SAP Community - SAP S/4HANA Embedded Analytics

CDS: How to use @AnalyticsDetails.elimination annotation to eliminate the internal business volume

2025-05-26T21:35:47.915000+02:00

This blog is about how to use @AnalyticsDetails.elimination to eliminate the internal business volume when executing an analytical query.

Internal business volume

When revenues made between two subsidiaries of company, for company’s total revenue, these revenues should not be considered, as transaction is happening within the company. For example, a company is divided into several subsidiaries. These subsidiaries are divided into further subsidiaries. This means there exists a hierarchy of subsidiaries. Subsidiaries are selling products to each other. For each subsidiary this is external sales. But for a parent subsidiary this is just internal sales, as both subsidiaries are descendants. By using this annotation, revenues made between two subsidiaries in an organization are no longer displayed.

More information about the concept can be found Elimination of Internal Business Volume | SAP Help Portal

Syntax

elimination: {

pair : array of {

dimension1: String(30);

dimension2: String(30);

}

rule : String(20) enum { ELIMINATE_IF_ALL; ELIMINATE_IF_ONE; };

}

The annotation can be used in cube-views (Analytics.dataCategory: #CUBE) or in analytical queries (define transient view entity with provider contract analytical query).

pair: this list defines the pairs (dimension1 and dimension2) which are checked for elimination. Both dimensions have needed a foreign key association to the same target view. If the annotation is used on cube level, both dimensions must be elements of the cube. If the annotation is used in an analytical query, both dimensions must be part of the source cube view.
rule: if set to #ELIMINATE_IF_ALL, then the volume is eliminated if all pairs are internal. If set to #ELIMINATE_IF_ONE the volume is eliminated if at least one pair is internal. If "rule" is not set, the AND-logic (ELIMINATE_IF_ALL) is applied.

Example

Following example displays the number of seat booked in flight according to the elimination rule specified on destination airport, departure airport, destination country, departure country.

@AccessControl.authorizationCheck: #NOT_ALLOWED
@EndUserText.label: 'Elimination of Internal Busniess volumn'
@ObjectModel.modelingPattern: #ANALYTICAL_QUERY
@ObjectModel.supportedCapabilities: [#ANALYTICAL_QUERY]
define transient view entity ZLR_PC_FLIGHTICE_SIMPLE
  provider contract analytical_query
  as projection on ZOQ_FLIGHT_ICE
{

  _airportfrom._hier( p_HierarchyID : 'GEO' ) as _airportFromHier,
  @Consumption.hidden: true
  _airportfrom.AirportID                      as dummyAirportFrom,


  _airportto._hier( p_HierarchyID : 'GEO' )   as _airportToHier,
  @Consumption.hidden: true
  _airportto.AirportID                        as dummyAirportTo,
  
  @AnalyticsDetails.query: {
    axis: #ROWS,
    displayHierarchy: #ON,
    hierarchyAssociation: '_airportFromHier'
  }
  
  airportfrom,
  
  @AnalyticsDetails.query: {
    axis: #ROWS,
    displayHierarchy: #ON,
    hierarchyAssociation: '_airportToHier'
  }
  airportto,

  @Consumption.filter.defaultValue: 'DE'
  @AnalyticsDetails.query.axis: #ROWS
  countryfrom,

  @AnalyticsDetails.query.axis: #ROWS
  countryto,

  seatsmax,



  @AnalyticsDetails.elimination: {
    pair: [{ dimension1 : 'countryfrom' , dimension2: 'countryto' }]
  }
  @EndUserText.label: 'SeatsMax Q-ICE country'
  seatsmax                                    as seatsmaxICEQ_C,


  @AnalyticsDetails.elimination:{
    pair: [ { dimension1 : 'airportfrom' , dimension2: 'airportto' },
            { dimension1 : 'countryfrom' , dimension2: 'countryto' } ],
    rule: #ELIMINATE_IF_ALL
  }
  @EndUserText.label: 'SeatsMax Q-ICE airport and country'
  seatsmax                                    as seatsmaxICEQ_AC,


  @AnalyticsDetails.elimination:{
    pair: [ { dimension1 : 'airportfrom' , dimension2: 'airportto' },
            { dimension1 : 'countryfrom' , dimension2: 'countryto' } ],
    rule: #ELIMINATE_IF_ONE
  }
  @EndUserText.label: 'SeatsMax Q-ICE airport or country'
  seatsmax                                    as seatsmaxICEQ_A_C


}

Output

In this example for columns (cells marked with colour):

SeatsMax Q-ICE country – elimination pair mentioned as 'countryfrom' and 'countryto'. Therefore, there is no entry where departure country and destination country are same.

SeatsMax Q-ICE airport and country- elimination pairs are 'airportfrom' and 'airportto', 'countryfrom' and 'countryto' And rule as #ELIMINATE_IF_ALL, Therefore, there is no entry where destination airport and departure airport are same AND destination country and departure country are same.

SeatsMax Q-ICE airport or country - elimination pairs are 'airportfrom' and 'airportto', 'countryfrom' and 'countryto' And rule as #ELIMINATE_IF_ONE, Therefore, there is no entry where destination airport and departure airport are same OR destination country and departure country are same.

Constraints

The annotation can be used in cubes or in queries.

The dimension names in one pair (AnalyticsDetails.elimination.pair) must have a foreign key association to the same dimension view and this dimension view must have an hierarchy association.
In the query definition:

It is not necessary that any of the dimensions in the list of pairs is part of the query
If a hierarchy is assigned to a dimension, then the pair dimension must be assigned to the same hierarchy or must be without hierarchy
If a dimension is not in drill down, then the condition of the one pair is always true (independent of the value of the other dimension of the pair). The same is true on totals/subtotal lines.

How to Embed a PowerBI Report within a Custom Fiori UI5 app

2025-06-13T16:13:54.100000+02:00

Hi Folks,

In this post I'll go over a simple yet powerful requirement: embedding a PowerBI report in a custom SAP UI5 Fiori application. This can be a great way to enrich your Fiori applications with rich, interactive analytics. Utilize this to create a seamless, unified user experience which puts data-driven insights right where decisions are made.

Prerequisites

A Power BI report that you are ready to share.
Access to the SAP Business Technology Platform (BTP) and the Business Application Studio (BAS).

Step 1: Get the Public Embed Code from Power BI

Generate a link for our Power BI report. This is the simplest method for embedding.
Follow the steps from the Microsoft knowledge base on how to embed Power BI reports into portals (also see screenshot)

Step 2: Create a new Fiori app in Business Application Studio

Open Business Application Studio and start a new project from template
Select “Sap Fiori Generator”
Select “Basic”
Select “None” for Data Source. We don't need to connect to an OData service for this example.
Input a View Name followed by Project Attributes and click “Finish”

Step 3: Add the Code for embedding to your XML view

Navigate to your project’s webapp > view folder and open the xml view you created.
Replace the existing code and insert the following code snippet in your home page view xml.
Modify the controllerName, and from the content modify the frame title, width, height, src, and frameborder as necessary.

<mvc:View
    controllerName="com.mycompany.powerbiapp.controller.Home"
    xmlns:mvc="sap.ui.core.mvc"
    xmlns="sap.m"
    xmlns:core="sap.ui.core">
    <Page id="page" title="INPUT_A_TITLE_HERE">
        <content>
            <core:HTML
                id="powerBIFrame"
                content='&lt;iframe title="INPUT_A_TITLE_HERE " width="100%"
                height="100%" src=" INSERT_YOUR_POWER_BI_EMBED_URL_HERE "
                frameborder="0" allowFullScreen="true"&gt;&lt;/iframe&gt;'>
            </core:HTML>
        </content>
    </Page>
</mvc:View>

Notice the < and >
^ This is crucial! In XML views, you must escape the < and > characters with their HTML entity equivalents (< and >) to ensure the XML is valid.

(Example from my project)

Step 4: Preview your application

Once your project is modified, save your project and preview the application by right clicking on the webapp folder and select “preview application.”
Choose one of the available preview scripts that shows up in the command palette (e.g., start-local).

Your Fiori application will launch in a new browser tab and you should see your PowerBI report embedded within your application

(Example from my project)

I hope you found this guide useful. Feel free to leave your questions and feedback in the comments below.

SAP S/4HANA Embedded Analytics for Finance: Apps, Architecture, and Implementation Tips

2025-07-01T10:18:52.032000+02:00

Introduction

If you come from the world of SAP R/3 or SAP ECC, you may hear the term ‘embedded analytics’ and assume it refers to a module or add-on, but it’s really a capability built into S/4HANA itself.

In this post, I’ll walk through embedded analytics, showing some examples for finance and sharing a few thoughts based on my own experience.

The old paradigm

SAP R/3 and SAP ECC are transactional systems. They sit on top of traditional relational databases that are not optimised for analytical operations. They couldn’t efficiently calculate totals on the fly, never mind aggregation, filtering, sorting etc.

(That’s why the general ledger had totals tables like GLT0 and FAGLFLEXT).

Secondly, the SAP graphical user interface (SAP GUI), was relatively static. It wasn’t easy to create role or context specific transactions.

The new paradigm

From SAP ECC to SAP S/4HANA, one of the biggest changes is the HANA database. It brings three new capabilities:

In memory computing
Columnar store
Parallel processing.

Combined, these enable S/4HANA to efficiently handle analytical operations.

Secondly, the new user interface: Fiori, is built on common technologies, HTML, CSS, JavaScript etc. This makes Fiori less proprietary and more dynamic.

SAP positions Fiori as a design language as well as a set of apps. They have published a design system to allow customers to build their own apps. Many of the analytical apps we will look at are based on re-usable design templates.

Enter the ‘analytical app’

This combination of flexibility from Fiori and power from HANA means we can create role based analytical apps.

There is no longer a need to load data to a data warehouse
There is no longer a need to implement a separate analytics or dashboard user interface.

Fiori itself includes many transactional and analytical apps. With embedded analytics, we are referring to a subset of popular analytical apps.

