Working with Microsoft Dataverse Connector in Power BI is usually straightforward—until you encounter a table that simply refuses to load any rows, even though the data clearly exists in the environment. This happens especially with hidden, virtual, or system-driven tables (e.g. msdyn_businessclosure, msdyn_scheduleboardsetting) which are commonly used in Field Service and Scheduling scenarios. Before jumping to a workaround, it’s important to understand why certain Dataverse tables don’t load in Power BI, what causes this behavior, and why the standard Dataverse connector may legitimately return zero rows. Causes – 1] The Table Is a Virtual or System Table with Restricted AccessSystem-managed Dataverse tables like msdyn_businessclosure are not exposed to the Dataverse connector because they support internal scheduling and platform functions. 2] No Records Exist in the Root Business Unit Data owned by child business units is not visible to Power BI accounts associated with a different BU, resulting in zero rows returned. 3] The Table Is Not Included in the Standard Dataverse Connector Some solution-driven or non-standard tables are omitted from the Dataverse connector’s supported list, so Power BI cannot load them. Solution: Export Dataverse Data Using Power Automate + Excel Sync Since Power BI can read:-> OneDrive-hosted files-> Excel files-> SharePoint-hosted spreadsheets …a suitable workaround is to extract the restricted Dataverse table into Excel using a scheduled (When the records are few) / Dataverse triggered (When there are many records and you only want a single one, to avoid pagination) Power Automate flow. What it can do –-> Power Automate can access system-driven tables.-> Excel files in SharePoint can be refreshed by Power BI Service.-> we can bypass connector restrictions entirely.-> The method works even if entities have hidden metadata or internal platform logic. This ensures:-> Consistent refresh cycles-> Full visibility of all table rows-> No dependency on Dataverse connector limitations Use case I needed to use the Business Closures table (Dataverse entity: msdyn_businessclosure) for a few calculations and visuals in a Power BI report. However, when I imported it through the Dataverse Connector, the table consistently showed zero records, even though the data was clearly present inside Dynamics 365. There are 2 reasons possible for this –1] It is a System/Platform Tablemsdyn_businessclosure is a system-managed scheduling table, and system tables are often hidden from external connectors, causing Power BI to return no data. 2] The Table Is Not Included in “Standard Tables” Exposed to Power BIMany internal Field Service and scheduling entities are excluded from the Dataverse connector’s metadata, so Power BI cannot retrieve their rows even if they exist. So here, we would fetch the records via “Listing” in Power automate and write to an excel file to bypass the limitations that hinder the exposure of that data; without compromising on user privileges, or system roles; we can also control or filter the rows being referred directly at source before reaching PBI Report. Automation steps – 1] Select a suitable trigger to fetch the rows of that entity (Recurring or Dataverse, whichever is suitable). 2] List the rows from the entity (Sort/Filter/Select/Expand as necessary). 3] Perform a specific logic (e.g. clearing the existing rows, etc.) on the excel file where the data would be written to. 4] For each row in the Dataverse entity, select a primary key (e.g. the GUID), provide the path to the particular excel file (e.g. SharePoint -> Location -> Document Library -> File Name -> Sheet or Table in the Excel File), & assign the dynamic values of each row to the columns in the excel file. 5] Once this is done, import it to the PBI Report by using suitable Power Query Logic in the Advanced Editor as follows – -> a) Loading an Excel File from SharePoint Using Web.Contents() – Source = Excel.Workbook(Web.Contents(“https://<domain>.sharepoint.com/sites/<Location>/Business%20Closures/msdyn_businessclosures.xlsx”),null,true), What this step does: -> Uses Web.Contents() to access an Excel file stored in SharePoint Online.-> The URL points directly to the Excel file msdyn_businessclosures.xlsx inside the SharePoint site.-> Excel.Workbook() then reads the file and returns a structured object containing:All sheets, Tables, Named ranges Parameters used: null → No custom options (e.g., column detection rules)true → Indicates the file has headers (first row contains column names) -> b) Extracting a Table Named “Table1” from the Workbook – msdyn_businessclosures_Sheet = Source{[Item=”Table1″, Kind=”Table”]}[Data], This would search inside the Source object (which includes all workbook elements), and look specifically for an element where: Item = “Table1” → the name of the table in the Excel fileKind = “Table” → ensures it selects a table, not a sheet with the same name & would extract only the Data portion of that table. As a result, we get Power Query table containing the exact contents of Table1 inside the Excel workbook, to which we can further apply our logic filter, clean, etc. To conclude, when Dataverse tables refuse to load through the Power BI Dataverse Connector—especially system-driven entities like msdyn_businessclosure—the issue is usually rooted in platform-level restrictions, connector limitations, or hidden metadata. Instead of modifying these constraints, offloading the data through Power Automate → Excel → Power BI provides a controlled, reliable, and connector-independent integration path. By automating the extraction of Dataverse rows into an Excel file stored in SharePoint or OneDrive, you ensure: This method is simple to build, stable to maintain, and flexible enough to adapt to any Dataverse table -whether standard, custom, or system-managed. For scenarios where Power BI needs insights from hidden or restricted Dataverse tables, this approach remains one of the most practical and dependable solutions. I Hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudFronts.com.

