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In Dynamics, managing access to sensitive data is handled through security measures such as role-based permissions. Security roles and access management play a significant role in ensuring that the right individuals have access to the appropriate information. To grant access to particular records, users with Share permissions typically navigate to the respective record and click on the “Share” button as shown below.

A common scenario arises when a sales manager needs to grant specific record access to a salesperson who doesn’t have the necessary permissions. The changes made by the sales manager may leave the salesperson unaware of their newly granted access, potentially leading to a loss in productivity. To resolve this issue, we can use access triggers introduced in Power Automate. These triggers activate whenever there is a change in user access for a particular record.

To improve this process, we can automate it by sending access notifications through email messages. By setting up a Power Automate flow, we can establish a notification system that triggers whenever access is granted, modified, or revoked for a particular user regarding specific records.

In this blog, we will utilize the example of a sales manager granting/modifying/revoking the permission of a salesperson in a lead record. To achieve this, we will need to create three different Power Automate flows, each of which is explained below.

1. Notify the user when Access is Granted for Lead records:

The GrantAccess action is triggered whenever full access, including Read, Write, Append, Append To, Delete, Share, and Assign permissions, is granted to the user for a specific record.

Let’s see how we can create a Power Automate flow for that:

Step 1: Create a Power Automate flow and select the trigger point as “When an action is performed”. Select the Catalog and Category as All, and Table as Lead. Select the Action name as GrantAccess.

Initialize the AccessMask and leadID Variable by clicking on add action and searching for initialize variable. Set the value for each variable as shown below:

• AccessMask = triggerOutputs()?[‘body/InputParameters/PrincipalAccess/AccessMask’]
• LeadID = triggerOutputs()?[‘body/InputParameters/Target/Id’]

Step 2: Validate the above variable values and retrieve Lead, Access Modified User, and Target User Information by using the Get a Row By ID action in Microsoft Dataverse connector as shown below:

• Lead = variables(‘leadID’)
• Access Modified User = triggerOutputs()?[‘body/InputParameters/PrincipalAccess/Principal/Id’]
• Target User Information = triggerOutputs()?[‘body/RunAsSystemUserId’]

Step 3: As we have retrieved all the necessary details, we can now send an email to the user informing them that they have full access to the record, as shown below. To send the email, we are using the Outlook connector. Essentially press on “Include an activity” and look for “Send an e-mail,” as illustrated underneath.

Now, enter the To as the primary email address of the target user and the Subject and Body as shown below.

2. Notify the user when Access is Modified for Lead records:

This flow triggers whenever specific access i.e. Read, Write, etc. is granted or revoked for a particular user under specific records.

When we look at the JSON output of the trigger action from the power automate, we can see the AccessMask in JSON data, which is the addition of all access values which has been given to the user as shown below.

Following are the access values for all permissions:

• Read: 1
• Write: 2
• Append: 4
• Append To: 16
• Delete: 65536
• Share: 262144
• Assign: 524288

Let’s say if we give a user Read and Write access, the AccessMask value will be 3. To check if the user has any specific permissions, we can use a basic calculation. First, divide the AccessMask value by the permission value (either Read or Write shown above). At that point, apply the modulo operation by 2 (which is nothing but the leftover portion returned after isolating by 2). If the remainder is 1 or greater than 1, the permission is granted. If the remainder is 0, the permission is not granted.

Imagine you have a user with Read and Write access, you would get the Access Mask as 3. Here’s how you can identify if specific permissions (Read or Write) are granted to the user.

1. Access Mask Value: 3 (this means both Read and Write permissions are granted)
2. Permission Values:
   • Read: 1
   • Write: 2

To check if each permission is granted, follow these steps:

Step-by-Step Calculation

For Read Permission:

1. Divide the Access Mask Value by the Read Permission Value:

• 3÷1=3

2. Apply the Modulo Operation (remainder when divided by 2):

• 3 modulo 2=1

3. Interpret the Result:

• As the result is 1, the Read permission will be granted.

For Write Permission:

1. Divide the Access Mask Value by the Write Permission Value:

• 3÷2=1.5

2. Apply the Modulo Operation (remainder when divided by 2):

• 5 modulo 2=1.5

3. Interpret the Result:

• Since the result is greater than 1, the Write permission is granted.

So, in this case, both Read and Write permissions are granted to the user based on the calculated values. This process helps us determine whether specific access permissions are granted or not for a given Access Mask value. Let’s implement this in the Power Automate and see how we can check if the permission is granted or not.

Step 1: The triggered step and variable declaration will be the same as we have used in Step 1 of GrantAccess flow; the only change is that the action name will be ModifyAccess.