I would break down embedded analytics into three categories:

Analytical apps
- Overview pages
- Analytical list pages
- Multi-dimensional reports
- Smart business KPIs
- Review booklets
Tools
- Query browser
- View browser
Embedded SAP analytics cloud.

Let’s look at each one and discuss some examples.

Analytical Apps by Category

Before we start, let’s look at how to find information about apps online.

How to find apps

The Fiori app library is published online: Fiori apps library.

An important note is that app availability varies by S/4HANA version. For example, some apps will release in Cloud version first before coming to on-premise versions. Other apps may only be available in Cloud versions. For the purposes of this post, we will look at all versions. Some apps listed may only be available on the Cloud.

Under categories, select ‘all apps’ (16,932 apps listed!)
On the bottom right of the left sidebar, click the ‘filter’ icon
Enter some filters e.g. ‘Analytical’ apps, ‘Finance’ line of business

This gives a much more focused list of apps to scroll through (351).

We will look at ‘overview’ apps first. Enter ‘overview’ in search to narrow the list further. A list of apps that include the following is shown:

Revenue accounting overview
General ledger overview
Accounts receivable overview
Sales accounting overview
Asset accounting overview
Accounts payable overview

Overview pages

Purpose

I would describe the overview page apps as analytical ‘home pages’ for a function. They show more detail than a simple KPI, but less detail than a more focused report.

They could be considered an entry level view into the analytics for the area in question.

Overview pages have a consistent layout. A header at the top with a range of search fields, followed by the content area, which is made up of cards. The cards may have charts, values, links etc. Users can drill down from the cards to see more detail.

Content ‘cards’ are a well-established concept in frontend design, and it’s good to see SAP embrace this.

SAP provide several standard overview pages. Customers can develop their own.

Example – General ledger overview

I found two different iterations of the general ledger overview. The first is from the Fiori app library, the second from the analytics SAP help page. This highlights how pages can be set up differently.

Layout and contents

They are split into two main content sections:

Filter toolbar at the top:

The filter affects all the cards on the page
Adapt filter can be used to add/remove fields
A set of filters can be saved as a variant. For example – a range of company codes.

Multiple cards are displayed below the filters:

The cards show either charts or lists
Users can move or hide cards. A card can be set as mandatory
Examples of cards for general ledger overview include:
- G/L account balance
- Recognised revenue
Drill-down is possible at multiple levels:
- Click the card header for less granular drill down
- Click card detail such as a bar chart column to see details

My thoughts

I like these overview pages. They come across as a classic functional dashboard. Having this directly in S/4HANA is great.

Key here is the customisation. If we take a typical general ledger accountant or manager, their focus tends to vary widely by organisation and industry.

In financial services, for example, I would want my overview screen to show recent transactions on certain accounts where accuracy and substantiation of assets is critical.

In manufacturing, I may want a view more oriented towards cost of goods sold or profit and loss by function.

I don’t think I’ve ever encountered a G/L accountant who cared about days payables outstanding, so it’s unusual to see that there. I could understand a cash flow metric being there, though.

References

Analytical list pages (ALP)

Purpose

Analytical list pages aim to bring together analytical and transactional work. The idea is to combine an analytical view with transaction data such that, the analytics provide a basis to act.

The best examples of these layouts combine a filter, a chart and a work list of items into one page.

In the Fiori app library, these will be marked as Fiori elements apps. SAP provide templates, customer can also create their own. They all have headers which allow for filtering and sorting.

Example: Commitments by cost centre

Commitments is a great example. An accountant may want to monitor commitments to spot something that is out of line with plan or previous period actuals.

In case you are not familiar with commitments, let’s go into more detail.

In this app you can compare commitments, actuals, and plan/budget:

Commitments: these are goods or services you have agreed to buy but haven’t yet received or paid for. There is no accounting impact yet: consider them as planned future expenses
- In S/4HANA this could be a purchase requisition or purchase order with no goods receipt or invoice receipt
- Commitments data comes from the extension ledger in the universal journal
Actuals: actual accounting postings to cost centres come from the universal journal (ACDOCA) main ledger
Budget/plan: can either come from the universal journal plan table (ACDOCP) or BW.

S/4HANA utilises ‘extension ledgers’ for commitment tracking, an example:

Purchase requisition/purchase order (PR/PO) for £10,000
- Commitment created for £10,000 (if the PO is linked to a cost centre)
Goods receipt (GR) is received
- Commitment is reduced by £10,000
- Actual cost is posted for £10,000 (via accounting journal)
Invoice receipt (IR) is received
- Clears GR/IR account and finalises the transaction

In ECC commitment information was stored in CO tables. With the S/4HANA changes it has moved to an extension ledger of the universal journal. It is separated out from the main ledger (ACDOCA) as these aren’t accounting postings.

This app pulls:

Actuals from ledger 0L
Commitments from extension ledger (usually OE)

Example 2: Allowance for doubtful accounts

Shifting from payables to receivables, another good example is doubtful debts. This app is called allowance for doubtful accounts.

The chart on the top half does an excellent job of showing the difference between overdue receivables and allowances made.

This may trigger an accountant to either change the allowances made or to follow up on some overdue items.

The customer balances are shown under the chart, and the accountant can drill down into the individual line items.

Layout and contents

Header to set filters
- Compact display: simple drop-down lists (see example 1)
- Visual display: mini charts (see example 2)
Content area 1: charts/lists
- Charts are shown summarising KPIs to identify areas to investigate
- There is a toolbar to change chart/list types and content
- Charts are interactive. Clicking on them filters the work list shown below
Content area 2: list of transactions i.e. work list
- Add/remove fields
- You can 'group' transactions by dimension in settings
- Drill-down using a variety of apps to different levels of detail

References

Multidimensional reports

Purpose

These apps are analytical queries. You could think of them like pivot tables in MS Excel. They allow you to select different dimensions and choose whether to split data into rows or columns via those dimensions.

They are a special kind of Fiori app labelled as Web Dynpro apps in the app library.

Like other apps, they’re launched from tiles on the Fiori launchpad. However, they can also be launched from the query browser, which will be discussed later.

Multidimensional report capabilities:

Usually a long list of dimensions (depends on the query)
Sorting
Filtering
Switching dimensions
Pivot
Add totals
Display data hierarchically
Drill-down
Bookmark navigation state - makes it easy to share or return after drill-down
Save page layout (after organising)
Export to MS Excel or PDF
Create new Fiori tile from the report
Navigate to other apps
- For example, from a customer you can use the right click context menu to navigate to apps such as view sales order, change sales order

As these multidimensional reports are based on queries that contain many fields, users need to select what’s of interest to them via the navigation panel.

Example: Balance sheet/income statement – multidimensional

Generated ‘on the fly’ from line items (ACDOCA)
Can select operational, local or group chart of accounts
Drill down into individual G/L account line items
Export to excel or PDF
Various features
- Add dimensions such as profit centre, segment
- Show various currencies
- Compare periods
- Compare actual / plan
- Compare actuals across multiple ledgers
- Link through to ‘display G/L account balances’, ‘display line items in general ledger’ etc.

This app is based on the CDS view C_FinStmntComparison. More on this later.

Example 2: Journal Entry Analyser

Overview of aggregated amounts of selected journal entries by G/L account
You can choose to group by many options; company code, fiscal year, posting date, business area, functional area, profit centre
Drill down into each journal to see detail

This app is based on the CDS view C_GLLINEITEMSQ0001. More on this later.

Layout and contents

Search filters at the top
Left side navigation panel, which allows you to select row and column fields
Data table to the bottom/right

References

Smart business key performance indicators (KPIs)

Purpose

These are tiles that you place on the Fiori launchpad to show specific key performance indicators (KPIs). The KPIs can be shown as values or via mini charts.

SAP delivers various predefined KPIs. Users can select them from a role-based catalogue.

Custom KPIs can also be created.

The KPI tiles support drill down into more detail via other analytical apps. For example, drill down into a multidimensional report. See also the section on analytical path framework at the end.

Example: Accounts payable

This screenshot from SAP learning shows various KPI tiles for Accounts Payable arranged on a Fiori launchpad page. The first is ‘overdue payables’ – app ID F1746 (SAP Help / Fiori app library).

Layout and contents

Individual tiles organised on Fiori Launchpad pages
Tiles show either values or mini charts
Tiles can be configured to highlight value based on predefined ranges. In the example cash discount utilisation is highlighted in red.

My thoughts

These tiles are very useful. It’s a flexible way to build a dashboard for a specific user role. The embedded analytics features: real-time data, and drill-down add a lot of power here.

To get the most out of this requires some thought. A common mistake a project team might make is to throw all Accounts Payable KPIs on a single page for an AP clerk or AP manager. However, if you think about these KPIs, the context of how the information can be used varies widely.

I could envision a set of KPIs for an Accounts Payable clerk or manager that they may review and action daily. This could include overdue payables. Personally, I may want to add KPI tiles for topics such as blocked invoices, or payment status for critical vendors.

On the other hand, I may set up a different page for the monthly reviews that include KPIs like invoice processing time, ageing analysis, reversals etc.

References

Review booklets

Purpose

Review booklets combine multiple related reports into a collection of easy to navigate pages.

One of the first review booklets was "group financial statements". This makes sense, as the financial statements are a set of reports that together measure the state of an organisation.

Features of review booklets:

Utilise analytical queries
Similar functionalities to multidimensional reports; pivot tables, drill-downs, hierarchies
As with other apps, this is a template design. Review booklets can be created or adapted
Group Financial Statements includes:
- Consolidated balance sheet
- Profit and loss statement
- Cash flow statement
- Statement of changes in equity

Review booklets are a good example of apps that were delivered in S4/HANA cloud first. I believe the first booklets will be available for on-premise this year.

Example: Group financial statements (F6133)

Other examples

Layout and contents

Global filters: affect content on all pages
Page filters: affect the content on a single page
The booklet is split into page groups (like tabs)
Each page group has one or more pages to subdivide the topic
Each page shows pivots and/or charts side by side
Columns and rows can be adjusted
Side toolbar: from multidimensional reports
Right sidebar: with formatting options
Navigate from review booklets to other apps
- Global and page filters are applied as you navigate.

