Welcome back to the next blog in the SAP S/4HANA Cloud Analytics blog series. In the last blog on “Intelligent ERP Update: SAP S/4HANA Cloud 1902 Release – Deep Dive for Analytics” where we’ve introduced several innovations as part of 1902 release highlights for our next-generation, intelligent cloud ERP solution for analytics deployment, we are happy to take the tradition of blogging series to the next level with new innovation scenario‘s for analytics. We’re releasing even more advanced analytics features and content in our new 1905 release that motivates enterprises to run independent ERP systems in cloud with analytics integration capabilities.
This blog provides you an overview of innovations in our SAP S/4HANA Cloud 1905 release for Analytics. Please check out also my video.
SAP S/4HANA Cloud 1905 – Analytics Video.
Top 3 business driven innovations to analyze your data using Analytics capabilities.
1. ABAP CDS Based Extraction from SAP S/4HANA Cloud.
2. SAP Financial Planning & Analysis - Driver-based Planning / Characteristic Derivation.
3. Real Time Manufacturing Analytics Cloud Content with SAP S/4HANA Cloud.
Let’s take deeper look at the First business driven innovation with ABAP CDS Based Extraction with SAP S/4HANA Cloud.
ABAP CDS Based extraction from S/4HANA CE is available with this 1905 release for front runner customers to replicate data in BW and BW/4HANA. Customers will be able to create an ODP-CDS source system against an S/4HANA Cloud instance by using CDS views with data extraction annotations.
First set of ABAP CDS Based extractors (Key use case examples are Asset Management, FIN-MD Hierarchies, Cost Center Activity Type, GL Account Line Item, Financial Planning Entry Item, Semantic tag, Engagement Project Master data and so on) are available with this release.
SAP S/4HANA comes with the Operational Data Provisioning (ODP) framework, which facilitates the provisioning of S/4HANA application data to a variety of consumers.
Operational Data Provisioning (ODP) provides two sets of APIs
Annotations for Full Load Data Extraction approach.
A view is enabled for data extraction via the annotation @analytics.dataExtraction.enabled: true.
Annotation dataExtraction.enabled not recommended to be added to arbitrary CDS views, but available only to views which are explicitly released for data extraction.
Key user tool - Extension of Custom CDS view tool to allow enabling of data extraction via the custom CDS view is currently not supported
Annotations for timestamp-based delta approach.
dataExtraction.delta.byElement.name – This is the element that should be used for filtering during delta extraction. This element can either be a date (ABAP type DATS) or a UTC time stamp.
dataExtraction.delta.byElement.maxDelayInSeconds – There is always a time delay between taking a UTC time stamp and the database commit. This annotation specifies the maximum possible delay in seconds. The default is 1800 seconds.
dataExtraction.delta.byElement.detectDeletedRecords – By using this annotation the system will remember all key combinations of the view that were extracted in delta mode. If a key combination does not occur in the view anymore this will automatically generate a delete image in the extracted data.
dataExtraction.delta.byElement.ignoreDeletionAfterDays – This annotation only makes sense together with dataExtraction.delta.byElement.detectDeletedRecords. The extraction will ignore deleted records if they are older than the specified number of days. The main purpose is archiving.
Annotations for Change Data Capture (CDC) - based delta approach.
Annotation dataExtraction.delta.changa.changeDataCapture - The new CDC-based approach is based on DB-triggers for the database tables used in a view. From the recorded changes in the table the resulting changes in the affected data extraction views are calculated and stored in the operational data queue for extraction consumers.
Annotation dataExtraction.delta.changa.changeDataCapture.mapping - Define the mapping of exposed elements in the CDS view and the underlying tables.
Annotation dataExtraction.delta.changeDataCapture.mapping.filter to define filters on the logging table is currently not supported.
More Information about this innovation can be found here.
Best Practices Explorer (Link: SAP Best Practices with S/4HANA Cloud)
Scroll down to Database and Data Management => Enterprise Information Management
Specific scope-item links, including documentation.
Core Data Services-Based Extraction with SAP S/4HANA Cloud (35D)
Download the guide Setting Up Core Data Services-Based Extraction with SAP S/4HANA Cloud (35D) here
Let’s take deeper look at the second business driven innovation for SAP Financial Planning & Analysis - Driver-based Planning and Characteristic Derivation.
