few seconds.

As shown in above screenshot, the data contained all the dates when initial Power BI report was prepared. But one fine day, some user entered question marks (???) in Date column since he/she was unaware of Date of transaction during data entry & decided to fill up that information when it becomes available. But such placeholder values generates errors, as Power BI attempts to skip these rows.

Power BI Desktop will take care to show up the error as shown in the above screenshot. But Power BI Service might not show the error on the face & silently skip the rows loading remaining data. This might affect reporting since amounts written on those rows would never be added as those rows were not imported into data model.

Scenario: Summary Rows at the bottom

Many people have a habit of calculating grand totals at the bottom of data in Excel (refer below screenshot).

This might ruin reporting in Power BI, as this row also gets incorporated into data, thereby inflating sum totals. Below are the comparative images section of the Power BI report with summary cards showing different figures before/after the summary row

Proper care needs to be exercised when such Excel data is intended to be used as data-source for Power BI. End-user of the Excel workbook needs to be informed of the above thing, and Excel summarization needs to be done in separate worksheets to prevent this.

Scenario: Gaps in Data

Certain times user might insert gaps in data rows (typically observed for printing purposes to adjust print preview range)

Power BI imports data including blank rows. The majority of the calculations would not get affected, except few DAX functions which will go on to include blank rows in calculations.

Above is the result using the COUNTROWS ( ) function, which also includes blank rows in the calculation result.

Calculation results differ a bit from other function like COUNT ( ) since this function excludes blank cells while counting.

Few developers prefer to use the COUNTROWS ( ) function, as it yields results faster (it simply returns back the row count of the table). Whereas, COUNT ( ) is relatively slow since it validates the values of each cell while calculating. Power BI report developer needs to account for these scenarios & develop measures accordingly.

Gaps also create blank options in the slicer dropdown, which does not appear professional.

The above mess could be avoided by adding an extra step of removing empty rows (refer to below image)

Scenario: Renaming of Column(s)

Many times end-users inadvertently change column titles, for better understanding or readability. Some business users might not prefer technical name of the column, so they might be tempted to re-name them before creating PIVOT tables/charts. Like in the below example, Excel workbook user changed column name Amount to Amount (in Rs) since organization is having multi-currency reporting, so user wants column title to depict this fact that amounts are in Indian Rupees.

Renaming results to failing of dataset refresh for Power BI reports, since originally while developing report, the column was titled as Amount. Power Query stores column names derived from Excel in the M script for import of Excel data.

Below is the error displayed when Power BI report is opened through Power BI Desktop

Report viewer needs to be a bit vigilant in monitoring refresh errors, since it shows-up as a small error icon as shown in below image

On clicking the error icon, message as below is shown which clarifies error in detail

Report users should get their email ID added into refresh failure notification triggers. Power BI will display data of last successful refresh for reports, which is even more disastrous.

Scenario: Cell Errors

Certain times, Excel formulas break due to deletion of cells which formula referred or any other miscellaneous reason. This results in cell error (as shown in below screenshot)

Just like Data Type mismatch discussed above, when the report is refreshed from Power BI Desktop, it would display count of rows with errors. But in Power BI Service, these errors are silent. Although Power Query can perform basic level of handling for these errors like substitution of errors with other value. Since this error originates from source, fixing it in source is more sensible than handling it in Power BI.

Scenario: Renaming / Moving of Excel file

Power BI supports absolute path while referencing any source file (refer below screenshot).

So, if the file is moved to some other folder, or maybe renamed, then the path needs to be updated in Power BI report too.

Same applies for Excel files referencing sharepoint (refer below screenshot).

Renaming or moving file to different folder, will result to change of sharepoint URL which needs to be updated.

Report developers can introduce parameter & link file path / URL with parameters which is easy to update from Power BI Service, without having to download, modify & re-publish Power BI report. It is not solution, but just an easy hack.

Google Sheets enjoys advantage in this scenario compared to Microsoft Excel, as links of Google Sheets do not change on renaming or moving file. Google Sheet assigns unique identifiers to the file which is independent of file name or location. Power BI supports Google Sheets as data-source & one can leverage this, if renaming/moving of file is unavoidable & happens frequently as a normal business scenario.

Conclusion

Excel might be a preferred choice of data-source, but one needs to think from broader perspective when using it for analytical & reporting purpose. Moving some of the Excel based data entry into Power Apps would be a strong solution, as forms have capability to validate the data before storing it. Power Apps use Dataverse as a backend which Power BI can connect easily. On an organizational level, this approach provides stronger reporting capability, compared to Excel.

AI Insights and Visuals

AI has been evolving in many sectors of technology, and the same is being implemented in Power BI to a very good extent. In 2019, there were enhancements in Power BI where more powerful AI features were included, like AI visuals, Text analytics, the inclusion of Azure machine learning models, Image recognition, which plays an important role in advanced analytics, quicker insights from data models, an automatic Q&A system, and more. In Power BI, there are various sections with AI features and capabilities.