Views can be saved and shared if authorisation allows.

References

Query browser

Purpose

The query browser isn’t an analytical app itself; it’s an app to search for analytical queries. You can however launch analytical queries from within the query browser. This opens them as multidimensional reports.

Analytical queries are a special type of CDS view. I will cover this later under solution architecture. I’ll note here that you can browse finance CDS view online. Help portal – CDS views for Finance.

If you click on CDS views for general ledger and then look for items with a technical name starting C_\ these are analytical queries. For example, in this list, we can see the query C_FinStmntComparison that was used in the balance sheet/income statement multidimensional report.

Query features

SAP provides many standard queries
Customers can develop their own queries
Access to queries is role-based
Queries can be added to favourites
Queries collect data by business scenario
The data is projected into the multidimensional analysis tool

Note that the query browser does not identify underlying views or source tables, but the view browser can show this.

Example

Unfortunately, I don’t have a screenshot of a finance query to hand, but here is a screenshot of the sales order item query (C_SalesOrderItemQry):

Note the "open for analysis" button on the bottom right, this will open the query as a multidimensional report.

Layout and contents

On the first page, you will see a list of analytical queries. For navigation:

Search: search for analytical views (or tables, view descriptions, column names, annotations, user tags)
Filter: filter by tag or application component

After selecting a query, you will see query details and the option to open for analysis.

References

View browser

The query browser only shows analytical queries. Those are CDS views of type "consumption". However, the information presented is limited.

The view browser shows all CDS views with more detail.

Analytics – view browser

Embedded SAC

Purpose

So, SAP analytics cloud is a whole other thing!

But you get a taste of analytics cloud with embedded analytics called ‘SAP analytics cloud embedded version’

To illustrate the difference, we could consider `stories`: one of the key features of analytics cloud. This is where we bring together values, charts, and other visualisations: images, pictograms, etc. to create a story.

For example, a management accountant may use multidimensional reports for analysis. They may then use a story to link together various pieces of information to explain a situation. A simple example might be showing a decline in profitability in a story which highlights a couple of connected things, reducing volume from a key customer and increasing costs for a particular cost centre.

What exactly is included with the 'SAP analytics cloud embedded version'?

Limited set of features
S/4HANA data only
Wrapped in Fiori UI
Stories can be set as jump targets from embedded analytics apps
Small number of predefined stories
Ability to create your own stories

Example: operating income

Source: SAP learning

Example: working capital

Source: SAP help

References

Other items

Analytical path framework

When browsing analytical apps, you may come across this term.

This is the approach to define how you drill down from KPIs into details. For example, from a KPI you may want to drill down into a chart, then from the chart into multidimensional reporting.

Analysis path framework

Solution Architecture

Let’s look at the main components of the solution architecture.

Embedded analytics is made possible by:

The performance capabilities of HANA database tables
Various features of Fiori (web-based, flexible, role-based, extensible etc.)

One piece of the puzzle which I’ve hinted at but not explained is the virtual data model, which sits between the tables and Fiori.

Virtual data model (VDM)

SAP database tables are mostly structured by module and business transaction. Analytical apps in Fiori are structured by user-role and business scenario. We need a way to bridge this gap.

In a simple application, data is retrieved from tables directly using code (usually SQL).

However, as we are talking about thousands of tables and thousands of apps, SAP came up with a better way to manage this.

This is the virtual data model (VDM).

As per the name, it’s a virtual structure only. Data is not stored in the VDM.

The VDM is made up of core data services views (CDS views), it will make more sense if we describe those.

Core data services (CDS)

Think of a CDS view as the specification of a set of fields along with information about those fields. They are code based and are a mix of SQL and SAP proprietary language.

CDS views are structured into layers:

Basic views
- These are the lowest level CDS views
- They get data from tables
- In finance think ACDOCA, SKB1 etc.
Composite views
- These can’t read tables, but can read other views
- These can’t be consumed by apps
- In finance, for example, this could be now a combination of tables
Consumption views
- Think of these as the queries
- They are ready to be consumed by apps
- These views are based on what the analytical app needs.

An example – commitments.

Earlier in this post, we discussed an analytical app based on commitment data. That app required commitments, actuals and plan/budgets. That means the consumption view would be an aggregation of all of this data. This consumption view will rely on many composite views, which in turn rely on several basic views.

In addition to many standard CDS views, customers can create their own.

Available CDS views can be seen on SAP help.

Fiori

Fiori is positioned as a design system as well as a set of apps.

Many of these apps are based on design templates that can be used to create your own apps. Fiori itself is a large topic; in this post I’d just highlight a few key points:

The Fiori frontend is based on standard technologies; HTML, CSS, JavaScript bundled together into SAP’s framework SAPUI5 (SAP developed JS framework based on OpenUI5)
Fiori connects to the S/4HANA backend using OData services (ABAP RESTful protocol). This connects Fiori to the CDS views
Fiori apps are typically launched from the Fiori Launchpad (FLP), which runs in a browser and handles navigation, user roles, app tiles, etc.

So, when you use one of these Fiori apps generally what is happening is:

Fiori app (JS) → calls OData endpoint (/sap/opu/odata/....)
OData service → implemented via an ABAP backend (often based on CDS views)
CDS view → reads from HANA and pushes data back via the service

As a side note, in ECC, the SAP GUI client communicates with the ABAP server using proprietary protocols, which was much more difficult to customise.

Implementation considerations

Let’s consider two main scenarios:

New S/4HANA implementations and upgrades from ECC
S/4HANA already implemented, but with minimal Fiori and/or analytics.

For new implementations and upgrades, when we consider the core S/4HANA work, the workload is huge. The change management impact is huge. This is before thinking about Fiori or analytics.

It's challenging to get requirements and design right for the Finance (FI CO). This is especially true for large international organisations. Designing optimal chart of accounts, cost centre and profit centre hierarchies is a big exercise. On top of the basics, more complexity is introduced with topics like multiple ledgers, product cost control, and margin analysis.

Figuring out how to fit in analysis and design for Fiori and embedded analytics is tough.

Many projects will create an analytics work stream. In my view, this doesn't work well. Team members working on analytics need to understand the core model.

I can't emphasise enough how important it is to focus on core elements of FI/CO. Many organisations suffer for years due to poor design of profit centres, ledgers, business area etc.

The sheer number of Fiori apps is a challenge in itself. But as we've seen in this article, we can filter the list down to a more manageable focus area for finance.

When it comes to Fiori, on implementations, my suggestion is to start small. Target the Fiori apps that deliver the most value for an initial go-live, then follow up with a continuous improvement program to investigate more apps.

Core apps for go-live might look like:

The top few KPIs per function as Smart Business KPI tiles
Multi-dimensional reports for classic roles such as cost accountants, to replace any manual extraction and excel manipulation.

However, I recognise that some consultants may promote a 'Fiori first' approach: put Fiori at the centre of the project. This may be a better approach, but you must be careful not to lose focus on the core design.

In the case where S/4HANA is already implemented, an organisation is free to choose their own path depending on their current goals and available resources.

They could initiate a larger formal project to implement Fiori and/or embedded analytics. A business case could be constructed based on time saving and IT cost reduction (elimination of other apps).

On the other hand, this could be done through a longer running continuous improvement program driven by product managers or centres of excellence. I would generally lean towards the latter, as it poses less of a change impact on an organisation.

User-role mapping

Fiori is built around roles. It's good to see SAP moving towards a more nuanced understanding of business roles.

Smart business KPIs and overview pages align well to user roles, team roles, and function roles. However, one thing I'd like to highlight is how much these roles vary by:

Organisation type (public vs. private sector)
Organisation size (local vs. international organisations)
Industry (e.g. manufacturing vs. financial services)
Current challenges and opportunities (e.g. accounting issues, ongoing acquisitions etc.).

We need to be careful to ensure KPIs and overview page cards are relevant.

The best way to think about what’s relevant is to look at the organisation and function objectives. These translate into responsibilities and accountabilities for a team or user.

Responsibilities and accountabilities are executed by actions that are informed by the information.

To illustrate, my design thinking would work something like this:

Role: Accounts Payable clerk - Accountability: 98% payment on time

Payment on time KPI tile on launchpad, drill down to nearly due / overdue items
Blocked invoices tile on launchpad, drill down to line items

Role: Cost accountant – Accountability: Monitor plan vs. actual on production costs

Tiles or overview pages focussed on certain cost elements, cost centres
Multidimensional reports to investigate further

When it comes to overview pages, I see potential for them to be designed for working groups, as well as individual roles.

For example, you may have an overview page oriented towards a monthly business review. This would cover all relevant aspects of revenue and cost.

Another overview page might relate to financial operations, which would cover operational metrics.

A further one could be related to financial stewardships and focus on metrics like manual journals or reversals, or transactions above a certain threshold.

These could be a useful tool for the facilitation of meetings and discussions. I think this is also true for review booklets.

Common pitfalls

Analytics for the sake of analytics.

The tiles look pretty. The charts look pretty. It’s tempting to just throw everything at a user.

I’d be very careful about this. Any content that we use should have a clear purpose. That purpose can either be an action or an escalation.

Reporting duplication.

Some of the reports in embedded analytics will duplicate or replace other analytical reporting. We should be careful to decide which report is the source of truth, any others should be retired.

I worked for a client once that had about 10 different versions of net profit, depending on which system you took the KPI from.

I've seen a significant number of issues arise during SAP implementations with the boundary between what I'd call 'trusted financial reporting' and broader analytics.

As soon as financial data leaves the finance function and gets into analytic systems, it can be so heavily manipulated it is no longer reconcilable to what's reported externally.

Examples of this include applying alternate hierarchies, running allocations etc. or even just simply filtering out data.

Analytic users may expect they can do similar things with embedded analytics. This is usually due to a lack of understanding of finance data.

There's no right or wrong answer here. I would say there are two truths:

There must be a clear source of 'accounting truth' for both external and internal management purposes. Everyone should understand what that is.
Further experimental manipulation should be possible somewhere, but it has to be understood that data is not trusted.