With this scope item, we deliver more driver-based planning scenarios, allowing a planner to input simple inputs and calculate outputs. The new driver-based planning is supplemented by a process called Characteristic Derivation. During a planning process, end user can derive a dimension from the attribute of another planning dimension.
Driver Based Planning Breakout example scenario shows Accounts Receivable results based on two Driver Based Planning Inputs Days Sales Outstanding and Net Revenue.
Screen Shot for Account Receivable - Driver Based Planning Input for Days Sales Outstanding
Screen Shot for Account Receivable - Driver Based Planning Input for Net Revenue
Screen Shot for Planning Trigger Functions
Screen Shot for Account Receivable Driver Based Planning Output Results.
More Information about this innovation can be found at
Best Practices Explorer (Link: SAP Best Practices for Analytics with S/4HANA Cloud)
Scroll down to Finance => Financial Planning & Analysis for S/4HANA Cloud
Specific scope-item links, including documentation:
SAP Financial Analysis for S/4HANA Cloud
SAP Financial Planning for S/4HANA Cloud
Download the free content from SAP Analytics Content library
See how here
Let’s take deeper look at the third business driven innovation with Manufacturing Analytics Cloud Content with SAP S/4HANA Cloud.
Analytical capabilities empower users making better decisions for Manufacturing (Plan to Produce) activities with a self-learning analytics solution.
More Information about this innovation can be found at
Best Practices Explorer (Link: SAP Best Practices for Analytics with S/4HANA Cloud)
Scroll down to Manufacturing => SAP S/4HANA Cloud Manufacturing content with SAP Analytics Cloud
Stay tuned for more updates in the next quarters!
Follow us via @SAP and #S4HANA, or myself via @har1234 (Hardeep Tulsi)
This blog provides you an overview of innovations in our SAP S/4HANA Cloud 1905 release for Analytics. Please check out also my video.
SAP S/4HANA Cloud 1905 – Analytics Video.
Top 3 business driven innovations to analyze your data using Analytics capabilities.
1. ABAP CDS Based Extraction from SAP S/4HANA Cloud.
2. SAP Financial Planning & Analysis - Driver-based Planning / Characteristic Derivation.
3. Real Time Manufacturing Analytics Cloud Content with SAP S/4HANA Cloud.
Let’s take deeper look at the First business driven innovation with ABAP CDS Based Extraction with SAP S/4HANA Cloud.
ABAP CDS Based extraction from S/4HANA CE is available with this 1905 release for front runner customers to replicate data in BW and BW/4HANA. Customers will be able to create an ODP-CDS source system against an S/4HANA Cloud instance by using CDS views with data extraction annotations.
First set of ABAP CDS Based extractors (Key use case examples are Asset Management, FIN-MD Hierarchies, Cost Center Activity Type, GL Account Line Item, Financial Planning Entry Item, Semantic tag, Engagement Project Master data and so on) are available with this release.
SAP S/4HANA comes with the Operational Data Provisioning (ODP) framework, which facilitates the provisioning of S/4HANA application data to a variety of consumers.
Operational Data Provisioning (ODP) provides two sets of APIs
- A metadata API to expose properties like field names, data types, delta capabilities, etc. of the data provider.
- A “data” API to provide the actual data supporting e.g. full or delta extraction, packetizing and parallelization of large loads, detection of deletions, etc. The ODP metadata API for ABAP CDS is based on a set of dedicated ABAP CDS annotations.
Annotations for Full Load Data Extraction approach.
A view is enabled for data extraction via the annotation @analytics.dataExtraction.enabled: true.
Annotation dataExtraction.enabled not recommended to be added to arbitrary CDS views, but available only to views which are explicitly released for data extraction.
Key user tool - Extension of Custom CDS view tool to allow enabling of data extraction via the custom CDS view is currently not supported
Annotations for timestamp-based delta approach.
dataExtraction.delta.byElement.name – This is the element that should be used for filtering during delta extraction. This element can either be a date (ABAP type DATS) or a UTC time stamp.
dataExtraction.delta.byElement.maxDelayInSeconds – There is always a time delay between taking a UTC time stamp and the database commit. This annotation specifies the maximum possible delay in seconds. The default is 1800 seconds.
dataExtraction.delta.byElement.detectDeletedRecords – By using this annotation the system will remember all key combinations of the view that were extracted in delta mode. If a key combination does not occur in the view anymore this will automatically generate a delete image in the extracted data.
dataExtraction.delta.byElement.ignoreDeletionAfterDays – This annotation only makes sense together with dataExtraction.delta.byElement.detectDeletedRecords. The extraction will ignore deleted records if they are older than the specified number of days. The main purpose is archiving.