As we know that Power BI work role is divided into 3 parts, which involve Data Preparation, Data modelling, and Data visualization, and each consists of some AI features or insights:

• Data Visualization: Consists of 4 visuals having AI capabilities: Key Influencers, Q&A Visual, Smart Narrative, and Decomposition Tree.
• Data Preparation: AI Insights Section: Text Analytics, Vision, and Azure Machine Learning
• Data Modelling: Q & A Section, Q & A Setup, Language, Linguistic Schema

Now let’s start with the visual section:

1. Key Influencer Visual
2. Q & A Visual
3. Smart Narrative
4. Decomposition Tree Visual

These visuals can be selected from the Insert tab and the Visualization tab of the Power BI desktop.

KEY INFLUENCER VISUAL

• Key Influencer uses the concept of machine learning for analysing the data and provides useful insights about the key factors that are impacting the selected field or metrics that we want to analyse, along with a visual representation.
• Helps in determining how the factors affect the selected metrics that we are analysing. For example, In the image, we are analysing the Gross sales, so here this visual helps to find out how the gross sales are affected by the factors, that is, when they get lower or higher.

There are two main tabs in the visual: Key influencers and Top segments.

Top Segments Tab

The Top Segments tab provides a detailed view of any segments that Power BI has identified for the metrics. A segment is simply the combination of the factors that affect the metrics that we are analyzing. The bigger the circle, the greater its impact, and after clicking those bubbles, a new page gets opened where we can observe more details about the factors (refer to the below image). It will drill down into the data and give you the details of that segment.

Q & A VISUAL

• The Q&A visual used in Power BI to create visuals uses the concept of natural language processing to interpret questions and give answers from the data. It simply asks the questions and gets the appropriate answer related to them with visual insights.
• It is similar to Smart Narrative Visual, but in addition to text output, it gives results in a visual format, and we can also convert the results into standard visual.
• With the help of Q&A visuals, we can ask a variety of questions, such as: Returning the top N products; providing a filter; condition-based scenarios; date filtering; returning a specific visual; using complex aggregations; sorting results; comparing data; and viewing trends or insights from the data.

The above figure shows a Q&A visual where automatic suggestions are created for gaining insights.

Figure: We have asked the question “top countries by net sales,” where the visual generates automatic output in the form of a chart for showing insights.

This option helps in converting the desired output into a standard visual. We can use this feature once we are satisfied with the solution and it matches the requirement, after which we can convert to standard visual.

This option opens up the Q & A setup menu for more in-depth configuration required, like review questions and managing the terms that have been set. It ultimately opens up the below window:

This window has full-fledged settings required for changes or updates required in the Q&A. Here we can set the questions, review them, search for suggestions, and manage the terms that we have added.

DECOMPOSITION TREE

• This is a highly interactive visual that allows you to decompose (break down) a measure by various attributes across different dimensions. This visual is used to perform root-cause analysis and understand your data in a detailed manner, which is enhanced by the built-in AI functionality.
• This AI visual breaks down a specific field or value into its sub-sections and gives data in a hierarchical manner, like a tree structure. It helps in revealing the relationships between different parameters of the business.

In the above image, we can observe that by expanding the +’ icon, we can go into in-depth details of the root node, like here we have a countries list, then in selected countries there are stores, and after that protein details, so it’s kind of a hierarchy-based analysis where in-depth insights can be taken out of the data model.

SMART NARRATIVE

• Smart Narrative is an important AI visual that helps in getting a quick explanation and key insights about the visual or report page in textual format. It is best suited for finding out a quick summary of the report.
• It is dynamic in nature that is content gets updated after any changes in the visuals or after filtering. It also gives you the freedom to update any text or add some key points about the visuals.
• Two methods: 1) creating a word-based narrative based on just a single visual, like a summarise option. 2) Create a full narrative, or we can say summaries, for all valid visuals on a dashboard.

  Figure –  “Summary: Smart Narrative” shows all the key points gathered from the scatter, line and doughnut chart displaying on the page. It has summarized the key aspects of all the visuals and presented them in text format.

  Note: Although it is dynamic in nature, any changes made to the data will not reflect in the visual.

  Smart narrative not implemented in : KPIs, Tables, matrices, cards, and some custom visuals.

Power Query AI insights:

• Text Analytics:  Text analysis (Detect language, Score sentiment, Key phrases)
• Vision: Tagging images, Image Analytics
• Azure Machine learning: It allows the use of Azure ML models.

Data Modelling Q & A:

• Q & A setup: Teach to understand, review questions, and manage the terms you have added.
• Language: Changing the language of the questions asked in the Q&A
• Linguistic Schema: Import or export the file containing important information. Q & A uses the dataset and the custom terms you have taught it to understand.

Note: To use the AI insights feature, it requires a Power BI Premium subscription. If you enable the Power BI Pro time trial, you can test and learn about this feature.