Why This Matters Imagine you’ve just delivered an order, and now there’s a service issue or repair request from the customer. What’s the best way to track and resolve that? That’s where Cases come in. This blog walks you through how your company can use Cases in Dynamics 365 CRM to efficiently handle post-delivery service and repair requests—directly linked to the order fulfillment process for better visibility and control. Let’s break it down step by step. Step 1: Navigate to Cases from an Order Fulfillment Record Start by opening the Order Fulfillment record.Click on the “Related” dropdown and select “Cases” from the list. This takes you directly to all service cases related to that order. Step 2: Create a New Case Click on the “New Case” button in the Cases tab. A Quick Create: Case form appears. Here’s what you’ll see and fill in: Optional fields like Contact, Origin, Entitlement, and others can be filled in if needed.You can also include details such as First Response By, Resolve By, and Description, depending on your business requirements. Once done, hit Save and Close. Step 3: View All Related Cases After saving, you’ll see a list of all Cases associated with the order under the Case Associated View. Each entry includes key info like: This makes it easy to monitor all service activity related to an order at a glance. Step 4: Manage Case Details Click on any Case Title to open the full Case record. From here, you can: Step 5: Monitor Service Performance Navigate to Dashboards > Service and Repair to track ongoing Case performance. Here’s what you’ll see: This allows your company’s service team to monitor progress, manage workload, and identify recurring product or fulfillment issues. To conclude, by following this process, your company ensures that every post-delivery service or repair request is captured, tracked, and resolved—while keeping everything connected to the original order. It’s simple, efficient, and fully integrated into Dynamics 365 CRM. Hope this helps!!! I hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfronts.com.

Imagine this: you update a product’s purchase date in a registration record and—boom—a related case automatically gets refreshed with the accurate “Purchased From” lookup. Saves time, reduces errors, and keeps everything in sync without you lifting a finger. Let’s walk through how to make that happen using Power Automate. The goal: When a Product Registration’s cri_purchasedat field is changed, the system will retrieve the related “Purchased From” record and update any linked Case(s) with the appropriate lookup reference. Let’s break down the step-by-step process of how this is done in Power Automate. Step 1: Trigger the Flow When Purchase Date Changes Flow trigger: When a row is added, modified, or deleted (Dataverse) This setup ensures that our flow only fires when that specific date field is modified. Step 2: Pull in the “Purchased From” Record Next, use List rows on the “Purchased From” table with a FetchXML query. We’re searching for a record whose name matches the updated cri_purchasedat. Set Row Count to 1, since we expect only one match. 3. Identify Any Linked Case Records Add another List rows action, this time on the Cases table. We look for records where cri_productregistrationid equals the current product registration’s ID:We now use the List Rows action to fetch all related Case records tied to the updated Product Registration. This time we’re targeting the Cases table (which is internally incident in Dataverse) and using a FetchXML query to match records where cri_productregistrationid equals the current record being modified. This step is critical because it gives us the list of Case records we need to update, based on the link with the modified product registration. <fetch> <entity name=”incident”>     <attribute name=”incidentid” />     <attribute name=”title” />     <attribute name=”cf_actualpurchasedfrom” />     <filter>       <condition attribute=”cri_productregistrationid” operator=”eq” value=”@{triggerOutputs()?[‘body/cri_productregistrationid’]}” />     </filter>  </entity></fetch> 5. Before updating anything, we add a Condition control to ensure that our previously fetched Purchased From record exists and is unique. Why? Because if there’s no match (or multiple matches), we don’t want to update the Cases blindly. We check if this length equals 1. If true → move forward with updates.If false → stop or handle the exception accordingly. To conclude, this kind of validation builds guardrails into your automation, making it more robust and preventing incorrect data from being applied across multiple records. After confirming a valid match, the flow loops through each related Case and updates the “Actual Purchased From” field with the correct value from the matched record, ensuring accurate linkage based on the latest update. Once this step runs, your staging automation is complete—with Cases now intelligently updated in real-time based on Product Registration changes. We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfonts.com.