Annotations for Change Data Capture (CDC) - based delta approach.
Annotation dataExtraction.delta.changa.changeDataCapture - The new CDC-based approach is based on DB-triggers for the database tables used in a view. From the recorded changes in the table the resulting changes in the affected data extraction views are calculated and stored in the operational data queue for extraction consumers.
Annotation dataExtraction.delta.changa.changeDataCapture.mapping - Define the mapping of exposed elements in the CDS view and the underlying tables.
Annotation dataExtraction.delta.changeDataCapture.mapping.filter to define filters on the logging table is currently not supported.
More Information about this innovation can be found here.
Best Practices Explorer (Link: SAP Best Practices with S/4HANA Cloud)
Scroll down to Database and Data Management => Enterprise Information Management
Specific scope-item links, including documentation.
Core Data Services-Based Extraction with SAP S/4HANA Cloud (35D)
Download the guide Setting Up Core Data Services-Based Extraction with SAP S/4HANA Cloud (35D) here
Let’s take deeper look at the second business driven innovation for SAP Financial Planning & Analysis - Driver-based Planning and Characteristic Derivation.
With this scope item, we deliver more driver-based planning scenarios, allowing a planner to input simple inputs and calculate outputs. The new driver-based planning is supplemented by a process called Characteristic Derivation. During a planning process, end user can derive a dimension from the attribute of another planning dimension.
- The planner can focus on Planning at driver-level and the complexities of deriving data for reporting and calculations takes place in background
- Planning views and analytical stories are more flexible, allowing planners to take advantage of disaggregation in the planning process, which saves time
Driver Based Planning Breakout example scenario shows Accounts Receivable results based on two Driver Based Planning Inputs Days Sales Outstanding and Net Revenue.
Screen Shot for Account Receivable - Driver Based Planning Input for Days Sales Outstanding
Screen Shot for Account Receivable - Driver Based Planning Input for Net Revenue
Screen Shot for Planning Trigger Functions
Screen Shot for Account Receivable Driver Based Planning Output Results.
More Information about this innovation can be found at
Best Practices Explorer (Link: SAP Best Practices for Analytics with S/4HANA Cloud)
Scroll down to Finance => Financial Planning & Analysis for S/4HANA Cloud
Specific scope-item links, including documentation:
SAP Financial Analysis for S/4HANA Cloud
SAP Financial Planning for S/4HANA Cloud
Download the free content from SAP Analytics Content library
See how here
Let’s take deeper look at the third business driven innovation with Manufacturing Analytics Cloud Content with SAP S/4HANA Cloud.
Analytical capabilities empower users making better decisions for Manufacturing (Plan to Produce) activities with a self-learning analytics solution.
- Latest update’s on Real-time Manufacturing SAP Analytics Cloud content enables a production manager to dive into S/4HANA Cloud using a business KPI’s such Current Stock Analysis, Outbound Delivery Performance and Scrap Amount analysis. Accelerators include a pre-built set of LIVE analytical dashboards, key performance indicators, and reports incorporating Actual and Plan cost associated with production orders.
More Information about this innovation can be found at
Best Practices Explorer (Link: SAP Best Practices for Analytics with S/4HANA Cloud)
Scroll down to Manufacturing => SAP S/4HANA Cloud Manufacturing content with SAP Analytics Cloud
Stay tuned for more updates in the next quarters!
For more information on SAP S/4HANA Cloud, check out the following links:
- SAP S/4HANA Cloud release info: http://www.sap.com/s4-cloudrelease
- Sven Denecken’s SAP S/4HANA Cloud 1905 Release Blog
- Best practices for SAP S/4HANA Cloud here
- SAP S/4HANA Cloud User Community: register here
- Press Announcement on SAP News here
- Feature Scope Description here
- What’s New here
- Help Portal Product Page here
- Implementation Portal here
Follow us via @SAP and #S4HANA, or myself via @har1234 (Hardeep Tulsi)
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