You use business process flows for all Dynamics 365 opportunities.
Some opportunities are closed before business process flow durations are calculated.
You need to ensure that business process flow duration values are calculated.
Solution: Create a flow to update the Status Reason of the business process flow table record to "Finished" and the Status to "Inactive" when the opportunity is won.
Does the solution meet the goal?
Creating a flow to update the Status Reason of the business process flow table record to 'Finished' and the Status to 'Inactive' upon opportunity closure ensures that the business process flow is marked as complete. This triggers the calculation of duration values.
This solution explicitly handles the process completion state, which guarantees that the business process flow duration is recorded even if the opportunity is won prematurely.
You use business process flows for all Dynamics 365 opportunities. Some opportunities are closed before business process flow durations are calculated. You need lo ensure that business process flow duration values are calculated.
Solution: When closing an opportunity, use the close as won dialog without completing the business process flow. Does this meet the goal?
Using the Close as Won dialog without completing the business process flow does not ensure that the business process flow duration is calculated. The process flow needs to be marked as completed or transitioned to an inactive state for duration values to be captured.
Simply closing an opportunity as won without completing the flow may bypass the finalization of process flow metrics, hence the goal is not met in this case.
A company's IT department has a .CSV file stored on one of their Shared Documents folders within their Microsoft SharePoint sites. The data from the .CSV file is ingested into Dynamics 365 Customer Insights - Data.
The file contains a row header and columns of different types, such as quantities and prices. The file also contains some rows with a high proportion of nulls.
You need to clean and transform the data in Customer Insights - Data to be ready for unification.
Solution: Transform the first row to be used as headers, remove rows that contain null values, and name the query. Select Next and your data is now ready for unification.
Does this meet the goal?
Transforming the first row to be used as headers, removing rows with null values, and naming the query prepares the data appropriately for unification in Customer Insights - Data.
By setting the headers and removing rows with high proportions of nulls, the data becomes cleaner and more structured, which is essential for successful unification in Customer Insights. Therefore, this solution meets the goal of preparing the data for unification.
A company's IT department has a .CSV file stored on one of their Shared Documents folders within their Microsoft SharePoint sites. The data from the .CSV file is ingested into Dynamics 365 Customer Insights - Data.
The file contains a row header and columns of different types, such as quantities and prices. The file also contains some rows with a high proportion of nulls.
You need to clean and transform the data in Customer Insights - Data to be ready for unification.
Solution: Transform the first row to be used as headers, define column types to be the appropriate field types and name the query. Select Next and your data is now ready for unification.
Does this meet the goal?
While transforming the first row to be used as headers and defining column types are necessary steps, the solution does not address removing rows with high proportions of nulls. Rows with significant null values can interfere with the quality of the unification process in Customer Insights - Data.
Therefore, this solution does not fully meet the goal as it does not handle data quality issues caused by null values.
A company's IT department has a .CSV file stored on one of their Shared Documents folders within their Microsoft SharePoint sites. The data from the .CSV file is ingested into Dynamics 365 Customer Insights - Data.
The file contains a row header and columns of different types, such as quantities and prices. The file also contains some rows with a high proportion of nulls.
You need to clean and transform the data in Customer Insights - Data to be ready for unification.
Solution: Transform the first row to be used as headers. Define column types to be appropriate field types and name the query. Create a full name and full address columns by merging the appropriate columns if they exist. Select Next and your data is now ready for unification-Does this meet the goal?
This solution also includes transforming headers and defining column types, along with creating merged columns. However, it still does not remove rows with a high proportion of nulls. Addressing null values is important for data quality and ensuring accurate unification.
Without removing rows with many nulls, the data may still have integrity issues that could impact the unification process. As a result, this solution does not completely meet the goal.