In today’s world, AI is no longer a luxury—it’s a necessity for driving smarter decisions, faster innovation, and personalized experiences. We have come up with our requirements for AI to support the conversion from MQL (Marketing Qualified Lead) to SQL (Sales Qualified Lead). How did the idea originate? In our organization, whenever a prospect reaches out to us, we search for company information like company size, revenue, location, industry type, contact person details, designation, decision-maker, and LinkedIn profile. This information helps the Sales team prepare better and deliver a stronger pitch by understanding the customer before the call. Also, during the MQL to SQL stage, we look for things like: This information helps the Sales team convert the prospect into a client and increases our chances of winning the deal. Earlier, this entire process was manual and time-consuming. So, we decided to automate it with an AI agent that can gather this information for us in just a few minutes. Implementation approach After the project was approved internally, we started exploring how to make it happen. Initially, we didn’t know where or how to start. During our research, we came across Copilot Studio, which allows us to build custom agents from scratch based on our needs. We learned about Copilot Studio’s and began building our agent. We named it Elevator Pitch. Version 1 Highlights: This feedback led to the idea for Version 2, which would automate more steps and also pull information from the internet. Version 2 Enhancements: Version 2 Features: Company & Contact information with a single click on MQL to SQL, the agent now generates the document within minutes—something that earlier used to take hours or even a full day. Live demo in Zurich & New York On 22nd May 2025, we had an event scheduled at the Microsoft office in Zurich with one of our clients, where we shared the Buchi journey with CloudFronts. We discussed how we collaborated to connect their multiple systems and prepared their data for insights and AI initiatives. At the same event, we had the opportunity to demonstrate our Smart Pitch product, which caught the audience’s attention. It was a proud moment for us to showcase our first AI product at the Microsoft office—delivered within just a few months of hard work. Our second opportunity came on 06 June 2025 in New York, at the AI Community Conference, where we presented again in front of a global audience. What Next in Version 3: So far, we have built this solution using Microsoft’s inbuilt Knowledge Center, ChatGPT API, SharePoint, company websites, and Dataverse. Since we were working with both structured and unstructured data, we faced some inconsistencies and performance issues. This led us to reflect and identify the need for Version 3 (V3), which will include: The development of Smart Pitch V3 is currently in progress. We’ll share our thoughts once it goes live. A demo video has also been shared, so you can see how smart and fast our Agent is at delivering useful insights. Delivering Answers in Minutes—Thanks to Smart Pitch I’d also like to share a quick story. One day, our Practice Manager was on leave, and we received a prospect inquiry about Project Operations to Business Central (PO-BC) pricing. I wasn’t sure where that information was stored, and suddenly our CEO asked me for the details. I was a bit stressed, unsure where to search or how to respond. Then I decided to ask our Smart Pitch agent the same question. To my surprise, the agent quickly gave me the exact information I needed. It was a big relief, and I was able to share the details with our CEO in just a few minutes—without even knowing where the document was uploaded. We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfonts.com.

