Lifecycle Management with SAP Analytics Cloud

To cover the costs of software infrastructure and operation, you need to get a paid license for a non-productive SAP Analytics Cloud system.

There are two types of test systems:

Once the SAP Analytics Cloud systems are in place, start your deployment by setting up roles, users, and teams in your test system.

To get started, see Security Concepts and Best Practice.

1. Set Up Roles
   • Each SAP Analytics Cloud system comes with standard roles such as Admin, Content Creator, and Viewer.
   • Along with this, it is recommended to have your own custom roles in your test system. You can assign multiple roles to each user or team. They’ll get the combined permissions from all their roles (that is, the union of the roles).
2. Create test and development users
   • Set up users manually in your test system, or upload a CSV file with the user data.
   • You can also manage your users and teams with the User and Team Provisioning API.
     Note

     • Do not create your business users in the test system since it has limited user licenses.
     • For more information, see the Choose Test Systems.
3. Set Up SSO
   • To make your users log in with single sign on (SSO), you need to set it up on each system. It is also recommended to set up SSO for connections to live data sources.
4. Create Teams
   • Teams let you share files and assign roles to groups of users, instead of individual users.
   • For example, you can create two separate teams for each project, one for content creators and one for viewers.
   • Follow these instructions to set teams up manually, or use the API.
     Note

     • Include the team’s name in the description field to make sharing easier.
     • With SSO enabled, you can automatically assign users to teams based on SAML attributes.
     • Avoid using team folders for lifecycle management, as you can’t export content in these folders.
5. Create folders and set permissions
   • Folder structures help you organize your content and restrict access to it.
   • For example, you can create content in the private My Files folder on your test system, and then save it to a folder within the Public folder before exporting it.
   • You can use sharing settings to restrict access to this folder and any files saved within it, which is quicker than managing access to individual files.
     Note

     • Keep your folder structure simple to make navigation quicker and easier for your business users.
     • For example, create one folder for each project or line of business.
     • To apply folder-level permissions to your models, save them in their project folders instead of saving them all together in the Models folder. For the best practices, see SAP Analytics Cloud - Security Concepts and Best Practices.
     • When you export a file, its folder structure will be added to the export automatically. Configure sharing settings again based on how you export it.

Once you’ve set up your users, teams, and other security configurations on your test system, you’re ready to create some content. There’s a few steps involved to create powerful analytics from your data with SAP Analytics Cloud.

1. Create Connections:

   Create connections to data sources in the test system.

   You can connect to test data and change the connections after you import the data to the production system. There are two types of connections:
   • Live data connections
   • Import data connections
   Connections aren’t saved in the folder system, so you’ll need to set up security on the connection itself. It is recommended to set up SSO for live connections. This way, the authorizations from the data source are applied in SAP Analytics Cloud.

   For import data connections, share with the users who need to create models based on them. For more information on setting up both data connections, see Data Connections.

2. Create Models:

   Build your models in the test system within the My Files folder, and then save them to a restricted folder in the Public folder. Then, set up security for models using sharing settings. Also set up security for the data within the model, if needed.

   For models based on live data connections, you can let the source system determine each user’s access level. For import data models, you can use model privacy to grant access by role to all model data, or to segments of it.

   You can also use Data Access Control on one or more dimensions to grant read and write access to individual users or teams. If you use both Data Access Control and model privacy, users must meet both conditions to view data. For more details, see Model and Version Security.

3. Create Stories, Story Templates, Planning Processes, Analytic Applications, Digital Boardroom presentations, or any other content:

   You can now get started with creating rest of your content. As with models, save files in the My Files area first, and then to a folder in the Public folder once you’re ready to export them.

   To get started with a basic story, see Creating Your First Story. To get started with prebuilt stories, create a few story templates. Your users can create their own stories based on these templates to keep a consistent look and feel.

   For more details, see Video: Save Stories as Templates. After the stories are ready, you can organize story pages to create Digital Boardroom presentations. To see a complete presentation, users will need to have permissions for the story pages as well as the presentation itself.

Once you’re ready to start promoting content to your production system, you can choose whether to use local .tgz files or transfer over the cloud using the content network. To keep your content organized, available, and secured by appropriate permissions, we recommend using the content network.

• To get an overview of both tools, see Transporting Content (Lifecycle Management)
• For detailed steps to export to the content network, see Transport Your Private Content Through Content Network Storage.
• For steps to export to a .tgz file, see Transport Your Private Content Through File System.

  Either way, the workflow is the same.

  Note

  • Sharing settings are included with .tgz exports, but not with the content network exports.
  • You need to maintain and share access for the lifecycle permission to access the content network and export packages from it. You need to have read access to export and import .tgz files. For more details, see Permissions.
  • By default, each SAP Analytics Cloud system can share up to 300 MB on the content network.
  • To import from the content network, your system should be on the same version of SAP Analytics Cloud as the exporting system, or later.
  • The .tgz files are only compatible with the same version of SAP Analytics Cloud, or with the subsequent release, so don’t wait too long before importing your content.

  Here are the various content sources to export:

  • Export roles:

    Start by exporting a package with your custom roles.

  • Export connections:

    If you prefer, you can create the connections again in your production system instead.

  • Export remaining content

    Start by adding business user content such as stories and Digital Boardroom presentations to your exports. Dependent content such as folders, models, and dimensions will be included as well.

    If you’re using the Content Network, you can choose whether to include data for an import data model. If the model has a lot of data, you can exclude it to speed up the import process and save storage space.

    If you’re exporting to a file, we recommend leaving the data out and refreshing it in the production system to keep the file size down. To do this, deselect the Include Data checkbox.

Once you have created and exported content from your fully configured test system, you can import this content in your production system.

• To get familiar with the process for importing from the Content Network, see Transporting Content (Lifecycle Management).

• Ensure that you look in the My Content folder to see privately shared content.

• To learn about importing from .tgz files, see Transport Your Private Content Through File System.

Once you’ve finished this step, your content is ready for productive use.

When you’re ready to create new content or make updates, verify it in the test system first. Then, import the content to the production system.

If the connections already exist in the production system, you can exclude them from the export. When importing updated content, note that two versions of the same object can’t exist on an SAP Analytics Cloud system.

The content network import workflow identifies any overlapping content. You can choose not to import the overlapping content, or you can overwrite the existing version of the content.

For more details on the overwrite options, see,

• Content Administration
• Transporting Your Content Through File System

You can also use collaboration features to inform users about the new version.

To get a quicker start on your analytics and planning scenarios, you can import business content packages from the content network.

If you plan on importing business content, it’s best to set up three SAP Analytics Cloud systems based on different phases of a product lifecycle:

• Explore Phase:

  Use separate system to import business content and verify if it meets your needs. You can try the content with sample data or connect live data models to your data sources. If the content matches your needs, choose the objects that you want to promote to the test system for further development.

• Development Phase:

  During this phase, you import the business content to your test system and connect it to your data source. Now you can change the models, stories, processes, and presentations to meet your needs.

  Note

  • Objects in the SAP Analytics Cloud have a unique identifier that remains the same when content is imported to a new system. When you import an object with a conflicting ID, you need to discard one of the versions of the object.
  • If your test system already has an earlier version of business content, you’ll see a warning during the import process.
  • You can choose whether to overwrite the existing objects or discard the conflicting objects from the import.
  • In case if you want to copy the existing content before overwriting it to make sure that you don’t lose any data or work.
  • You can also use another system in this phase to test your changes to the business content, if needed.
• Production Phase:

  When the content is ready, you can include it in your main lifecycle management workflow: import the content to production, set up permissions, and connect it to your production data sources.