Limitations:

• In some conditions where there is too large and complex data, there might be a slight variation in the visual’s output.
• If there are changes in the data, they don’t reflect in some visuals, as we have seen in the Smart Narrative.
• Next point is regarding data security, which revolves around the use of AI-based visuals.
• Q&A visual works best when your data is fully prepared, with all fields in accurate language. If your fields are not fixed or has lot of variation the creating a Q&A visual is trickier to use in such cases.

So, this was about the first part of the Power BI features blog, where we have seen how AI plays a vital role in Power BI, like in the functioning of the visuals section, power queries, and the data modelling part.

Thank You!

Happy Reading!

Power BI filters are useful tools for organizing data, visualizing and comparing your data visualizations, and creating reports. They are utilized to sort data according to a chosen condition.

Filter Types :

1. Visual Level Filter
2. Page Level Filter
3. Report Level Filter
4. Drill Through Filter

On the right side of the dashboard, locate the Filter Pane. From there, we may apply all of these filters. (refer to the image below)

Now we will see each filter type in detail, like how to apply them and also the type of filtering that can be applied:

1. Visual Level Filter:

This Filter is used in sorting single visuals, for example, cards, slicers, bar charts, maps, and other visuals. Filtering is done at a granular level. Let’s see the steps involved in it with an example:

Step 1: Expand Visualizations, Filters, and Fields panes from the right side.

Step 2: To apply a visual filter, you just have to click a visual that you want to filter; for example, in the below image we have to click Sunburst visual.

Visual Level Filter for Sunburst visual with fields

Step 3: After that, select the field we want to filter from the Filters pane. Here in the below image, we have chosen the ‘Continent’ field and from that ‘Europe’ option. As a result, Sunburst visual will filter as per the selected option. (refer to the below image)

We can also do multiple selections and also new Data field can be added there.

Sunburst Visual after applying a filter of ‘Europe’ from field ‘Continent’

2. Page Level Filter:

Page level filter used to apply filters on a single page of a report applies filters to all the visuals present on that page.

Step 1- To use page-level filters, use the option “Filters on this page” from the Filters pane

Step 2- Drag and drop a field based on which you wish to sort the information on a report page. In the image below we can see that the ‘Country’ field is dragged into selection.

And in the below image whole page is being filtered by selecting ‘France’ and Germany’ from the ‘Country’ field, so the whole page will show details related to these 2 countries only. Here we can make both single and multiple selections as needed.

 3. Report Level Filter:

To apply a filter to the entire report, that is, to all the pages of a report uniformly, we use report-level filters. To apply this filter, drag and drop a field from the Fields section into the ‘Filter on all Pages’ section. Let’s start with the below examples we have filtered the whole report by selections ‘Australia’ and ‘Cannada’ from the data field ‘Country.’

As we can now see, the Report pages – ‘Orders’ and ‘Custom Visual’ are getting filtered and showing only data related to country Australia and Cannada.

 4. Drill Through Filter:

By using this filter, we can focus on specific entities or on a particular field. This filter helps us to get a deeper insight into the data we want. For that, we first add the fields in the drill-through section. We can analyze any part of the data set and create any drill-through views.

I will focus more on Drill through filter and step by step process of creating it in the next blog series. Stay tuned!

Till now, we have seen types of filters in Power BI; now we will look into modes of Filtering:

1. Basic filtering: It’s a very basic form of filtering, which is applicable in all 3 forms of filters. Here simply we can select an option by which we want to filter and also search the options, like country names. (Refer to the below image)

   2. Advanced filtering: This filtering is also present in all 3 types of filters we have seen above. In the figure, we can see how we do advanced filtering. It has a unique and important role; by this filtering, we can find ranges of data by applying some conditions or rules. It gives you more precise control over what you want to filter out.

Based on the type of columns, advanced filtering capabilities vary. In the second image, we can see the set of rules or conditions we can apply.

So this was all about filters and filtering in Power BI. It is a very useful feature that helps in sorting our data and making an effective and efficient dashboard.

What is RLS with Power BI

Row-level security (RLS) with Power BI can be used to restrict data access for given users. Filters restrict data access at the row level, and you can define filters within roles. In the Power BI service, members of a workspace have access to datasets in the workspace. RLS doesn’t restrict this data access.

You can configure RLS for data models imported into Power BI with Power BI Desktop. You can also configure RLS on datasets that are using DirectQuery, such as SQL Server. For Analysis Services or Azure Analysis Services lives connections, you configure Row-level security in the model, not in Power BI Desktop. The security option will not show up for live connection datasets.

Define Roles and Rules in Power BI Desktop

You can define roles and rules within Power BI Desktop. When you publish to Power BI, it also publishes the role definitions.

To define security roles, follow these steps.

Import data into your Power BI Desktop report, or configure a DirectQuery connection.

• Note: You can’t define roles within Power BI Desktop for Analysis Services live connections. You need to do that within the Analysis Services model.