Step 2: Initialize boolean variables for all access permissions and set the respective expressions as shown below:

• Read Permission Check Equation:

IsReadPermissionGranted = if(equals(mod(div(variables(‘AccessMask’), 1), 2), 1), true, false)

• Write Permission Check Equation:

IsWritePermissionGranted = if(equals(mod(div(variables(‘AccessMask’), 2), 2), 1), true, false)

• Append Permission Check Equation:

IsAppendPermissionGranted = if(equals(mod(div(variables(‘AccessMask’), 4), 2), 1), true, false)

• Append To Permission Check Equation:

IsAppendToPermissionGranted = if(equals(mod(div(variables(‘AccessMask’), 16), 2), 1), true, false)

• Delete Permission Check Equation:

IsDeletePermissionGranted = if(equals(mod(div(variables(‘AccessMask’), 65536), 2), 1), true, false)

• Share Permission Check Equation:

IsSharePermissionGranted = if(equals(mod(div(variables(‘AccessMask’), 262144), 2), 1), true, false)

• Assign Permission Check Equation:

IsAssignPermissionGranted = if(equals(mod(div(variables(‘AccessMask’), 524288), 2), 1), true, false)

Step 3: Now we have all permission values as Boolean. We can check if the value is true, and then the access is granted; otherwise, the access is revoked as shown below:

• Read: @{if(variables(‘IsReadPermissionGranted’), ‘Granted’,’ Revoked’)}
• Write: @{if(variables(‘IsWritePermissionGranted’), ‘Granted’,’ Revoked’)}
• Append: @{if(variables(‘IsAppendPermissionGranted’), ‘Granted’,’ Revoked’)}
• Append To: @{if(variables(‘IsAppendToPermissionGranted’), ‘Granted’,’ Revoked’)}
• Delete: @{if(variables(‘IsDeletePermissionGranted’), ‘Granted’,’ Revoked’)}
• Share: @{if(variables(‘IsSharePermissionGranted’), ‘Granted’,’ Revoked’)}
• Assign: @{if(variables(‘IsAssignPermissionGranted’), ‘Granted’,’ Revoked’)}

3. Notify the user when Access is Revoked for Lead records:

This flow triggers whenever all access of the user has been removed from the specific record.

The flow is almost similar to the Grant Access flow, apart from some modifications mentioned below:

1. The Action name would be RevokeAccess
2. Retrieve the Target user record by providing the Table name as “User” and Row Id from Trigger Outputs i.e. “triggerOutputs()? [‘body/InputParameters/Revokee/Id’” as shown below
3. The email needs to be modified as shown below

Conclusion:

By following the steps for creating Power Automate flows to notify users about access changes, organizations can not only improve productivity but also strengthen security measures by maintaining clear visibility and control over data access.

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Receipt processing is a prebuilt artificial intelligence model available in Power Automate. It’s designed to analyze receipts using cutting-edge optical character recognition (OCR) technology. This model can understand both printed and handwritten text on receipts. Once it reads a receipt, it extracts important information such as the store’s name and address, the transaction’s date and time, and a detailed list of items purchased. For each item, it identifies the name, price, quantity, and total cost. Additionally, it calculates the subtotal (the total before tax), the tax amount, any tip included, and the final total spent. This helps streamline tasks like expense tracking and management.

Let’s delve deeper into this practical example for a better understanding:

At a local general store, the owners use Dynamics 365 CRM to keep customer information. However, keeping track of customers’ purchases and calculating daily profits has been difficult. They used to manually enter each purchase into a record, export it to Excel, and then figure out the daily profit. This process took a lot of time and often had mistakes.

To make things easier, they decided to use the receipt processing feature in Power Automate. Now, when a customer buys something, they just upload the receipt into the system. The receipt processing model automatically extracts important details like items bought, their prices, and the total amount spent. This data is then automatically entered into an Excel spreadsheet.

Implementing the described scenario using Power Automate

A custom entity named “Customers” has been established, containing fields for customer details such as name, phone number, and address. Additionally, a “Receipt” field has been incorporated for uploading receipt images or files.

Currently, our Power Automate flow is triggered by a modification of a row, as the receipt upload in the form can only occur after the record is created. We’ve specified the customer’s table name in the parameters.

After retrieving the record, we need to download the associated file. To accomplish this, we will add a “Download a file or an image” action immediately after the trigger action. We’ll pass the extracted record ID to this action and specify “receipt” as the column name from which the file will be downloaded.

After getting the file, we’ll use a tool to extract information from the receipts. If the document is big and has just one receipt, we’ll tell the tool which page the receipt is on. This enhances cost-effectiveness and improves operational efficiency.

Note: Suppose there are receipts on different pages: one on page 2 and another spanning pages 3 and 4. If you specify ‘2’, the tool will only gather information from the first receipt. Choosing ‘3-4’ means it will only extract info from the first page of the second receipt. If you choose ‘2-4’, it will collect details solely from the first receipt, ignoring the second one.