The role of embedded analytics vs. other tools should be clear.

Conclusions

Overall, embedded analytics are great. The team at SAP have done a great job in two areas:

Fiori:

Based on standard web technologies
Very well documented design system
Template approach with emphasis on a customer’s ability to create what they need

S/4HANA virtual data model

A smart way to define a huge range of data sets and make them available for consumption.

As someone who implemented R/3 and ECC several times, I wish we had embedded analytics and Fiori back then.

On the flip side, as with everything with SAP, it’s highly complex. This article just covered an overview, but each of these apps has a lot of detail to address:

Technical set up
Configuration
User settings
Options to create customer versions.

Some apps are only available in certain cloud versions, and some apps require certain configuration approaches in FI CO. There's a lot of hidden complexity behind this post.

For an organisation not using Fiori and embedded analytics, I’d suggest starting with those that deliver the most value, based on your current challenges and opportunities. The best way to do this is likely through setting up an expert role and potentially a continuous improvement program.

I’d like to cite and thank SAP for the good quality information that I gathered for this post:

Various pages on SAP Help: please see the links above throughout the document
SAP learning: Discovering Embedded Analytics
Fiori app library.
Analytics on the SAP Help Portal:
- SAP S/4HANA Cloud
- SAP S/4HANA

If you want to delve deeper I suggest working through the SAP learning course on Embedded Analytics as a starting point.

If you have any thoughts on this, please share in the comments.

I have a lot of other posts about finance transformation on my website or Linkedin. A few which may be of interest:

Transitioning from SAP Design Studio to Advanced Reporting Solutions

2025-07-08T09:33:21.738000+02:00

The landscape of business intelligence is transforming, and staying ahead means embracing change. With the deprecation of SAP Design Studio, businesses should transition their custom reports to more advanced technologies to ensure seamless data-driven decision-making and meet emerging business needs.

Understanding the Current Situation

SAP Design Studio applications were officially deprecated in the CE2102 release and are no longer available as of CE2108. As this technology is no longer supported, it's crucial for users to explore alternative solutions to recreate their reports. Continuing with Design Studio may lead to operational risks and hinder future innovation. Despite the ability to use custom Design Studio apps, it's strongly advisable to transition to newer technologies that promise support and advancement.

Customer Options for Rebuilding Reports

To support this transition, there are several effective options for rebuilding reports:

Rebuild Reports Using Multidimensional Data Grid
Rebuild Reports Using Multidimensional Analysis
Rebuild Reports Using Review Booklet

Understanding the purpose and layout can refine your choice:

Multidimensional Data Grid

Purpose: Used for detailed, flexible analysis and visualization of large data sets.
Features: Allows users to switch between grid and graphical displays with configurable settings.

Multidimensional Analysis (MDA)

Purpose: Designed for detailed analysis and visualization.
Features: Enables analysis across multiple dimensions using pivot tables and charts, with functions for slicing, dicing, and saving personal data views. Based on SAP Fiori as up-to-date UI technology. Forms part of a bigger modernization concept based on review booklets using the same side panels. Is harmonized with the Data Analyzer in SAP Analytics Cloud using the same analytical controls. Offers additional features, such as commenting, measure calculation.

Review Booklet

Purpose: Offers guided pages to business users.
Features: Predefined business pages that focus on presenting key insights. Present an aggregated view of business data by combining predefined business pages, which are typically grouped under main areas. Can embed multidimensional insights for a specific area.

Select the appropriate report type according to your specific business requirements.

Exploring the Rebuild Path

Crafting a successful transition involves mapping the path from Design Studio to your chosen method.

Rebuild Reports Using Multidimensional Data Grid

Multidimensional Data Grid provides a straightforward solution for report rebuilding. Customers can easily create Multidimensional Data Grid reports by creating applications in the APP View Browser. Detailed steps can be checked in the blog.

It offers simplicity and ease of use, making it ideal for those seeking minimal time investment and consultant involvement. Furthermore, MDG comes equipped with seamless Google Workspace integration, asynchronous export capabilities, and advanced filtering options. If these features align with your near future needs, MDG should be at the top of your consideration list.

Additionally, MDG will continue to receive regular updates, ensuring enhancements in accessibility, security, and overall user experience.

Rebuild Reports Using MDA

MDA emerges as a compelling option with moderate complexity, promising regular updates and enhancements. Before creating an MDA report, queries must be recreated in the APP Custom Analytical Queries and Services. DEMO

Following this, create the MDA report in the APP Manage KPIs and Reports with a few steps. DEMO

This involves the initial recreation of queries, as MDA only supports the advanced V3 analytical query. Therefore, MDA balances the need for time and cost efficiency with the requirement to remain aligned with current trends and capabilities. MDA provides a user-friendly interface along with advanced features such as commenting, measure calculation, and AI-related functions. In CE2602, MDA will support asynchronous export, and in CE2608, it will support Google Workspace integration and advanced filtering capabilities.

Rebuild Reports Using Review Booklet

The Review Booklet offers a comprehensive method for report reconstruction, characterized by higher complexity and the necessity to redesign report layouts. It organizes the multiple reports under the same topic together to facilitate the usage. Similar to MDA, customers need to recreate a query in the new APP Custom Analytical Queries and Services, followed by navigating the Review Booklet tab in the App Manage KPIs and Reports to create a new review booklet.

Creating a Review Booklet in the Review Booklet Designer

It demands significant time and consultant involvement but assures regular updates, providing a future-proof solution for businesses with complex reporting needs and AI capabilities.

Key Takeaway: Evaluating Your Next Steps

Each rebuilding strategy carries unique advantages:

Multidimensional Data Grid (MDG) stands out for its simplicity and ease of use, making it ideal for quick and straightforward report rebuilding with minimal effort. It offers a reliable solution with ongoing updates for accessibility, security, and user experience.
Multidimensional Analysis (MDA) provides a modern and updated user interface with advanced features such as conditional formatting, commenting, and AI-assisted business insights. The rebuilding process is also not complicated which involves a few more steps, including initial query recreation. And MDA is still catching up on some features available in MDG.
Review Booklet offers a comprehensive and aggregated view of business data, embedding multidimensional insights effectively. While it demands some time and consultant involvement for layout redesign, it provides greater long-term benefits and AI capabilities, making it a robust solution for businesses with complex reporting needs.

Embracing change is essential in the journey towards more reliable, innovative reporting solutions. Transitioning from SAP Design Studio to advanced platforms opens doors to enhanced analytical experiences and future-proof capabilities. This change not only mitigates risks associated with deprecated technologies but also serves as a strategic advantage, positioning businesses for continued success in a data-driven world.

Acknowledgments

A big thank you to @zhipeng_liu for the valuable feedback and insights. Your support has been greatly appreciated.

Marla Alschweiki

2025-07-15T13:38:58.090000+02:00

Marla

Germany

SAP Mentor since 2025
Follow Marla on LinkedIn

In Marla's own words: "I am a curious SAP Data Analytics & AI Developer with a strong focus on Business Intelligence, Embedded Analytics, and AI-driven development. Over the past eight years, I've worked across the SAP ecosystem from S/4HANA and SAP BW to SAP Analytics Cloud and Fiori/UI5, where I've helped design and implement clean, scalable solutions that bridge backend data architecture with user-friendly interfaces.

I specialize in building modular Virtual Data Models using CDS Views, aligned with SAP’s clean core strategy.

I'm especially excited about the evolving landscape of SAP Business AI and its potential to transform how enterprises operate from predictive analytics and intelligent process automation to personalized user experiences.

I'm a strong believer in continuous learning, open knowledge-sharing, and the power of community. Whether through mentorship, collaboration, blogs, or workshops, I actively seek out opportunities to grow, uplift others, and create spaces where everyone feels empowered to learn, contribute, and thrive."

Topics of interest: SAP Data Analytics, SAP Business AI, Embedded Analytics, SAP Business Data Cloud, SAP Fiori/UI5

Marla, what inspired you to become an SAP Mentor?

Becoming a mentor is my way of giving back to the SAP community that’s played such a big role in my journey. I truly believe that knowledge grows when it’s shared, and I want to help others find clarity and confidence in this fast-moving field. With my background in product development, I can help approach problems with a critical eye and a practical mindset to build SAP solutions that truly address real-world needs and push innovation forward.

What advice would like to share with other SAP community members?

Stay curious and don’t be afraid to ask questions, no matter how simple they seem. SAP is a vast ecosystem, and nobody knows everything. Collaboration and continuous learning are key. Also, share your experiences! Whether it’s a small tip, a lesson learned, or a project success, your voice might be exactly what someone else needs to hear.

Commitments in focus: How SAP S/4HANA is changing the management of financial commitments

2025-07-31T12:38:31.963000+02:00

What does commitment mean in the SAP context?

Commitments describe the outstanding financial obligations of a company. These include, for example, orders, provisions or approved but not yet utilized funds. Although no payment has yet been made, these items impact liquidity, budgeting and risk assessment.

SAP records such obligations in the system in several modules, in particular in Materials Management (MM), Financial Accounting (FI) and Controlling (CO). It is precisely at this interface that SAP S/4HANA’s decisive advantage emerges.

GRAPHIC 1: Schematic overview of commitment processing between MM, FI and CO

From static reporting to real-time insight

SAP ECC already offered the option of managing commitments. However, the flow of information was often sluggish, fragmented and poorly integrated. Analyses were based on upstream reports that rarely reflected the current data situation.

SAP S/4HANA solves this problem with real-time data processing. Every relevant transaction, be it an order, an investment application or a provision, is immediately recorded in the system and can be analyzed instantly. The underlying SAP HANA database ensures that data is not only quickly available but also intelligently linked.

Comparison of COOI and ACDOCA: the evolution of commitment management

A key difference between SAP ECC and SAP S/4HANA lies in how financial obligations are managed and mapped. In SAP ECC, the COOI table (open orders, accruals and commitments) played a central role in commitment management. This table recorded open items and provided an overview of financial obligations but was often static and limited in terms of updates.