From the Modeling tab, select Manage Roles.

From the Manage roles window, select Create.

Under Roles, provide a name for the role.

Validate the Roles Within Power BI Desktop

After you’ve created your roles, test the results of the roles within Power BI Desktop.

From the Modeling tab, select View as.

The View as roles window appears, where you see the roles you’ve created.

Select a role you created, and then select OK to apply that role. The report renders the data relevant to that role.

You can also select Other user and supply a given user.

It’s best to supply the User Principal Name (UPN) as that’s what the Power BI service and Power BI Report Server use.

Within Power BI Desktop, Other user displays different results only if you’re using dynamic security based on your DAX expressions.

Select OK.

The report renders based on what that user can see.

Now that you’re done validating the roles in Power BI Desktop, go ahead and publish your report to the Power BI service.

Manage Security on your Model

To manage security on your data model, open the workspace where you saved your report in the Power BI service and do the following steps:

In the Power BI service, select the More options menu for a dataset. This menu appears when you hover over a dataset name, whether you select it from the navigation menu or the workspace page.

Select Security.

Security will take you to the Role-Level Security page where you add members to a role you created in Power BI Desktop. Contributors (and higher workspace roles) will see Security and can assign users to a role.

You can only create or modify roles within Power BI Desktop.

Working with Members

Add Members

In the Power BI service, you can add a member to the role by typing in the email address or name of the user or security group. You can’t add Groups created in Power BI. You can add members external to your organization.

You can use the following groups to set up row-level security.

• Distribution Group
• Mail-enabled Group
• Security Group

Note, however, that Office 365 groups are not supported and cannot be added to any roles.

Remove Members

You can remove members by selecting the X next to their name.

Validating the Role Within the Power BI Service

You can validate that the role you defined is working correctly in the Power BI service by testing the role.

1. Select More options (…) next to the role.
2. Select Test data as role.

Using the username() or userprincipalname() DAX function

You can take advantage of the DAX functions username() or userprincipalname() within your dataset. You can use them within expressions in Power BI Desktop. When you publish your model, it will be used within the Power BI service.

Within Power BI Desktop, username() will return a user in the format of DOMAIN\User, and userprincipalname() will return a user in the format of user@contoso.com.

Within the Power BI service, username() and userprincipalname() will both return the user’s User Principal Name (UPN). This looks similar to an email address.

Using RLS with Workspaces in Power BI

If you publish your Power BI Desktop report to a workspace in the Power BI service, the RLS roles are applied to members who are assigned to the Viewer role in the workspace. Even if Viewers are given Build permissions to the dataset, RLS still applies. For example, if Viewers with Build permissions use Analyze in Excel, their view of the data will be protected by RLS. Workspace members assigned Admin, Member, or Contributor have edit permission for the dataset and, therefore, RLS doesn’t apply to them. If you want RLS to apply to people in a workspace, you can only assign them the Viewer role.

Considerations and Limitations of RLS

The current limitations for row-level security on cloud models are as follows:

• If you previously defined roles and rules in the Power BI service, you must re-create them in Power BI Desktop.
• You can define RLS only on the datasets created with Power BI Desktop. If you want to enable RLS for datasets created with Excel, you must convert your files into Power BI Desktop (PBIX) files first.
• Service principals cannot be added to an RLS role. Accordingly, RLS won’t be applied for apps using a service principal as the final effective identity.
• Only Import and DirectQuery connections are supported. Live connections to Analysis Services are handled in the on-premises model.
• The Test as role/View as role feature doesn’t work for DirectQuery models with Single Sign-On (SSO) enabled.

Thank you for reading.

Reports In Power BI

A Power BI report is nothing but a multi-perspective view of a data set with visualizations that represent different findings and insights from that data set. A report may be a single visualization or pages full of visualizations.

Visualizations can be pinned to dashboards and if you select the pinned visualization, it will open the report from where it was pinned. One important point to remember is that reports are based on a single data set.

The visualizations in a report represent a nugget of information. These visualizations aren’t static, you have the option to add and remove data, change visualization types, and apply filters in your quest to discover insights and look for answers. Like a dashboard, a report is highly interactive and highly customizable, and the visualizations update as the underlying data changes.

The image below represents how a sample report looks.

Power BI Dashboard

A Power BI dashboard is a single page, often called a canvas, that uses visualizations to tell a story. Because it is limited to one page, a well-designed dashboard contains only the most important elements of that story.

The visualizations visible on the dashboard are known as tiles. These tiles are pinned to the dashboard from reports. The visualizations on a dashboard come from reports and each report is based on one data set. In fact, one way to look at a dashboard is to consider it as an entry point into the underlying reports and data sets. Selecting a visualization takes you to the report (and data set) which was used to create it.

Power BI Dashboards are a wonderful way to monitor your business, look for answers, and to see your most-important metrics at a glance. The visualizations on a dashboard may come from one underlying data set or many, and from one underlying report or many. A dashboard combines on-premises and cloud-born data, providing a consolidated view regardless of where the data lies.