Managing reminders and deadlines can be tricky, especially when you’re juggling multiple tasks in Dynamics 365. But what if you could set up automatic email reminders based on specific dates? In this guide, I’ll show you how to use Power Automate with D365 CRM to send automatic email reminders when certain dates are entered, and follow up at 7, 14, 21, and 28-day intervals if another related date field isn’t filled. By the end of this post, you’ll learn how to create a simple workflow that keeps your team on track by sending timely reminders when needed. The Use-Case: Automatic Email Reminders for Unfilled Dates Imagine this: Your Project Manager fills in a date for a project milestone. But if the next milestone isn’t updated after a set period, your system will automatically send reminder emails to the right people. This saves your team from having to manually follow up and ensures that important dates are never overlooked. Here’s how you can set it up using Power Automate: Key Components of the Solution Follow the Power Automate step outlined below: Select the “When a row is added, modified or deleted” trigger from the Dataverse connector, set the Change Type to Modified, choose the Order Fulfillments table, set the Scope to Organization, specify the columns cf_submittalprotocolprocess and cf_initialshopdrawingssubmitted to trigger only on changes to those fields, and optionally use the Filter Rows field to apply additional conditions if needed. Click the ellipsis (three dots) on the top-right corner of the trigger card, select Settings, scroll to the Trigger Conditions section, and enter the following expression to ensure the flow only triggers when cf_initialshopdrawingssubmitted is not empty: Below is the condition that you need to add: @and(     not(empty(triggerOutputs()?[‘body/cf_initialshopdrawingssubmitted’])),     equals(triggerOutputs()?[‘body/cf_submittalprotocolprocess’],979570001) ) Add the “Get a row by ID” action from the Dataverse connector, set the Table Name to Opportunities, and use the dynamic value Opportunity (Value) for the Row ID to retrieve the corresponding Opportunity record related to the modified Order Fulfillment. Add the “Get a row by ID” action from the Dataverse connector, set the Table Name to Order Fulfillments, and use the dynamic value Order Fulfillment for the Row ID to retrieve full details of the modified Order Fulfillment record. Add a “Compose” action named Comments for Email, and provide a formatted list including Project Manager (Order Fulfillment), Opportunity Contact, Secondary Contact, a static email (e.g., testblog@gmail.com), and again Project Manager (Order Fulfillment) as an example. Add a “Filter array” action, set the From field to a coalesce(…) expression generating a list of participants, and in Basic Mode set the condition to Item is not equal to null to remove null entries. Use a coalesce(createArray(…)) expression to conditionally construct an array of activity parties based on field availability (Opportunity Contact, Secondary Contact, Owner ID, Project Manager), falling back to a default address (CRMAdmin@gmail.com) if the Project Manager is null. coalesce(     createArray(         if(             not(equals(outputs(‘Get_Opportunity_by_ID’)?[‘body/_cf_opportunitycontact_value’], null)),             json(concat(                 ‘{“participationtypemask”: 2,”partyid@odata.bind”: “contacts(‘, outputs(‘Get_Opportunity_by_ID’)?[‘body/_cf_opportunitycontact_value’], ‘)”}’             )),             null         ),         if(             not(equals(triggerOutputs()?[‘body/_ow_secondarycontact_value’], null)),             json(concat(                 ‘{“participationtypemask”: 2,”partyid@odata.bind”: “contacts(‘, triggerOutputs()?[‘body/_ow_secondarycontact_value’], ‘)”}’             )),             Null ),         if(             not(equals(triggerOutputs()?[‘body/_ownerid_value’], null)),             json(concat(                 ‘{“participationtypemask”: 3,”addressused”: “testblog@gmail.com”}’             )),             null         ),         json(concat(             ‘{“participationtypemask”: 4,”partyid@odata.bind”: “systemusers(‘, outputs(‘Getting_Order_Fulfillment_by_ID’)?[‘body/_cf_projectmanager_value’], ‘)”}’         )),         if(             not(equals(outputs(‘Getting_Order_Fulfillment_by_ID’)?[‘body/_cf_projectmanager_value’], null)),             json(concat(                 ‘{“participationtypemask”: 1,”partyid@odata.bind”: “systemusers(‘, outputs(‘Getting_Order_Fulfillment_by_ID’)?