With SAP S/4HANA, commitments are managed via the ACDOCA table. This central financial accounting table offers several advantages:

Real-time data processing:
ACDOCA enables the recording and evaluation of transactions in real time. This means that every change is immediately reflected in the system, resulting in an up-to-date and accurate representation of financial obligations.
Integrated data structure:
In contrast to the fragmented data management in ECC, ACDOCA integrates data from different modules and processes, providing a more comprehensive overview of financial obligations and their impact on the organization.
Extended analysis functions:
With the ACDOCA data, organizations can perform complex analyses, such as plan/actual comparisons and trend analysis, which were previously difficult to carry out. This enables targeted management of the financial strategy and early identification of potential risks.
Flexibility and adaptability:
ACDOCA's modern data structure supports easy adaptation to increasing requirements and the integration of new technologies such as AI-supported analyses and forecasts.

This transition from COOI to ACDOCA clearly illustrates how SAP S/4HANA has advanced commitment management and supports companies in not only recording financial commitments, but also actively managing and strategically utilizing them.

GRAPHIC 2: Comparison of the commitment functionality in SAP ECC and SAP S/4HANA

Advantages of real-time data processing in SAP S/4HANA:

Application example - supply chain management:
A company uses SAP S/4HANA to monitor its stock levels in real time. By recording orders and deliveries immediately, it can react more quickly to changes in demand and avoid bottlenecks. This leads to more efficient warehousing and reduced storage costs.
Case study - Financial planning:
A company in the energy sector implements SAP S/4HANA to gain real-time insight into its financial obligations. By immediately recording investment requests and provisions, the company can plan its liquidity more accurately and identify financial risks at an early stage. This leads to improved strategic decision-making and optimized budget management.

A new level of integration and analysis

Another milestone is the seamless integration. SAP S/4HANA not only links MM, FI and CO technically, but also creates a unified view of obligations. This means that not only individual items, but also comprehensive budget evaluations or plan-versus-actual comparisons are possible at the touch of a button.

At the same time, modern analytics functions offer new insights. Companies can recognize trends, identify anomalies at an early stage and make decisions based on reliable figures.

Explanation of the Fiori interface and its advantages:

The SAP Fiori interface offers a modern, intuitive and responsive design that greatly simplifies access to SAP applications. Users benefit from a role-based user experience that takes their specific tasks and needs into account. This enables a personalized and efficient way of working, as users can quickly access the information and functions relevant to their role without having to navigate through complex menus and transactions. The Fiori interface is particularly beneficial for users without in-depth technical system knowledge, as it facilitates the use of SAP applications and increases productivity through clear visualizations and simple interactions.

GRAPHIC 3: SAP Fiori dashboard with current commitment values

Exposure in the project business: actively managing cash flow

Commitments have a particularly strategic impact in the project environment. SAP S/4HANA makes it possible to update payment obligations assigned to WBS elements, networks and orders, automatically and with precise value types. This creates the basis for project-related liquidity planning based on actual obligations.

Down payments, allocations and project-related target/actual comparisons can also be clearly mapped. The ability to adjust financial items as required also increases flexibility.

GRAPHIC 4: Project-oriented commitment process from the WBS element to the commitment

Concrete implementation and specific functions of SAP S/4HANA

User-friendliness thanks to the Fiori interface

A key difference in the implementation of commitment management between SAP ECC and SAP S/4HANA lies in the use of the modern Fiori interface, which enables more intuitive and user-friendly interaction. This interface supports a role-based and personalized user experience, allowing users to work efficiently without in-depth system knowledge.

Use of intelligent functions for commitment management

In addition, SAP S/4HANA offers specific functions such as AI-supported forecasting models that help to better manage future obligations and identify risks at an early stage. These models use historical data and probabilities to provide automated decision-making tools that significantly simplify commitment management.

Technical implementation with CDS views and virtual data model

In order to make optimum use of the functions and forecasting models mentioned, a robust database is required. The available CDS views in particular play a central role in analyzing the commitments. The content provides a manageable selection of CDS views that can be used to analyze the obligations. In the project context, it is advisable to check the associated virtual data model (VDM) and the definition of the key figures to see whether they reflect the requirements of the specialist area. This may result in expansion requirements that need to be mapped in the customer's own CDS views. In particular, when combining data from the two tables COOI and ACDOCA, it must be checked how these are merged.

What's next? A look at the roadmap

The development of SAP S/4HANA is far from complete. The upcoming releases promise, among other things, AI-supported forecasting models for commitment management. Automated decision-making aids based on historical data and probabilities will further reduce the burden on day-to-day business.

A lot is also happening in terms of user-friendliness. The continuous improvement of the Fiori apps ensures that both controllers and project managers can work efficiently with commitments without in-depth system knowledge.

Conclusion: Managing commitments, not just recording them

Commitments not only show what a company has already incurred. They are a mirror of future expenditure and therefore an early warning system for the company's finances. SAP S/4HANA takes this tool to a new level by combining real-time data, integrated processes and dynamic analysis functions with modern implementation approaches and specific functions. With the Fiori interface and AI-supported forecasting models, commitments is transformed from a mere booking value into a strategic management tool. Companies that rely on these new possibilities can monitor commitments more specifically, manage budgets more precisely and identify financial risks earlier. The key to this lies in the intelligent use of existing data and a system that makes this data available in real time and in a user-friendly way.

But that's just our view. How does your company perceive these changes? What experiences have you had with the implementation and use of SAP S/4HANA in commitment management? We look forward to hearing your perspectives and discussing new solutions together.

SAP S/4HANA Cloud Public Edition 2508 亮点前瞻：分析报表

2025-08-01T03:00:00.056000+02:00

最新的SAP S/4HANA Cloud Public Edition 2508版本为分析带来了强大的功能增强，突显了SAP致力于提供优质数据分析工具的承诺。此次更新不仅提升了效率，还增强了嵌入式分析的可用性和功能。下面我们将深入探讨这个版本的重要功能。

视图浏览器增强
多维分析：图表功能升级
审核手册：页面评论功能
审核手册APP中的“重置”功能
升级审核手册报表
【重要】弃用的分析业务目录
【重要】CE2508 弃用的CDS 视图

视图浏览器增强

此次发布的一大亮点是视图浏览器应用的增强。现在，视图浏览器应用使用多维分析来显示发布的分析查询，而不是传统的Web Dynpro Grid。这种变化为用户提供了更灵活和直观的数据分析界面，使复杂数据集的可视化和解释更加有效。

此外，用户可以方便地访问有关已弃用的CDS视图的详细信息，包括其弃用日期和替代者细节。这通过一个简单的链接或直接导航到相应的CDS视图来实现。此功能确保客户能够清晰快速了解到目前自开发项目的状态，从而有效地规划和管理。

多维分析：图表功能升级

CE2508在多维分析功能中进行了显著增强，特别是在图表功能区域。新增的系列轴功能，大大提升了数据可视化。该功能允许用户在图例或系列轴上添加维度，从而提供更大的数据呈现的灵活性。

目前，系列轴功能支持众多图表类型，包括柱状图、条形图、折线图、组合柱线图、热图、堆积条形图、堆积柱形图、百分比堆积条形图和百分比堆积柱形图。这种广泛支持确保客户能够选择最适用的图表类型，以准确地表达其数据情境。

审核手册：页面评论功能

在本次更新中，最引人注目的新功能之一是页面评论功能。该功能允许用户将评论附加到特定报告页面，并将其与相关筛选条件、维度和度量联系起来。这不仅促进了实时协作和反馈跟踪，还确保即使视图发生变化，每个人都在相同的数据背景下工作。这一改进显著提升了数据分析的无缝协作能力。

页面评论功能涵盖了几个直观的操作：

添加评论：用户可以轻松为当前视图添加评论，并自动保存相关变量、筛选条件、维度、结构和度量。
管理讨论：支持通过回复进行线程讨论或为不同话题发起新的讨论线程。
显示评论：评论默认与当前视图链接，用户可以通过关键词、分类、标签和状态进行快速搜索。
编辑与协作：用户可以编辑评论属性并使用标签通知团队成员，促进针对特定分析需求的协作数据讨论。

这项功能的推出，为团队成员之间的信息分享和合作提供了强大的支持，有助于更精准地满足数据分析的需求。

审核手册： “重置”功能

为了进一步简化分析流程，审查册中引入了“重置”按钮。在分析数据时,应用各种过滤器和进行详细的深入分析是常见步骤，然而，要回到默认设置重新开始分析常常需要繁琐的手动调整。有了“重置”按钮，用户现在只需点击一下即可轻松恢复默认布局，即标准视图。这项功能通过简化重置分析参数的步骤，提高了用户的工作效率。

升级审核手册报表

在系统升级之后，审核手册可能不是最新的，需要更新以提供最新的配置和功能。

当用户打开审核手册的配置时，需要升级的审核手册会提示用户改审核手册需要升级。升级确保所有审核手册保持高度的更新性和功能性，从而为用户提供有效的分析能力。

【重要】弃用的分析业务目录

以下是自CE2502 起弃用的分析相关业务目录，请用户及时对业务目录进行更新，切换使用后继业务目录。

业务目录	业务目录标识	后继
SAP_CA_BC_ANA_AQD_PC	分析 - 查询设计（已弃用）	SAP_CORE_BC_EXT_AQA_PC
SAP_CA_BC_SSB_PC	分析 - KPI 设计（已弃用）	SAP_CORE_BC_SSB_PC
SAP_CA_BC_VDM_BROWSE	分析 - 查询浏览器	SAP_BW_BC_AQB_PC

【重要】CE2508 弃用的CDS 视图

在CE2502版本中，有85个CDS视图将被弃用。可以点击下方链接，设置对象类型为“CDS 视图”，更改状态为“弃用”，查看具体信息。

可扩展性对象中的更改概览

用户需及时根据系统内的使用情况，将正在使用的弃用CDS视图更新为其后续版本。

如何及时发现受影响的CDS视图？请参考下方的博文。

SAP S/4HANA Cloud Public Edition ：如何及时发现受影响的项目并采取升级行动？

更多关于 SAP S/4HANA Cloud Public Edition分析报表话题:

SAP S/4HANA Cloud嵌入式分析业务用例汇总及简介查看
SAP S/4HANA Cloud公有云版本中三系统开发扩展性与二系统关键用户扩展性报表开发对比查看

SAP Analytics Cloud Q3 2025 Release

2025-08-17T15:21:32.025000+02:00

SAP has officially rolled out its Q3 2025 release for SAP Analytics Cloud (SAC) and it brings exciting new features that make working with data easier and faster.