A dashboard isn’t just a pretty picture, it is highly interactive and highly customizable. The tiles update as the underlying data changes

Dashboards vs Reports

The image below shows how a visualization looks on a dashboard once you have pinned it.

Conclusion

A Power BI dashboard gives you the following functionalities:

• Add tile
• Usage metrics
• View related content
• Set as featured
• Subscribe
• Share
• View
• Use Power BI Q&A to ask a question.

Thank you for reading.

Introduction

Without the right visuals, your Power BI report is redundant. To showcase powerful insights you need to understand when and how to use different visuals so that you can avoid wasting valuable time on building reports that don’t make an impact.

We’ll walk through the process we use to decide which visuals to use to showcase different insights, allowing you to correctly apply and design visuals in your reports so that you are implementing best practices and driving clear insights.

What are Power BI Visuals

Visuals are simply a visual (picture) representation of your data and are the most important part of any Power BI report as they are responsible for bringing your data to life.

Visuals help you to tell a better data story, enabling your users to simply and easily identify and understand the patterns in your data.

‍Types of Power BI Visuals‍

There are many ways to show your data through visualization.

When choosing your visual you need to consider what type of information your insight is looking to show.

The type of visual you chose to depict your data will depend on: the data you wish to communicate and what you want to say about that data.

Most visuals can be divided into the following 6 categories.

1. Comparison

Comparison visuals compare data between different categories.

Use Case: Income across fiscal years.

2. Data Over Time

Data over time visuals represent the spread of data over a period of time and are displayed to identify trends or changes.

Use Case: Sales performance over time.

3. Correlation

Correlation visuals are used to find whether there is a relationship between two or more variables.

Use Case: Price and demand.

4. Distribution

Distribution visuals are used to show how often values occur in a dataset.

Use Case: Distribution of orders

5. Part-to-Whole

Part-to-whole visuals show the breakdown of elements that add up to a whole.

Use Case: Profit by product segment.

6. Ranking

Ranking visuals showcase an ordered list based on a unique data point and are used when the position of the element is more important than its relative value.

Use Case: Leads by sales stage

Positioning of Visuals

Where you position your visuals in your report is critical.

A consistent layout and grouping of relevant metrics together will help your audience understand and absorb the data quickly. The correct layout ensures your dashboard is easy to understand and has a logical flow between different insights, which is important as users tend to process information from top to bottom.

Grouping relevant metrics together, such as KPIs, adds further to the logical report flow and the ease of user insight interpretation.

Top

The top of a dashboard should include high-level insights represented as visuals such as KPIs or Gauges.

The middle of a dashboard should represent trend-based data including activity-based metrics, and visuals that demonstrate data over time. This section is best suited for larger visuals.

Bottom

The bottom of a dashboard is reserved for granular metrics such as specific KPIs, or Tables.

Sizing of Visuals

The sizing of your visuals will depend on the level of detail you want them to display.

The greater the detail that your insight presents, the larger you want your visual to be so that users can distinguish the finer details.

For example, when visualizing an insight that compares multiple data categories, you would opt to have a larger visual in comparison to an insight which is visualizing a singular number.

Standard Power BI Visuals

Power BI has numerous options for how you can visualize your data.

Follow my below blogs for information on visuals. Thank you for reading.

Data Visualization – How to Pick the Right Chart? – Part 1

Data Visualization – How to Pick the Right Chart? – Part 2

Usage metrics help you understand the impact of your dashboards and reports. When you run either dashboard usage metrics or report usage metrics, you discover how those dashboards and reports are being used throughout your organization, who’s using them, and for what purpose.

Usage metrics reports are read-only. However, you can copy a usage metrics report. Copying creates a standard Power BI report that you can edit. You can also build your own reports in Power BI Desktop based on the underlying dataset, which contains usage metrics for all dashboards or all reports in a workspace. To begin with, the copied report shows metrics just for the selected dashboard or report. You can remove the default filter and have access to the underlying dataset, with all the usage metrics of the selected workspace. You may even see the names of specific users if your admin has allowed that.

Why Power BI Usage Metrics are Important

Knowing how your content is being used helps you demonstrate your impact and prioritize your efforts. Your usage metrics may show that one of your reports is used daily by a huge segment of the organization and it may show that a dashboard you created isn’t being viewed at all. This type of feedback is invaluable in guiding your work efforts.

You can only run usage metrics reports in the Power BI service. However, if you save a usage metrics report or pin it to a dashboard, you can open and interact with that report on mobile devices.

Prerequisites

• You need a Power BI Pro or Premium Per User (PPU) license to run and access the usage metrics data. However, the usage metrics feature captures usage information from all users, regardless of the license they’re assigned.
• To access usage metrics for a particular dashboard or report, you must have edit access to that dashboard or report.
• Your Power BI admin has to have enabled usage metrics for content creators. Your Power BI admin may have also enabled collecting per-user data in usage metrics.