[‘body/_cf_projectmanager_value’], ‘)”}’             )),             json(concat(                 ‘{“participationtypemask”: 1,”addressused”: “CRMAdmin@gmail.com”}’             ))         )    )) Switch to Advanced Mode in the Filter array and use the expression @not(equals(item(), null)) for better control over null filtering of the dynamic participant list. In the step below, I used a “Compose” action to extract the Project Manager’s ID, which is then used in the filter array step. Below is the body of the Filter Array, which I’ve saved in a new Compose action named “Email Participants.” Add a “Compose” action to generate the email body using HTML formatting, apply an if() expression to dynamically insert the recipient’s name, and use concat() to list the required items for fabrication. Below is the expression I used to identify all the recipients who will be receiving the emails. if(   not(equals(outputs(‘Get_Opportunity_by_ID’)?[‘body/_cf_opportunitycontact_value@OData.Community.Display.V1.FormattedValue’], null)),    concat(‘<strong>’, outputs(‘Get_Opportunity_by_ID’)?[‘body/_cf_opportunitycontact_value@OData.Community.Display.V1.FormattedValue’], ‘</strong>’),    null After completing the previous steps, add a parallel branch with four parallel actions, each configured to send the email after a delay of 7, 14, 21, and 28 days, respectively. After introducing a 7-day delay, add a parallel branch that retrieves the corresponding Order Fulfillment record by ID and checks if both the Drawing Approval Date and Redline Issued Date fields updates have been made since the initial trigger Add the “Add a new row” action from the Dataverse connector, set the Table Name to Email Messages, populate the Activity Parties field using the dynamic output outputs(‘Email_Participants’), and map the Description field similarly with the appropriate output value containing the email body content. Set the Regarding (Order Fulfillments) field in the “Add a new row” action to the dynamic value Order Fulfilment (cf_orderfulfillment) to associate the email with the corresponding Order Fulfillment record. This is how the final power automate will look like Similarly, you can do for 14,21 and 28 days. Below is how the email would look like in CRM: The trigger will start when Initial shop drawings field contains the date field and Submittal/protocol process equals certain option. If both Redline Int Received and Drawing Approved Date fields remain empty after 7, 14, 21, and 28 days, CRM will automatically send a follow-up email on each of those days. This is how the email will look like. To conclude, setting up this automatic reminder system in Power Automate for D365 CRM will help your team stay on top of project milestones, reduce manual follow-ups, and make sure nothing gets overlooked. It’s a simple yet effective way to automate reminders and keep everyone informed without any extra effort. Hope this helps!!! We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfonts.com

Microsoft Dataverse is a powerful data platform that supports secure and scalable data storage for business applications. However, raw data imported into Dataverse often needs transformation—cleaning, reshaping, filtering, or merging—to make it useful and reliable for apps and analytics.  In this blog, we’ll show you how to apply transformations to data before or after it reaches Dataverse using tools like Power Query, Dataflows, and business rules—ensuring you always work with clean, structured, and actionable data.  What is Data Transformation in Dataverse?  Why Data Transformation Matters Data transformation refers to modifying data’s structure, content, or format before or after it’s stored in Dataverse. This includes:  Step-by-Step Guide: Connecting a Database to Dataverse  Step 1: Open the Power Apps and select the proper Environment  Step 2: Open Dataflow in Power Apps and create a new Dataflow  Step 3: Connect to the Database using SQL Server Database.  Step 4: Add the required credentials to make the connection between the database and Dataverse.  Step 5: Add the transformation in the Dataverse  Step 6: Add proper mapping of the column and find the unique ID of the table   Step 7: Set the schedule refresh and publish the Dataflow.  Step 8: Once Dataflow is published, we can see the table in the Power apps  To conclude, transforming data in Dataverse is key to building reliable and high-performing applications. Whether using Power Query, calculated columns, or Power Automate, you can ensure your data is clean, structured, and actionable.  We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfonts.com. Ready to improve your Dataverse data quality? Start with a simple dataflow or calculated column today, and empower your business applications with better, transformed data.