Home Page Renewal: Personalized Productivity : The SAC home page has been completely redesigned to enhance user experience and productivity. New interface makes it easier than ever to navigate SAC and stay focused on what matters most. Key highlights include:

Customizable layout tailored to individual workflows
Quick access to:

JustAsk (natural language query assistant)
Guided learning paths
Recent and featured files
Calendar highlights

AI-Assisted Features: AI deeply into SAC with several new capabilities:

Advanced Formula Generation -

Use natural language comments to generate complex formulas.
Automatically create descriptions for existing scripts.
Ideal for data actions and planning models.

AI-Assisted Commenting -

Summarize, translate, and rephrase comments in tables / story.

Chart Summary for PowerPoint -

Generate presentation-ready summaries of charts.
Save time and improve communication of insights.

Natural Language Calculations -

Define calculations in Data Analyzer using plain language.

Data Analyzer Enhancements: The Data Analyzer now supports:

Scheduling of reports
Color highlighting for better readability
Hyperlink integration into stories
New calculation options for deeper analysis

Better modeling Tools : Modeling in SAC is now more powerful and secure as -

New aggregation exception types for advanced logic
Conditional validation to ensure data quality
Data security-based display: restrict data visibility based on user roles.

Story building made easier:

New table builder experience with undo/redo
Arrow indicators to visually represent positive/negative values
Performance recommendations tab to optimize story performance.

System Administration Updates:

SAP BTP cockpit integration for planning data storage.
Enhanced SSO (Single Sign-On) for easier access across SAP products

Reference : What's New in SAP Analytics Cloud | SAP Help Portal

SAP EWM Podcast Episode 18 - Reporting & KPIs with SAP EWM

2025-09-10T12:12:40.484000+02:00

Welcome to our blog post for the SAP EWM Podcast Episode 18 - Reporting & KPIs with SAP EWM

You can listen to the podcast episode in English and in German on Spotify, Amazon Music, Apple Podcasts or Sway! Enjoy!

From Insight to Action... KPIs and Reporting in SAP EWM!

In warehouse logistics, flexible and powerful reporting is essential to meet the diverse information needs of stakeholders—from warehouse managers and logistics leads to floor staff. SAP Extended Warehouse Management (SAP EWM) offers a rich set of tools and frameworks to support operational, tactical, and strategic reporting.
This blog post summarizes the key reporting capabilities in SAP EWM, including the Warehouse Management Monitor, Measurement Services, CDS views, and integration with SAP Analytics Cloud.

Continuous further development of operational reporting

1) Operational Reporting with the Warehouse Management Monitor

Since its first release in 2005, the Warehouse Management Monitor has been a central tool in SAP EWM for operational reporting. It enables real-time analysis and control of warehouse activities directly on the productive system.

Key features include:

Data Selection & Evaluation: Users can select and evaluate data for all EWM objects using filters, selection variants, and table layouts.
Monitor Methods: These allow direct actions such as printing documents, changing picking priorities, assigning resources, blocking stock, and more.
Automatic Refresh & Dashboards: Data views can be refreshed periodically, turning the monitor into a lightweight dashboard for workload visibility.
Snapshots: Manual or automated snapshots capture the state of warehouse data at specific points in time, useful for audits or historical comparisons.

SAP EWM Warehouse Monitor

Customizing the Warehouse Monitor for Personas and Roles

The monitor is not just a reporting tool—it’s a technical framework that supports extensive customization:

Role-Based Monitors: Customers can define monitors tailored to specific roles, showing only relevant nodes and methods.
Extensibility: New nodes and methods can be added via minor development and customizing, seamlessly integrated into the standard UI.
UI Adaptation with SAP Screen Personas: Field labels can be renamed to match user terminology (e.g., “Handling Unit” → “Load Carrier ID”), improving usability and comprehension.

SAP EWM Monitor Adaption with SAP Screen Personas

2) EWM Measurement Services – Defining Your Own KPIs

Introduced in 2007, EWM Measurement Services provide a structured approach to defining and calculating warehouse KPIs. The framework consists of three steps:

Basic Measurement Services
Predefined data selections that return raw KPIs (e.g., number of outbound delivery items).
Tailored Measurement Services
These apply selection criteria to basic services (e.g., delivery items posted as goods issue today), making KPIs more meaningful.
Calculated Measurement Services
Using a formula editor, tailored services can be combined and transformed into advanced KPIs (e.g., average delivery items per working hour).

Measurement Services in SAP EWM

These KPIs can be integrated into the SAP Fiori Launchpad as dynamic tiles using standard OData services. Thresholds can be defined, and if exceeded, SAP EWM can trigger alerts via the Alert Framework, enabling workflows, notifications, or emails.

How to Integrate EWM Measurement Services into SAP Fiori Launchpad:

SAP EWM key figure services can be called up via oData Service
This allows the key figures to be displayed as a KPI tile in the Fiori Launchpad, for example
The central oData service /SCWM/LM_MEASUREMENT_SERVICE_SRV is used for this.
This is called with the warehouse number, key figure service ID and key figure service type and returns the calculated KPI as a value.
See SAP Note 2070357 and the this SAP Community blog for additional information.

Measurement Service KPI in SAP Fiori Launchpad

Visualizing KPIs with EGF and Crystal Reports

SAP EWM also supports graphical visualization of KPIs:

The Easy Graphics Framework (EGF), available since 2007, allows visual representation of key figures.
In 2011, Crystal Reports were integrated into the Warehouse Monitor for enhanced graphical reporting

3) Embedded Analytics with CDS Views

The introduction of SAP HANA Live in 2014 marked the beginning of embedded analytics in SAP EWM. Although HANA Live was later replaced by Core Data Services (CDS), CDS views offer significant advantages:

Defined in the ABAP Dictionary and executed in SAP HANA, combining transportability with high performance.
Enable complex data selections and calculations directly in the database.
Used in standard apps like the Warehouse KPI App (introduced in 2017), which offers over 15 out-of-the-box KPIs.

SAP Core Data Services Views (CDS) - Simplified Illustration

Find more information here:

Users can also define their own KPIs using the “Manage KPIs and Reports” app, leveraging standard or custom CDS views with selection criteria, thresholds, and drill-down options.

Embedded Analytics for SAP EWM based on CDS Views

This How-to-Guide helps you to build your own KPI or watch the following video: https://video.sap.com/media/t/1_92uy711w

4) Beyond EWM – Integration with SAP Analytics Cloud and Excel

CDS views extend reporting beyond SAP EWM:

SAP Analytics Cloud (SAC): Enables real-time, cross-warehouse dashboards without data replication. EWM data can be combined with other sources for holistic insights.
Microsoft Excel Integration: With a small add-in, users can pull data directly from SAP EWM into Excel for further analysis—ideal for organizations heavily reliant on Excel.

SAP EWM meets SAP Analytics Cloud

Find more information about SAP Analytics Cloud here:
https://www.sap.com/products/data-cloud/cloud-analytics.html

CDS Views in MS Excel

Find more information about CDS views in MS Excel for SAP EWM here:
https://help.sap.com/docs/SAP_BUSINESSOBJECTS_ANALYSIS_OFFICE

Strategic Reporting with SAP BW/4HANA

For long-term planning and strategic reporting, SAP recommends using SAP BW/4HANA:

EWM is an operational system; data such as deliveries, packaging, and serial numbers are transferred to systems like SAP ERP or SAP GBT after execution.
Documents should be archived after 3–6 months to maintain performance.
SAP BW/4HANA provides extractors and BI content for long-term reporting across time series.

Conclusion: Maximum Transparency in Logistics

SAP EWM, together with tools like SAP Analytics Cloud and SAP BW/4HANA, offers a comprehensive reporting landscape—from simple KPIs with alerting to advanced dashboards and predictive analytics. These capabilities empower warehouse operations with actionable insights and support data-driven decision-making

Further Resources

Please visit our SAP EWM Podcast Landing Page with all Episodes, Blog Posts and Podcast Notes!

Fixing the “More Members Available” Issue in SAP Analysis for Office based on Custom CDS Views

2025-09-30T09:01:06.054000+02:00

While working with SAP S/4HANA Embedded Analytics and SAP Analysis for Office (AFO), you may encounter the message: "More Members Available"

This typically appears when filtering or selecting values in prompts, and it indicates that the number of available members exceeds the display threshold. Although SAP provides a configuration setting (FetchMemberLimit) to increase the limit, this doesn’t always resolve the issue, especially when the value help includes duplicate entries.

Standard Fix: You can try increasing the member limit in AFO:

Go to File > Analysis > Customize Analysis > Technical Configuration
Set FetchMemberLimit to a value higher than the expected number of members.

However, this only works if the number of members is within the new threshold. If duplicate entries exist, the system still struggles to display all entries. In many cases, the issue arises from repeated members in the value help CDS view. This is common when the value help is generated from a custom CDS view without ensuring uniqueness.

To resolve this, modify your value help CDS view to return distinct values.

Syntax

define view XXXXX as select distinct from XXXX
{

}

In previous blogs, we have seen how value help can be generated with help of below annotations.

@Consumption.filter.multipleSelections: true
@Consumption.valueHelp:

Conclusion
The “More Members Available” issue in SAP Analysis for Office can be frustrating, but it’s often solvable with a simple tweak to your CDS view design. By ensuring your value help view returns distinct values, you can improve usability and performance in your embedded analytics reports.

Exploring CDS based Analytical Models with the Relation Explorer - analytical view/perspective

2025-10-01T16:39:40.313000+02:00

Relation explorer analytical view / analytical perspective makes it easy to get an overview of the analytical model with very less clicks. No need to open all the views to understands details.