View a Usage Metrics report

1. Start in the workspace that contains the dashboard or report.
2. From either the workspace content list or from the dashboard or report itself, select the icon for Usage metrics .
   
3. The first time you do this, Power BI creates the usage metrics report and lets you know when it’s ready.
4. To see the results, select View usage metrics. Usage metrics are a powerful way as you work to deploy and maintain Power BI dashboards and reports.

Conclusion

Usage metrics can play a key role in recognizing report usage and can potentially help in identifying hidden insights of reports on a periodic basis.

As in the previous two parts, we will see how to integrate and use Power Apps as a writeback option in the Power BI dashboard in this part. We will see how to change the fields passed from Power BI to Power Apps as a data source, as well as how to change Power Apps visuals with any other Power App visual.

Why need to change power apps visual? 

As we go from Dev to Prod after development is complete that time, we need to change the Power App visual to point to Production environment. 

How to change existing power apps visual to another app? 

There are two ways to do this. 

1. First way is you can directly Delete and re-add the visual but in this case, you must follow the same process to select environment, select app, add data fields, and set the Hight and Width of the visual etc. so this is not the effective way to change the app if there are more number of pages in report
   1. 1. 1. 1. 1. Process to replace Power Apps visual Directly                                                         
2. Second way is you can use the following method
   1. Open report in Power BI desktop
   2. Select Power Apps visual
   3. Change the visual to table visual
   4. Click save and close the report
   5. Reopen the same report and select the same table visual and change it to Power App
   6. Now you got an option to select new app select the app and use the app.
      1. 1. 1. 1. Process to replace Power Apps using table conversion method

 How to change data fields pass from power bi to power apps? 

1. Open the report in Power BI service only (Not works in power bi desktop)
2. Edit the report and select the Power Apps visual
3. Pass whatever the fields you need to change or add new fields
4. Select 3 dot from upper right corner and click edit
5. It will open Power Apps editor and the fields you passed are now can be accessible in power apps
6. After changes save and publish the app. click back to report and save the report and use the app with latest data.
   1. 1. 1. 1. 1. process to Update Data fields passed to Power App from Power BI

Power BI Performance Tuning

Whenever we start developing reports in Power BI three important things always come to mind these are data accuracy, data security, and report performance. If the report performance is not up to the mark, then there is no meaning of how much effort we have put into it, we have to think of redevelopment or redesigning of the report for better performance.

There are some best practices that when implemented can lead us to better performance, let’s see them one by one-

1. Reducing the model size– Model size is correlated in the negative direction to model performance, thus smaller our data model is, the faster it will be. We can reduce our data model by following the steps-

• Removing unnecessary data– Instead of importing all the data, take only the data you need. Remove all the unnecessary or unused columns from a table and filter out the data which is not useful.
• Turn of auto Date\Time- When this option is enabled, this feature creates a date hierarchy for all the date column and automatically create a hidden table for all the Date type column in the model which will lead to a huge data size.

We can check and disable Auto Date/Time in power bi desktop go to File -> option & settings -> Options -> Data Load (Global & current File). From here you can enable or disable it.

2. Model Designing– We usually create Star or Snowflake schema model in Power BI in which the Star schema model is the best design for Power BI reporting and gives you better performance than other models. Star schemas have a fact table and dimension table(which are connected to the fact table). It looks like as below

If we talk about the snowflake schema it is a further normalization of the dimension table to reduce redundant data and we create sub-dimension tables which are connected to the dimension table. It looks like as below

This model is more normalized than the Star schema model, but it gives less performance because it creates additional joins in queries for subdimension tables. So, the fewer joins we have in the model is better.

3. Report view – While creating a report there are some important points, we need to follow which will lead to better performance.

• Limit the number of visuals on the page or report, creating too many visuals on a page or report slows the performance.
• Removing unnecessary interaction between visuals will improve report performance. By default, on a report page, we have interaction between all visuals.
• Always use Power BI certified visuals in the report. It has better performance than custom visuals or third-party visuals.
• Using too many slicer visuals can reduce the performance of the report. Instead of using too many slicers in the report, we can use only the important ones and, can use filter pain based on requirements.

4. DAX – We can create both the Measure and calculated column using Dax, but it is important which one to use when because it is going to hamper report performance. We have to use Measure instead of calculated column whenever possible because the calculated column use space in the model and consumes both disk space and RAM which causes slow performance. On the other hand, measures are nothing but virtual formulas which don’t consume space, its only use calculation power.

• When to use Calculated column– If you want your new data to appear on ROWS, COLUMNS, or in FILTERS in a PivotTable, or on an AXIS, LEGEND, or, TILE BY in a Power View visualization, you must use a calculated column. Just like regular columns of data, calculated columns can be used as a field in any area, and if they are numeric, they can be aggregated in VALUES too.
• Use of Variables in measure can increase your measure performance also it reduces the complexity of measure and increases readability.