Modern applications demand seamless, real-time data access. Microsoft Dataverse—the data backbone of the Power Platform—makes it easier to build and scale low-code apps, but often your enterprise data resides in legacy databases.  Connecting a database to Dataverse enables automation, reporting, and app-building capabilities using the Power Platform’s ecosystem. In this blog, we’ll walk you through how to connect a traditional SQL database (Azure SQL or On-Premises) to Microsoft Dataverse.  What is Dataverse?  Dataverse is Microsoft’s cloud-based data platform, designed to securely store and manage data used by business applications. It’s highly integrated with Power Apps, Power Automate, and Dynamics 365.  Key Features:  Why Connect Your Database to Dataverse?  Step-by-Step Guide: Connecting a Database to Dataverse  Step 1: Open the Power Apps and select the proper Environment  Step 2: Open Dataflow in Power Apps and create a new Dataflow  Step 3: Connect to the Database using SQL Server Database.  Step 4: Add the required credentials to make the connection between the database and Dataverse.  Step 5: Add proper mapping of the column and find the unique ID of the table   Step 6: Set the schedule refresh and publish the Dataflow.  Step 7: Once Dataflow is published, we can see the table in the Power apps  To conclude, connecting your database to Dataverse amplifies the power of your data, enabling app development, automation, and reporting within a unified ecosystem. Whether you need real-time access or periodic data sync, Microsoft offers flexible and secure methods to integrate databases with Dataverse.  Start exploring virtual tables or dataflows today to bridge the gap between your existing databases and the Power Platform. Want to learn more? Check out our related guides on Dataverse best practices and virtual table optimization. We hope you found this blog useful. If you would like to discuss anything further, please reach out to us at transform@cloudfonts.com.

Efficient data cleaning is a crucial step in any data preparation process, and Power Query makes it easy to handle common tasks like trimming and removing unnecessary spaces with functions that you can apply across multiple columns and queries at once. By creating and invoking a function, you can quickly trim and remove spaces from all the columns & tables you need, saving time and effort. In this blog, we’ll show you how to use Power Query functions to streamline your data-cleaning process. The power query we are going to use to trim text in columns is – (text as text, optional char_to_trim as text) =>letchar = if char_to_trim = null then ” ” else char_to_trim,split = Text.Split(text, char),removeblanks = List.Select(split, each _ <> “”),result=Text.Combine(removeblanks, char)inresult This Power Query function takes text as input and removes extra spaces or a specified character from a text string. It splits the text into parts, filters out empty strings, and recombines the cleaned parts using the specified character. If no character is provided, it defaults to removing spaces. The power query we are going to use to remove spaces from the text is – (InputTxt as text) => let Clendata = Text.Combine(List.Select(Text.Split(Text.Trim(InputTxt),” “),each _ <> “”),“”) in Clendata The Power Query function removes all spaces from a given text string. It trims the input, splits it by spaces, filters out blanks, and then combines the parts into a single string. The result is a clean, space-free text, ideal for standardized data preparation. Now, we have our power query function ready, we can use this function across multiple columns or dataset. To do so, go to Add Column > Invoke Custom Function > Your Power Query Function. To conclude, Cleaning and transforming data in Power Query become much easier and more efficient with the use of custom functions. Whether you need to remove spaces, clean multiple columns, or standardize text, these functions save time and ensure consistency across your dataset. By applying these techniques, you can handle large, messy datasets with ease, making your data ready for analysis or reporting. Start implementing these simple yet powerful methods today to streamline your data preparation process! We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfonts.com.

