This blog covers some of the latest new features and enhancements in SAP Analytics Cloud and SAP Digital Boardroom release 2019.07. Please note that the Analytics Cloud Help documentation is updated at the same time as the upgrade to this release, so the links here may not yet reflect what is described below until after the upgrade is complete.
Upgrade your SAP Analytics Cloud agent to the latest agent version 1.0.223 to take advantage of all the data acquisition types!
If you haven’t upgraded yet, here are the data acquisition minimum requirements:
- Version 1.0.85 for SAP Business Warehouse (BW)
- Version 1.0.99 for SAP ERP / SQL databases
- Version 1.0.91 for SAP Universe (UNX)
- Version 1.0.75 for all other data source types
For more information, see System Requirements and Technical Prerequisites.
Highlights of this release:
- Planning: Process loop statements only against booked cells
- Planning: Append values to existing data rather than overwrite data
- Planning: Right click for context menu
- Planning: ‘Member-on-the-fly’ and ‘Table new line’ features are now combined
- Planning: Specify booking account for allocation steps
- Planning: Set Data Locks within a story
- Data Integration: SAP BW import enhancements
- Data Integration: Import data from Dow Jones articles
- Data Integration: Schedule jobs adjust for Daylight Savings Time
- Data Integration: Area name geo enrichment model enhancements
- Data Integration: Reflect query structure from BW query
- Data Integration: oAuth support for BPC connections
- Intelligent Enterprise: Support for tuple filters in SAP BW queries
- Administration & Infrastructure: Models and Points of Interest moved to Files area
- Administration & Infrastructure: Migration of model permissions from roles to teams and sharing settings
- Administration & Infrastructure: Changes to roles during migration
- Administration & Infrastructure: Editing model’s sharing settings after migration
- Administration & Infrastructure: Analytics Hub – Include assets in existing administrative download/upload
- Mobile: Support for AirWatch SDK integrated authentication
- Smart Predict: Publish predictive model to an existing PAi scenario
- Smart Predict: Variables with no influence are no longer displayed
- Data Visualizations: Support for dynamic HANA input parameters
- Data Visualizations: Removed member count limit on input controls based on flat dimensions
- Data Visualizations: Sharing private and global bookmarks
- Data Visualizations: Exporting story content in a merged PDF
- Data Visualizations: Performance improvement when filtering across models
Learn with our latest video tutorials
- Create a model from SuccessFactors Workforce Analytics
- Apply filters to a chart in a story
- Apply filters to a geo map in a story
- Full tutorial playlist >
No more unnecessary looping! Using FOREACH.BOOKED, you can now choose to repeat statements only against booked cells.
The quick action menu for table cells has been updated so when you select a table cell, you can easily bring up the context menu with a right click.
In your table, you can now combine creating new members in dimensions with creating a new line for booking the data.
When you are creating an allocation step, you can specify a Booking Account. If a Booking Account is set, all results calculated when executing the allocation step will be booked to that account instead of the original accounts. This holds for both allocated values and eliminations. This can be combined with the options: Keep Source and Overwrite Target.
You can now set Data Locks on a table from within a story. The initial drill state of the data locking editor is given by the selection in the story.
During data wrangling, you can now join up to 10 datasets. As well, we now support Tabs as a delimiter for CSV files.
With your subscription to Dow Jones, access full datasets directly from SAC! If you want to try out the data, there is a built-in trial key that gives you access to a sample of the dataset. Note that only metadata is retrieved. Connection queries are asynchronous and can be refreshed. To ensure the best performance, it is recommended to add proper filters to your query if the underlying table contains over 2 billion rows.
If you are in a region that encounters Daylight Savings Time (DST), you now won’t need to worry whether to “spring forward” or “fall back” with your SAC clocks! For import and export jobs, the schedule timing now adjusts for DST according to the time zone you select. When creating a new schedule, it will default to the time zone of the browser. For existing jobs, you will have to open the models and set the DST.
Geo enriching by area now supports enriching all countries, regions, and subregions. Enriching is possible if the country data can be provided as ISO3 and ISO2 codes, or as full country names in English. Region and subregion must be in English as well. Geo-enriching at the country level is mandatory for imported data or data inputted by the user.
Boost your productivity when you create BW models! Uploaded BW data now respects the model structure. The wrangling session will have inherited modeling structure from the BW source that includes dimensions, attributes, attribute data types, and hierarchies. Please note that you’ll need SAP Analytics Cloud agent version 1.0.223 or later.
BPC connections now support oAuth tokens. With the combination of oAuth and SAML, you won’t need to re-enter your credentials. When compared to basic authentication, oAuth provides stronger security. With oAuth support, BPC user credentials will not be stored in SAC.