We start small , looking to a branch of a star schema , dimension Connection ( /DMO/ANA_I_Connection ) which is part of the flight data model. After calling the relation explorer we see the Relation Explorer Tab in the below section of the screenshot.

Relation Explorer tab shows:

in the left pane the associations from the dimension to other CDS views over all levels are visualizes . It starts fully expanded!
next to the CDS view name we see the view type. E.g. /DMO/ANA_I_CARRIER is of type [Dimension]
in the right pane one can see all the elements of current view. As /DMO/ANA_I_Connection is selected all elements of this dimension are displayed.
- One can select (single click) any other view e.g. /DMO/ANA_I_CARRIER and the right pane get updated with the elements of that view.
- double clicking the view will directly open the view
the right pane displays all elements grouped by roles.
- In this example we have 2 keys and e.g. expanding AirlineID show label and additional details about association and InfoObject. In case of compounded parent we would have seen the Parent element.
- double clicking AirlineID highlight the element in the source ( see line 21 )
- [Text] gives a hint that AirlineID has text, this is missing for ConnetionID -> no Text supported
- Attributes list all dimension attributes. Last one, DistanceUnit is a Unit element.
- the Unit group show all unit related elements.
- other group are shown if relevant.

One can also start the analytical Relation Explorer based on query or cube view.
In both cases the cube view is opened and visualizes the cube with all associations .

We change focus and look to the example of a query - starting relation explorer shows the cube definition including dimension Connection from above.

with one view

in the left pane we get all associations visualizes. View names are shown with the view types
in the right pane we can see all 13 dimensions of the cube and get if dimension support Text, have Attributes or Hierarchies
- from 1st screenshot we already know that Text, Attributes and Hierarchies are available for AirlineID
- expanding ConnectionID show that it is compounded to AirlineID
we see all 5 measures with additional details like aggregation method and unit
with " Units (2) " we see the unit related dimensions. This section only list those dimension that are unit relevant. Expanding the dimension like CurrencyCode lists all related measures.
( Both dimension are also listed in dimension section. )
for time based dimension one can directly understand the base dimension and wherefrom it is derived without looking into the source ( see below snippet with FiscalYearMonth )

How to start the Relation Explorer

call relation explorer to see the view

right click somewhere in the CDS view

( in case of CDS view type cube, dimension or query the analytical view is the default view )

If the view is not shown, choose Configure Context and activate it.
one can switch any time to different perspective / context if needed

Release Info: analytical view / analytical perspective started with

BW/4HANA 2023 FP 04
S/4HANA Public Cloud and Privat Cloud OP2023

Reporting and Analytics

2025-10-23T15:02:24.955000+02:00

Co-Author: Gergana Deneva; in addition, generative AI was used to help create this content

Elevate Your SAP S/4HANA Experience: We Value Your Feedback!

At SAP, we're committed to enhancing the reporting and analytics capabilities of SAP S/4HANA to better meet your evolving needs. We are therefore excited to invite you to participate in a customer engagement initiative focused on “Review Booklets and MDA: the next level of SAP S/4HANA Reporting and Analytics.”

Why Your Input Matters

Your insights and experiences are invaluable to us. By sharing your requirements and feedback, you help shape the future of SAP S/4HANA, ensuring that our solutions are tailored to your specific needs. This interactive initiative is applicable for both Public Cloud and Private Cloud Edition users.

What's in it for you?

This customer engagement initiative aims to delve into the next level of SAP S/4HANA Reporting and Analytics. You will have the opportunity to provide feedback and learn more about the following topics:

Review Booklets: Explore the latest innovations in report generation and customization.
Multidimensional Data Data Analytics (MDA): Discover advanced analytics capabilities designed to provide deeper insights into your business operations.

Your feedback will help us refine the apps, ensuring they align with the requirements of your business.

How to Participate

We’ve made it easy for you to get involved:

Visit the Influence Page: Go to the campaign page on SAP Influence.
Register: Click on the registration link and follow the simple steps to sign up. Please note that the registration period is open until November 15th.
Provide Feedback: Once registered, you will have access to the platform where you can share your thoughts and requirements.

How you'll benefit

Participating in this initiative brings several advantages:

Direct Influence: Your feedback will have a direct impact on the product roadmap.
Early Access: Gain early insights into upcoming features and innovations.
Community Engagement: Network with other SAP S/4HANA users and share best practices.

Don't Miss Out!

This is your chance to play a pivotal role in the evolution of SAP S/4HANA Reporting and Analytics. Your valuable contributions will help us create a more robust and user-friendly platform that meets the diverse needs of our community.

We look forward to your feedback and are excited to collaborate with you on this journey - register now!

Important Dates: Registration Deadline: November 15th

For more information and to sign up, please visit the campaign page.

Thank you for your continued support and partnership. Together, we can drive innovation and create exceptional business solutions.

Information on the SAP Help Portal

SAP S/4HANA, SAP S/4HANA Cloud Private Edition

SAP S/4HANA Cloud Public Edition

Review Booklets

Multidimensional Analysis

What's New Viewer (Cross Applications -> Analytics)

Review Booklets

Multidimensional Analysis

What's New Viewer (Cross Applications -> Analytics)

How to derive dynamic text label in CDS view with value lookup

2025-10-26T15:45:04.408000+01:00

Background:

Recently I got a requirement to create an account payable aging report which contains dynamic text based on user input, for example, user input aging interval as: 30, 60, 90. in the output of the report, the required report format will be:

Customer	Due in 30 days	Due in 31-60 days	Due in 61-90 days	Due 91+ days
1001	$300	$600	$900	$1200

In the traditional SAP BW world, this can be easily achieved by Text variable, however in SAP CDS view, dynamic text is not available in early versions. In my current system version, the version is ABAP 7.58, and it supports this annotation very well.

Solution:

So instead of going this complex scenario, I will use a simple SAP data source: SFLIGHT to achieve the similar requirement.

Scenario explanation:

There will be only one base measure called Airfare, based on the user input: number of Days X, the output will show the AirFare, X Days AirFare, X+1 Days AirFare, the highlighted two columns text will be dynamically derived based on the user input

Year Month	AirFare	10 Days AirFare	11 Days AirFare
02.2023	$300	$600	$900

This is how the end to end implementation work look like

Define a helper view: this is to return the day + 1

Define cube level data model, with base fare, base fare * input days, base fare * input days + 1, the measure if purely for distingush the different dollar value

Comes with the fun part, in the query view, we define the parameter which populate the derived dayPlus1 using lookup view which defined in first step:

In the measure column, wewill appy the dynamic text using annotation: @Consumption.dynamicLabel:

Now Let's run the CDS view from BW query, as you can see user will be prompt to fill input days (default value is 10):

The measure column will derive the text dynamically based on user input and helper view:

With the help to combine annotation @Consumption.dynamicLabel and @Consumption.derivation.lookupEntity, we will be able to achieve the same requirement in the BW world by using Text variable and custom exit.

I hope this blog give you some help with the how dynamic text can be implemented using SAP CDS view, if you have any questions, please feel free to leave a message.

CDS : How to define default Key Display in Analytical queries

2025-11-05T09:33:09.182000+01:00

This blog explains how to define default key display settings in analytical queries, ensuring user-friendly key visualization. Many Hierarchy Nodes and Dimensions have compounded keys that are shown as a long-concatenated keys during runtime. Sometimes its not needed to see the concatenated long key. To enhance readability, @AnalyticsDetails.query.keyDisplay annotation is use to specify the default display behaviour for key.

Annotation: @AnalyticsDetails.query.keyDisplay

Accepted Values:

#NOT_COMPOUND: Displays the key as a non-compounded characteristic value.
#COMPOUND: Displays the key as a compounded characteristic value (concatenated long key).
#CONTEXT_DEPENDENT: Displays the key based on its uniqueness. If the compounded key is unique due to drill-down selection, it will be shown as non-compounded

Example

@AccessControl.authorizationCheck: #NOT_ALLOWED
@EndUserText.label: 'Query: Simple Example for Key display'
define transient view entity ZLR_PC_KEY_DISP 
provider contract analytical_query
as projection on ZOQ_FLIGHT {

  
  @AnalyticsDetails.query.axis: #ROWS
  carrid,
  
  @AnalyticsDetails.query: {
      axis: #ROWS,
    totals: #SHOW
  }
  @EndUserText.label: 'ConnectionID Not Compound'
  @AnalyticsDetails.query.keyDisplay: #NOT_COMPOUND
  connid,
  
  @AnalyticsDetails.query.axis: #FREE
  @analytics.hidden: true
  currency,
   
  @Semantics.amount.currencyCode: 'currency'
  @EndUserText.label: 'Booking total 2019'
  paymentsum

 
}
where flyear =  '2019'
  and currency = 'EUR'

Output

Release Info

This feature is available from Cloud release 2508

SAP Note: 3600690 - CDS: query display key settings

Simple understanding of Hierarchies in SAP S/4HANA Embedded Analytics– Part 1 (Basics)

2025-11-21T12:47:55.322000+01:00

Accurate CDS hierarchy modeling and understanding is critical for financial reporting and operational analytics. Without well-defined hierarchies, drill-down analysis and aggregated insights become challenging. This blog demystifies hierarchy concepts in SAP S/4HANA Embedded Analytics and shows how they power SAC,Multidimensiona Reports , AFO and Datasphere scenarios.This blog will give you the basic understanding of Hierarchies in CDS Views (S4 Hana)

What This Blog Covers

Common hierarchy terminologies
Types of hierarchies in CDS Views
Standard hierarchies in S/4HANA
Why custom hierarchies matter

1. Key Terminologies-

Before diving into implementation, let’s clarify the basics:

Root Node : Top-most node in the hierarchy. Has No parent. Signifies the Hierarchy Structure and contains the Hierarchy metadata. A hierarchy can have several root nodes.
Hierarchy Node : Represents a group or element in the hierarchy.
Parent Node : A node that has child nodes beneath it.
Child Node : Belongs to a parent node.
Leaf Node : Lowest node in the hierarchy (end point).
Hierarchy Directory : Stores hierarchy definitions.
Siblings : Nodes sharing the same parent.
Hierarchy Level : Indicates depth of a node in the hierarchy. Levels can be defined as 0,1,2,3 . In S/4 EA change level based hierarchies to Parent child hierarchies .
Recursive Hierarchy: Nodes can reference themselves (e.g., Org Structure). Implemented using self-association in CDS.
Hierarchy Node Texts: These contain the descriptive names or labels associated with the hierarchy nodes, typically derived from the related master data (for example, the name or description of a General Ledger account or Product Hierarchy Description).
Orphan nodes, These are the nodes missing a link to a root. You can choose to ignore them, raise an error, or promote them as alternative roots using annotations like ORPHANS = ROOT

2. Example of a Hierarchy-

Company → Product Category → Product → Variant Shoe Model 1

Example:

Company (ABC Corp) → Root Node -> The top most node in hierarchy (Defined via Hierarchy Directory and Root node has no parent defined)---- To be discussed in details in another Part 2.
Product Category (Footwear) → Child of Company->A Parent Node for Product but this is not the Root node(Since it has Company as a Parent Node).
Product (Shoes) → Child of Product Category->Parent Node for Variant
Variant (Shoe Model 1) → Leaf Node->Lowest Node in the hierarchy

In SAP: For Cost Center or Profit Center hierarchy, Controlling Area could be the root node.