To enter the extracted information into an Excel sheet, we’ll link the output parameters to the columns of our created table in Excel Online. The output parameters from the Receipt extraction model include the purchased item name, quantity, price, merchant’s name, address, total amount, subtotal, tip, tax, and more. We’ll organize this data in the Excel sheet according to our table columns.

Now that our flow is set up, we’ll test it by uploading the following receipt in the customer’s entity form.

The flow was activated when a modification was made to a customer entity’s record. The information from the receipt image was successfully extracted and entered into the system according to the flow’s instructions. You can now compare the values of those entries with the details from the receipt image provided above.

Conclusion

Our flow successfully automated the process of extracting information from a receipt uploaded to a customer entity’s record. By triggering the flow upon modification of the record, we ensured that the receipt details were accurately captured and entered into the system. This streamlined the data entry process and provided a reliable way to compare the entered values with the original receipt image. Overall, the flow showcased the efficiency and precision of automating repetitive tasks with Power Automate.

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Microsoft provides Power BI Report Builder to design paginated reports within the Power BI ecosystem. Paginated reports can create highly formatted, pixel-perfect reports suitable for printing or sharing as PDFs. With Power BI Report Builder, users can design these reports with precise control over layout, formatting, and data presentation, complementing the interactive and exploratory capabilities of Power BI Desktop.

Power BI Report Builder supports various data sources, allowing users to connect to different data types to create paginated reports. Some of the commonly used data sources include Microsoft SQL Server, Analysis Services, Power BI Datasets, Azure SQL Database, Oracle, Excel, XML, and CSV/Text Files.

In this blog post, our primary emphasis will be on integrating Power BI Datasets within Power BI paginated reports. We will consider one of the use case scenarios where I have a Power BI report that displays an invoice report specific to each user. This report includes numerous calculated columns and measures. To facilitate exporting the report as a PDF, we use paginated reports. Instead of recreating all the columns and measures manually in Power BI Report Builder, we can leverage the functionality of adding a Power BI semantic model.

Demonstration: Add a Power BI Semantic Model in the Power BI Report Builder.

Step 1: Opening Power BI Report Builder

When you open Power BI Report Builder for the first time, you will be greeted with a screen where you can choose to open an existing report or start a new one.

Above the design surface is the toolbar, where you have various tabs to change the formatting and setup of your report. To the left of the design surface is the Report Data pane, which displays all the built-in fields (automatically available in any paginated report), parameters, images, data sources, and datasets included in your report.

Step 2: Creating a New Report

You can also have the option to create a report from a wizard or a blank report.

Click on the data source and select the add power bi semantic model connection.

Step 3: Selecting the Semantic Model

Select your workspace and choose the appropriate semantic model.

Step 4: Adding a Dataset

After selecting the semantic model, add a dataset. A dataset comprises the query to be executed, a list of fields with their data types to be included in the report, and a connection to a data source.

Step 5: Designing the Query

Select your data source and click on Query Designer. The Query Designer is a visual representation of the families, fields, and criteria that the query is configured to return. While designing a query, it can be tailored to return particular records based on predetermined criteria and formatting.

In the screenshot below, you can see the measures developed in Power BI. You can also add tables to the query by simply dragging them into the design area.

Step 6: Select “OK”

Now you see your new dataset with a list of its fields in the Report Data pane, along with your calculated Columns and Measures.

By following these steps, you can effectively create and customize paginated reports in Power BI Report Builder, leveraging the Power BI semantic model to incorporate rich data visualizations and insights.

Note: To utilize a Power BI semantic model with DirectQuery in Power BI Report Builder, your dataset must include a measure, even if you don’t intend to display it in your report. Without the measure, the dataset won’t provide accurate data results.

Conclusion:

This way, you will be able to use the Power BI Semantic model incorporated within Power BI Paginated Reports.

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As Microsoft is providing us more flexibility with Power Automate (MS Flow), now we can retrieve List of solution details and their components directly within Power Automate flows.

Recently, we got a requirement to retrieve the list of solutions and their components from specific environments and store this information in an Excel file on SharePoint.

We can achieve this using Solution and Solution Components entity records in Dynamics CRM.

Below are the steps to achieve this requirement.

Step 1: Initialize the Array variable

This array variable should include the details of the solution component types you want to retrieve from the solution, as shown in the screenshot below.

The above array contains the following details of the solution component types:

• ComponentType – The type value of the component from the documentation.
• EntityName – The logical name of the component type.
• FilterRowName – The field name to filter the component type.
• SelectColumn – The field name to filter the component type.

You can find all solution component types information in Microsoft Documentation

Step 2: Retrieve solutions

Add the ‘List rows from selected environment’ step to retrieve the solutions from the specific environment. Use a filter query to retrieve the visible solutions and exclude the default solution.