As you can see below I have created two measures, Total Sales Formatted v1 without variables and Total Sales Formatted v2 with variables. In v1 the total, target, and previous sales execute each time in a switch statement.

Whereas in v2 all the sales calculations are stored in a variables and we are reusing them in a switch statement. It will give better performance than v1 measure.

5. Cache update frequency- By default, the Power BI cache update frequency is set to one hour. Cache update frequency should be set at similar intervals to data source refresh frequency. If, for example, your data set refreshes only once per day, you should update the cache frequency accordingly. This improves report performance.

6. In a relationship tab we have to avoid bidirectional filtering because they introduce complex joins between the tables and affect background performance.

7. We can reduce the data loaded on a page by using Drill-through, bookmarks, and tooltip which reduce the page loading time.

8. Use Enterprise gateway instead of Personal gateway because it gives better performance. Personal gateway only support Import mode, whereas Enterprise gateway support both Direct Query and Import mode. It is recommended to use an enterprise gateway while working with a large dataset.

These are some of the best practices which are recommended and can be implemented to improve the performance.

Background

Microsoft Excel is a popular and preferred spreadsheet solution for quick daily use reporting by the majority corporations and businesses in the world. Many times, corporate users need access to organization’s data in Excel for further development of MIS Reports. Power Query is a powerful tool embedded in Excel which can connect to internal Database Server, Online CRM/ERP Services, Excel files etc. hosted within an organization’s network or over the cloud. Apart from that, users also heavily use hyperlink to another Excel files reusing existing source data already prepared by other people.

Challenges / Issues

Numerous challenges or issues occur when a user attempts to refer to data stored in another Excel file (via formulas) or Database Server / ERP (via Power Query) as below:

• There is a high probability that the original source Excel file to which references are made, might get deleted by the owner, or the structure may get modified, eventually breaking cell references. This would certainly lead to errors in reports.
• Data imported via Power Query arrives in the form of table. Tables are generally not suitable for reporting, as various reporting authorities prescribe a fixed and pre-defined format in which to prepare and submit a report.
• Power Query is a bit technical, and not every user might be able to grasp it.
• Database connection requires a server IP address, credentials, etc. Exposing of such critical information might lead to security lapses, allowing easy access to the organization’s internal data.

Solutions

Microsoft offers 2 ways to connect to Power BI published data from Excel

1. Direct connectivity to Power BI published dataset via Power Pivot, which supports creating DAX measures too.
2. Access of designated tables of Power BI datasets, which can be used as an Organizational Data Types.

Power PIVOT

A dataset is essentially a collection of tables co-related using the relationship feature of Power BI. Excel lacks an easy and direct way of analyzing relational data. One needs to use VLOOKUP, XLOOKUP, MATCH, INDEX like functions for co-relating data. Power Pivot provides an easy way of reusing Power BI published dataset, having already defined relationships to design reports in the form of Pivot Table or Pivot Charts directly in Excel itself.

Steps to analyze Power BI published dataset using Power PIVOT is as below

1. In Excel navigate to Data tab > Get Data > From Power BI <organization_name>
   
2. Right-hand pane window will appear. Select target dataset from the list
   
3. Excel will create a new worksheet with blank Pivot Table designer. Drag & Drop fields into Pivot just like regular Pivot.

Organizational Data Types

Excel supports 3 types of data types natively:

1. Text
2. Number (includes Date-Time)
3. Logical (True/False)

Formula is not a data type but evaluates to any of these 3 data types. Office 365 version of Excel introduced support for a new data type called Linked Data Type, which is of type record. Linked data type holds a reference to a record (or row) containing multiple fields. So virtually it is a cell which can hold multiple values internally (refer screenshot below).

(linked data types have an icon as prefix in cell value)

Value of a linked data type cell can be extracted into another cell by referencing the linked data type cell using formula =cell_reference followed by a dot sign, which further enumerates field names in that record type cell (refer screenshot below)

(evaluated screenshot below)

Excel includes a few built-in linked data type sources such as Stock Market, Currency, Geography, and so on. Apart from that, any Power BI dataset table can be promoted to include itself as a custom linked data type, which is available only to Excel users of that organization. Such data types are available as organizational data types.

Organization Linked Data Types can be created using Power BI, by setting a table as a featured table and then publishing it (screenshot below).

Advantages of Organization Linked Data Types approach:

• This approach prevents exposure of the source Excel file or Database to the end user, thereby enforcing privacy.
• Power BI Service supports an elegant system of access control by designating workspace access to a specified user group, which also gets applied to Organizational Data Types.
• User does not need to import source data again into some separate Worksheet to setup VLOOKUP to fetch values of other fields. So, the resulting excel file is light-weight with smaller size and fewer formulas in it (explained in case study).
• Organizational Data Types work seamlessly in Excel Online (browser-based Excel). User does not even require to be on an organization’s VPN to access source data. Power Query or external Excel file references require the user to be on an organization’s LAN, which is a downside.