SAP Analytics Cloud now supports tuple filters in SAP BW queries. This includes filtering with multiple dimensions in addition to using the “AND” and “OR” operators. The feature requires BW/4HANA 2.0, BW on HANA 7.5 SP16 (September 2019).
You can create filters by selecting multiple data points in the widget. Note that “Exclude”, blending, and linked analysis between widgets that contain tuple filters are not yet supported. Hierarchy and Currency Unit cannot yet be filtered.
Administration & Infrastructure
Who doesn’t like to be organized? Models can now be organized in folders. Existing models are migrated to the Public > Models folder, and existing Points of Interests are migrated to the Public > Points of Interest folder. No need to modify security roles to share models. You can:
- Save models privately and share them directly with users or teams
- Save models into team-specific folders
- Save models into Public subfolders created by Administrators
A Sharing Settings dialog makes it easier to see which users and teams have access to specific models.
For more information, please read Model Migration to Files.
This upgrade will include a migration of existing permissions. No need to fret as you won’t lose access to your models during the migration.
A team will be created with the same name as the role (the creator will be “SYSTEM”) and users assigned the role will be added to the team. The team will be granted equivalent Sharing Settings on the models in Public > Models folder. Where roles are already assigned to teams, the existing team will be granted equivalent Sharing Settings on the models.
To Read, Update, Delete, or Maintain a model, you will need to be assigned both the general application privilege (assigned via a security role) and the Sharing Setting on the model itself (can be applied on the model directly via a parent folder).
Roles that granted access to some models but not others will be updated to include the corresponding top-level Read, Update, Delete, or Maintain model permissions.
Like stories and files, you can also edit the following sharing settings on a model:
- Model owner
- Users granted Full Control (or Read and Share permissions)
- Users who belong to a team of which is granted Full Control (or Read and Share permissions)
- Administrators who have access to Browse > Files > System
It is advised to avoid resetting permissions on Browse > Files > System > Public > Models, unless you want to overwrite the migrated sharing settings on models. It is best to create your own folders, move models into them, and reset permissions for users and teams who need access moving forward.
Analytics Hub Administrators can now import Analytics Hub content when using the existing “Reset & Upload” workflow. Contents include:
- Assets (Visible, Hidden, Draft)
- User data, including favorites
- Picked for you
- Usage data
The SAP Analytics Cloud iOS SDK now supports AirWatch SDK integrated authentication with certificates. Apps built using the SAP Analytics Cloud iOS SDK integrated with AirWatch SDK can pull digital certificates from AirWatch for user authentications on live connections. Please note that this is only applicable for customers using AirWatch MDM in their organization.
Create new predictive scenarios while adding new and improved models to existing predictive scenarios in a Predictive Analytics integrator (PAi).
In the classification or a regression predictive model debrief, the variables with no influence are no longer displayed.
HANA input parameters are considered ‘dynamic’ if they use an expression to determine their value. Control at the story and widget level to access up-to-date dynamic variable values.
Story Designers can now create input controls with more than 4,000 members where all the dimension members are accessible to the users via the backend search. This is only available for filters with dynamic list of values, or where Story Designers have selected the “All Members” option. For static filters where the story designers have fixed selections, the 4,000 limit still applies. This is available in BW and supports story, page and topic filters on flat dimensions. Cascaded filters are not yet supported.
Story viewers now have access to dimension members beyond the 4,000 limit via backend search. You can select all the values that resulted from the search, given that the search result list has less than 4,000 members. The first 100 members are initially loaded, then the next members will be loaded in the background as users scroll down the widget. Note that deselecting values may result in an exclude query. The loading of filtered members in the background is a performance improvement as it reduces the time it takes to open existing stories.
Story viewers now have an ability to share their private bookmarks with other users, as long as the users have story access.
Story owners and story designers are now also able to create global bookmarks that are available to everyone with story access. When sharing the story, you can choose a global default bookmark for the selected set of users. Global bookmarks can also be shared with individual users or teams.
Bookmarks can be easily shared via a direct link found in the share dialog.
Story viewers are now able to create a single PDF export containing each member of the story input control. Only one story input control can be selected per batch export. Note that story filters based on time dimensions are not yet supported.
When two datasets are linked together using one dimension and a story or page filter is created based on an unlinked dimension, indirect filtering occurs. Backend logic for processing indirect filters has been enhanced to overcome performance issues and data volume limitations.
The performance improvements are focused on the following scenarios:
- Models linked by a flat dimension with a filter on an unlinked dimension.
- Models linked by a time dimension with the same time dimension used as a filter. This was previously treated as an indirect filter.
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