Step 3: Retrieve Solution Components

Now, proceed to retrieve the solution component for the previously obtained solution. Utilize an ‘Apply to Each’ step to iterate through each solution, and within that, add a ‘List rows’ action to retrieve the solution component using the filter rows.

Step 4: Filter Array step to check the Component Type

For each solution component, implement an ‘Apply to Each’ loop. Inside this loop, use a Filter Array step to check that the Component Type of the current solution component matches the type specified in the array variable we set up earlier.

Expressions from the above image:

• variables(‘SolutionCompentSummary’)
• @item()[‘ComponentType’]
• items(‘Apply_to_each_Solution_Components’)?[‘componenttype’]

Step 5: Retrieve Solution Components Summary

Add the ‘List rows’ step to retrieve the solution component if the Component Type matches and add a row into the Excel table.

Expressions from the above image:

• length(body(‘Filter_Solution_Component_Summaries’))
• body(‘Filter_Solution_Component_Summaries’)[0][‘EntityName’]
• body(‘Filter_Solution_Component_Summaries’)[0][‘FilterRowName’]

Expressions from the above image:

• items(‘Apply_to_each_Solutions’)?[‘uniquename’]
• items(‘Apply_to_each_Solutions’)?[‘createdon@OData.Community.Display.V1.FormattedValue’]
• items(‘Apply_to_each_Solutions’)?[‘version’]
• items(‘Apply_to_each_Solutions’)?[‘ismanaged@OData.Community.Display.V1.FormattedValue’]}
• outputs(‘List_of_Solution_Component_Summary’)?[‘body/value’][0]?[body(‘Filter_Solution_Component_Summaries’)[0][‘SelectColumn’]]
• concat(toUpper(take(replace(string(outputs(‘List_of_Solution_Component_Summary’)?[‘body/value’][0]?[‘@odata.type’]), ‘#Microsoft.Dynamics.CRM.’, ”), 1)), toLower(skip(replace(string(outputs(‘List_of_Solution_Component_Summary’)?[‘body/value’][0]?[‘@odata.type’]), ‘#Microsoft.Dynamics.CRM.’, ”), 1)))

Conclusion:

By following these steps, you can retrieve Solutions and their component details using Power Automate flow.

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The tools of AI Builder help you find important information in text. They organize this info into categories that make sense for your business needs.

There are two types: prebuilt and custom. Prebuilt ones are ready to use without any extra work. They’re great for lots of common tasks. Custom ones need you to train them with your data and settings. This way, you can make a model that’s perfect for what you need.

Practical Example: Making Appointments Easier with Automated Data Handling

Imagine running a small doctor’s office. Patients fill out a form on your website with their name, phone number, city, email, and when they want an appointment. With AI Builder in Power Automate, the system automatically collects and saves this info from the forms present on the website.

As patients send their forms, Power Automate quickly grabs the important details and stores them in a file or your CRM system. Later, when it’s time to schedule appointments, you can easily find the patient’s info, contact them, and sort out their appointments.”

Extract Data from Text and Input into Excel Spreadsheet

We’ll create a manual-triggered flow in Power Automate to demonstrate using AI Builder’s entity extraction model. This flow will include a Text Input parameter.

Now, next to the initial triggered action, add an AI Builder action. From the available AI Builder actions, select “Extract entities from text with the standard model.”

This action requires two parameters: the first one is the language, and the second one is the text. You can pass a file as well from the trigger action, but documents cannot exceed more than 5,000 characters.

As we proceed to input data into the Excel file, a table has been prepared with two columns: one for the type of entity and the other for its respective value. This model will provide outputs such as entity value, entity type (including person’s name, phone number, email, city, date, etc.), confidence score, character length, and starting location.

Now that the flow is set up, we can proceed with testing it. To do this, I’m passing the content of an email as input to the manually triggered action. In the email content, you’ll find information such as the patient’s name, phone number, address, and preferred date, as shown in the screenshot below.

After executing this flow, you’ll find the results in the Excel file. Unlike the key phrase extraction model, this model is more advanced as it allows you to categorize the detected text. In contrast, key phrase extraction simply extracts key phrases from the text or file. For further understanding of the key phrase extraction model and its application, you can refer to the blog titled “Understanding key phrase extraction in power automate for text analysis”.

Conclusion

We’ve successfully demonstrated the implementation of a Power Automate flow utilizing an entity extraction model. Through a practical example of appointment scheduling at a medical clinic, we showcased how this technology efficiently extracts and organizes relevant data from text inputs. By automating the extraction process, businesses can streamline their workflows, enhance data accuracy, and improve overall efficiency. With the ability to categorize extracted information, AI Builder’s entity extraction model offers a powerful solution for various text analysis tasks.

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