(Beta) Updating the Services Forecast Live Dataset On Demand
The Services Forecast Live dataset includes information about revenue, costs, and margin in PSA and is used as a basis for creating services forecast plans.
You must have the appropriate permissions to update the Services Forecast Live dataset or create a scheduled job to update it. For more information, see Setting up the Services Forecast Live Dataset.
To update the Services Forecast Live dataset now:
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Do one of the following:
- From the PSA Administration tab, scroll down to the Services Forecasting section.
- From the Services Forecast Setup tab, open the active services forecast setup record. If one does not already exist, you must create one.
- Click Manage Services Forecast Live Dataset. If the button is not available on your page layout, you must add it manually.
- Select the destination app for the dataset. For data security purposes, we recommend you select your PS Cloud Core Analytics app. This step is necessary only the first time you run a job to update the dataset. Skip it for all subsequent updates.
- [Optional] Select the Full Dataset Update on Run Now checkbox, if you want to run a full dataset update. For more information see, Running a Full Dataset Update.
- Click Run Now.
To check the progress of the RFQ_ForecastTypesToCSVProcessor or CFQ_ForecastTypesToCSVProcessor jobs, go to the Apex Jobs page. For more information, see the Salesforce Help.
You can use the Salesforce Data Manager app that is available from the App Launcher to check when the process is complete. For more information on using the Data Manager app, see the Salesforce Help.
Running a Full Dataset Update
When updating the dataset on demand you can run a full dataset update by selecting the Full Dataset Update on Run Now checkbox. This replaces all existing data with the latest data instead of only updating the fields that have changed since the last update.
This option is useful for:
- Ensuring data integrity and recovery in case of errors or missed updates.
- Incorporating schema changes.
- Preventing partial or missed records from persisting in the dataset.
- Resetting and validating data in an effective way when investigating update or reporting issues.
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