Uploading and managing quotes efficiently is crucial for Dynamics 365 CRM users. However, sometimes you may want to restrict certain buttons, such as the ‘+New’ Quote button, to only users with specific roles, like the “System Administrator.” In this guide, I’ll walk you through how to achieve this by leveraging JavaScript and the Ribbon Workbench tool in Dynamics 365. This method allows administrators to control button visibility based on user roles, ensuring that only users with the correct permissions can access sensitive functionality. The Use-Case: Restricting Access to the ‘+New’ Quote Button for Non-Administrators. Imagine a scenario where your organization needs to ensure that only users with a “System Administrator” role can create new quotes in Dynamics 365. This is crucial for maintaining control over who can initiate important processes within your CRM system. Using JavaScript and Ribbon Workbench, you can easily customize the UI to hide the ‘+New’ Quote button for non-administrators. Here’s how this use case can be implemented: In this scenario, your team wants to ensure that only system administrators have access to the “+New” button for creating quotes in the system. For non-administrators, the button will be hidden from both the homepage subgrid and the main quote tab to prevent unauthorized users from creating quotes. By using the Ribbon Workbench tool, a custom JavaScript function is created to check if a user has the “System Administrator” role. If they do, the “+New” button remains visible, and they can create a new quote. For all other users, the button is hidden. Key Components of the Solution 1. Ribbon Workbench: The Ribbon Workbench tool allows you to customize the Dynamics 365 ribbon, enabling you to create custom buttons and define their visibility and actions. It is used to create the new custom “+New” Quote button, which replaces the default button while maintaining system integrity. 2. JavaScript Customization:  Custom JavaScript is used to manage role-based access for the “+New” Quote button. The script checks the user’s role within Dynamics 365 to ensure that only users with the “System Administrator” role can view and use the button. This helps enforce security and restricts unauthorized users from creating new quotes. 3. Enable Rule for Button Visibility:   An Enable Rule is set to control the visibility of the custom “+New” Quote button based on the user’s role. It ensures that only users with the “System Administrator” role can see and use the button, while hiding it for other users. 4. Custom Button Action (Command): The command linked to the custom “+New” button triggers a custom action (JavaScript function) to open the quote form. This ensures that the action associated with the button aligns with the business needs and provides a seamless user experience for administrators. Step-by-Step Process Sign in to Dynamics 365 using your URL, such as abc.dynamics.com, and enter your credentials or login to make.powerapps.com Create a solution and add the web resource. Once it’s done login to ribbon workbench from XRM toolbox and connect to your organization. After logging in, it is recommended to create a new solution for Ribbon Workbench in Dynamics 365. Ensure that no forms, views, charts, or other entities are included, as Ribbon Workbench may fail to upload the solution with excessive data. Only include the Quote entity with no additional dependencies. Ensure the existing +New Quote button is hidden, as modifying Microsoft-standard buttons is not recommended. Instead, create a new custom button and implement the functionality for creating a new quote Form using custom JavaScript. I have provided the code for this functionality as well. Ensure that the existing +New button for quote would be hidden from the homepage Subgrid and the quotes main tab. Next step would be to create a enable rule. Enable rule is used to control the visibility and availability of a button or command of the button. Name the id of your choice but make sure to add the suffix Enable Rule. Here, un-customised is set to False. By setting isCore (or Un customized) to false, you’re indicating that the button or element is a custom component, not part of the out-of-the-box (core) solution provided by Microsoft. This helps differentiate custom actions from the default ones in the system. Below is the code for the new quote form create and user role-based code. Make sure to select the Function name properly. After setting the enable rule, go to the Commands section in Ribbon Workbench and rename the command. A command defines the action triggered by a button click. Since this is a new button, you’ll need to add the custom form opening code. Below is the function for creating the form. Final Steps: Once the command is added, don’t forget to add the Enable rule that you have created above. Once the command is added, make sure to add all the rules we wrote into the custom button. The image also needs to be added so that the icon can be visible. My custom +New icon looks like this. Testing: Once everything is done, make sure Publish the changes. You can now try to log in from the user that has no System administrator role. Once logged in, you can see that button is not visible. Button will be only visible to user that have system Administrator role. User having no System Administrator role. You can see below that there’s no +New button displayed. To conclude, by following this guide, you can efficiently control the visibility of the ‘+New’ Quote button in Dynamics 365 CRM, making it accessible only to users with the “System Administrator” role. This ensures better control over who can create quotes in the system while maintaining the flexibility of user roles. We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfonts.com.