Data task automation

  • 4 minutes

Data task automation in finance and operations apps lets you repeat many types of data tasks and validate the outcome of each task. Data task automation is useful for projects that are in the implementation phase.

For example, you can automate the creation and configuration of data projects. You can also configure and activate the running of import/export operations, such as the setup of demo data, golden configuration data, and other tasks that are related to data migration. You can also create automated testing of data entities by using task outcome validation.

We recommend the following approach for data task automation:

  1. Identify the data-related tasks that will benefit from automation - Implementation teams should review their configuration management plan and data migration plan to identify potential data tasks for automation and also to identify data entity test cases.

  2. Define tasks - Tasks are defined in an XML manifest. You can keep your manifest under source control as part of configuration management in your application lifecycle management (ALM) strategy.

  3. Put the data packages that are related to data task automation in the Shared asset library of Lifecycle Services - You can also use a specific Lifecycle Services project as you require. The Data task automation manager can consume packages from any sandbox and/or production environment that is related to the Lifecycle Services project.

    Note

    The user account that runs Data task automation manager in finance and operations apps must have access to Lifecycle Services and to the Lifecycle Services project that is referenced in the manifest for data packages.

    Although data task automation can be run in any environment in the cloud, we recommend that you not run any import/export tasks that use integration application programming interfaces (APIs) in a production environment. Data task automation that involves integration APIs should be used only for automated testing.

  4. Run the data tasks and then review the outcomes - Data task automation manager provides the success or failure outcome for each task. It also provides insights into the reason why a task failed.

For more information, refer to Data task automation.