Case Study: VLOOKUP vs Organization Data Types

Scenario:

HR of an organization is required to prepare an Excel file where we need to do some analysis for individual employees. He exports an Excel file of Employee Master from ERP and then manually copies and pastes Employee Master data from that Excel file every month. Currently he is referencing and linking to this master data using the VLOOKUP function (as per below screenshot). He is maintaining this Master worksheet in many Excel files & has to manually update it.

In the above approach, if the HR fails to update Employee Master sheet, it can lead to incorrect reporting & decision-making. Also, if any column gets added or removed from Employee Master in future, VLOOKUP function will require modification of column references manually.

As a BI Consultant what solution can you offer ?

Solution:

Power BI supports connectivity to popular ERP, Database, Excel files etc. We will simply create a dataset in Power BI, extracting this data from ERP (via Power Query transformations if required). And then, without creating any visualization, we will simply publish the dataset, setting Featured table in the modelling window of Power BI, to the desired workspace of HR. This will enable HR to view tables of Power BI datasets shared with him. Afterwards, we will simply remove VLOOKUP and replace it with cell references as demonstrated below:

Compatibility

All the things explained and demonstrated in the blog are compatible on an Office 365 version of Excel (Desktop + Web). User needs to be on Office 365 Business or Enterprise subscription. Office 365 Personal / Home subscription or perpetual editions of Office like 2013, 2016, 2019, 2021 etc. do not support all these features as they require associated domain, which is missing in these editions.

Writeback in Power BI Using Power Apps!

Since we are aware that Power BI does not directly allow us for editing or writeback, we may use Power Apps to provide this feature. We learned how to integrate Power Apps on Power BI dashboard in the first part. In this section, we’ll create the same canvas app to utilize it as a writeback option in Power BI.

So how we can writeback?

Here are steps to achieve the same

1. Go through the first part and integrate Power Apps on Power BI dashboard.
2. Open the same app in Power Apps editor by going to make.powerapps.com
3. Add same data source in Power App which we used in Power BI report.
4. Insert different dropdowns to get filter data from Power BI and from data source. Set items property of PBI dropdown to the formula shown in the below GIF.
5. Set default property of data source dropdown to the formula shown in GIF below, to get the default selected value from Power BI report.
6. Set the item property with the formula shown in GIF below to get the data from the data source and make the dropdown dependents.
7. Select the dropdowns shown in below image and set it’s allow empty selection property to true.
8. Select all the dropdowns and make visible property false (Optional as per requirement)
9. Insert edit form on screen and configure it as shown in the below GIF.
10. Save and publish the app, now the app is ready. Open the Power bi report and use the app for writeback in the power bi.

Read More:

The integration of Power Apps on Power BI dashboards blog series continues with this second post. We learned how to integrate Power Apps on a Power BI dashboard in the first section. This section demonstrated how to create same Power Apps for use as writeback in a Power BI dashboard. We’ll go over how to alter the Power Apps’ visuals and data fields provided to the app in the next section.

Microsoft Power Apps is an excellent example of a Platform as a Service (PaaS). It allows you to create mobile or tablet apps for internal business users that can run on Android, IOS, and Windows. We can now integrate Power Apps into our Power BI reports using the Power Apps visualization in the Power BI Marketplace. Integrating Power Apps on Power BI Dashboard gives you more analytical and visualization power.

Why do you need Power Apps on the Power BI dashboard?

The best option for users who need to write back in Power BI is to integrate Power Apps and use it as a writeback option.

What are the best ways to Integrate Power Apps on Power BI report?

• Use URL redirect feature
• Use Power Apps visual on the report directly
  1. Use the existing canvas app
  2. Create a new canvas app

How to integrate Power Apps on Power BI Dashboard?

1. Open Power BI Desktop and choose Data Source you want. Here I am selecting Dataverse as it is the ideal data source for Power Apps and supports direct query.
2. Select the table and click Load. If the data is formatted, click Transform Data if you want to Perform Transformation on the columns. I am directly loading the data and using Direct Query to reduce the data load time.                                 
3. Create Visuals In the Power BI report.
4. Insert Power Apps visual in Power BI report.
5. Add parameters to the visual and follow the steps outlined in the Power Apps visual.
6. Choose the environment and then create new or choose existing canvas apps.
7. It will open the Power Apps editor window in the browser, and the fields you passed in the visual are added as a data source in the canvas app.     
8. You can use those fields in the app using PowerBIIntegration.Data.
9. After creating the app, Click save and publish the canvas app.
10. Publish the report, and now you can use Power Apps on the dashboard.

Read More :

This blog is the first part of the integration of Power Apps on Power BI dashboard series. In this part, we saw how to integrate Power Apps on Power BI dashboard. In the next part, we will see how to write back in Power BI using the same canvas app.