3. Types of Hierarchies in CDS Views-

In S/4HANA Embedded Analytics, hierarchies are modeled using CDS annotations. Most common type:

Parent-Child Hierarchy 🔗 Always recursive. Each node references its parent, forming a tree structure. Example: Cost Center hierarchy where each cost center belongs to a higher-level.

Level-Based Hierarchy 🔗Not supported in S4 Hana Embedded analytics as Analytical engine supports only Parent Child Hierarchy for drill downs and aggregations.

CDS Annotation Example:

@Hierarchy.parentChild:
{ recurse:          {   parent: 'ParentNode',   child:  'HierarchyNode'   },
  siblingsOrder:    {   by: 'SequenceNumber',    direction: 'ASC'   },
  directory:        '_Hierarchy' }

4. Standard Hierarchies in S/4HANA-

SAP delivers several predefined hierarchies:

Cost Center Hierarchy – For controlling and cost reporting
Profit Center Hierarchy – For profitability analysis
GL Account Hierarchy – For financial statements
Product Hierarchy – For material classification
EHS Location Hierarchies – For Environment, Health & Safety compliance

5. Custom Hierarchies-

Business requirements often demand custom hierarchies:

Example: A sales region hierarchy combining geography and product lines.
Implemented using CDS Views by developing a relation between Dimension Views and Hierarchy View.

6. Why Hierarchies Matter?

Enable drill-down reporting in SAC or Fiori Multidimensional Reports/AFO.
Support aggregations at multiple levels
Provide flexibility for planning and analytics

7. Technical Details: Where Hierarchies Are Stored-

HRRP_NODE_N Table: Stores Parent Node info, hierarchy node level details, and relationships between nodes(Example where GL Account/Profit Center/Cost Center Hierarchy details are stored).
HRRP_DIR_N Table: Stores Hierarchy Directory info (name, version, metadata).

👉When you activate or create a hierarchy (standard or custom), these tables are updated.

HRRP_NODE_N = Node-level details (Parent-Child relationships)
HRRP_DIR_N = Directory-level details (Hierarchy header info)

8. Hierarchy Components & Annotations-

A well-defined hierarchy in CDS has multiple views:

Source(Master Data) View: Provides key fields and relationships (Eg GL Account/Cost Center/Product related details).
Hierarchy View: Contains the relationships and parent–child links. Eg - Hierarchy(Child) Node, Parent Node, Node levels etc.
Directory View: Provides metadata about hierarchy variants -- Optional.
Text Views for labeling nodes and directory entries . Eg: GL account and it's text/ Cost center and it's text etc.

Proper annotations (@Hierarchy.parentChild, @ObjectModel.dataCategory: #HIERARCHY) control recursion, sibling order, orphan handling, and directory association

Notes:

Parent–Child Hierarchy: Supported in CDS for analytics; recursive structure.
Level-Based Hierarchy: Not supported in CDS for analytical consumption (only parent–child works for drill-down).
Orphan nodes = Nodes without a parent.
A Parent Node can have multiple Hierarchy Nodes/Child nodes but a hierarchy node cannot have multiple parents
A node can appear under different parents in different hierarchy versions, not in the same hierarchy( It usually refers to alternate hierarchy versions or variants, not simultaneous parents in the same hierarchy. For example:

Hierarchy Variant/Version 1: Region → Country → City

Hierarchy Variant/Version 2: Sales Channel → Country → City.This is managed via Hierarchy Directory.

A CDS hierarchy cannot serve as the underlying source for building another CDS hierarchy.

Next Article (Part 2)

How to activate Standard hierarchies and develop Custom Hierarchies in CDS Views (S4 Hana EA).
How to consume hierarchies in SAC

Extending an SAP Sample Sankey Custom Widget in SAP Analytics Cloud to Deliver Top-N Hierarchical In

2025-12-23T08:37:05.630000+01:00

Introduction

SAP Analytics Cloud (SAC) does not provide a native, built-in Sankey chart as part of its standard visualization catalog. As a result, Sankey-style visualizations in SAC are typically implemented using custom widgets.

In mid-2024, while working on an enterprise analytics engagement, I encountered a requirement to visualize:

Top N parent entities (for example, service providers)
And, for each parent, the Top M child entities independently ranked per parent (for example, services offered)

All of this needed to be displayed within a single Sankey visualization, enabling users to immediately understand concentration and distribution patterns without navigating multiple charts.

To address this requirement, I explored SAC’s Custom Widget framework and extended an existing SAP-provided sample Sankey custom widget into a production-ready solution.

I have intentionally included the development and deployment steps in detail so that consultants or developers who are completely new to Sankey custom widgets in SAP Analytics Cloud can deploy and validate the solution end-to-end by following this blog.

SAP Sample Sankey Custom Widget as the Starting Point

Since SAP Analytics Cloud does not include a native Sankey chart, I referred to a sample Sankey custom widget implementation shared by SAP through the SAP Community. This SAP-provided sample demonstrates how a Sankey diagram can be rendered using the SAC Custom Widget framework and served as a technical starting point for my work.

The SAP sample was very helpful as a technical reference, as it illustrated:

Custom widget structure and lifecycle
Binding SAC model data to a widget
Rendering a Sankey diagram using JavaScript

During mid-2024, while actively working on this requirement, I engaged in the comments section of that SAP Community blog (SAP Analytics Cloud: Custom Widget & Widget Add-Ons Samples Preview) under my SAP Community user@GovindaRaoBanothu45 , discussing feasibility and implementation boundaries.

While the sample clarified the basic rendering approach, it also highlighted functional gaps when applied to enterprise-scale analytical requirements.

Why the SAP Sample Sankey Widget Was Not Sufficient

The SAP-provided Sankey custom widget was intentionally designed as a generic sample. Its behavior was to render all nodes and all links returned by the bound dataset.

However, the sample widget did not support the enterprise analytical requirement I was addressing, specifically:

No Top-N filtering at the parent level
No independent ranking of child nodes per parent
No data trimming prior to Sankey rendering
Limited usability for large, real-world datasets

As a result, while the SAP sample demonstrated how to render a Sankey chart, it did not meet the analytical or usability needs of an enterprise dashboard.

This gap required extending and enhancing the SAP sample Sankey custom widget with additional, original logic to reshape the dataset before rendering.

High-Level Architecture of the Extended Sankey Widget

The Sankey rendering mechanism remains aligned with the SAP sample, while the data-transformation layer represents the core extension.

Development Environment and Prerequisites

Prerequisites

To get started, ensure that you have:

Access to SAP Analytics Cloud with Optimized Design Experience enabled
Node.js installed on your local machine
Visual Studio Code (VS Code) installed
Basic understanding of JavaScript and JSON

Development Environment Setup

Install Node.js

Download Node.js from: https://nodejs.org
Verify installation:

node -v
npm -v

Install Visual Studio Code

Download VS Code from: https://code.visualstudio.com
Use VS Code as the primary editor for developing and maintaining the custom widget files.

Set Up the Project Folder

Create a project folder (for example, SankeySAPcode).
Open the folder in Visual Studio Code.
Create the following files:

main.js – core widget logic and Sankey rendering
index.json – metadata and resource configuration
hash256.js – Node.js script for integrity key generation

Sankey Custom Widget – Standard Project Structure

For the standard Sankey implementation, I created a dedicated project folder (for example, SankeySAPcode or any relevant project name). This folder contains all the supporting and required files needed to build and deploy the Sankey custom widget in SAP Analytics Cloud.

The project folder includes:

main.js
The web component JavaScript file that contains the core widget logic and Sankey rendering code (mandatory).
index.json
The metadata file that defines the custom widget, its properties, and resource references (mandatory).
sankeyChartStyling.js
An optional styling component used to expose styling options in the widget’s styling panel (optional).
build/ folder
Contains the helper script hash256.js, which is used to generate SHA-256 integrity keys for the JavaScript files.

Note: Styling files (Styling.js, sankeyChartStyling.js, or xxxxStyling.js) are optional and may not be required for all custom widgets.
However, the web component JavaScript file (main.js or equivalent) and the metadata file (index.json) are mandatory for every SAP Analytics Cloud custom widget. The build folder is included purely for maintainability and ease of understanding.
Technically, the hash256.js file can also be placed directly in the main project folder. Including it in a separate subfolder is optional and does not impact deployment, as long as the final ZIP file complies with SAP Analytics Cloud’s upload requirements.

Creating Integrity Keys and Deploying the Code

SAP Analytics Cloud supports SHA-256 integrity validation to ensure uploaded JavaScript resources are not modified.

Generating Integrity Keys

Open a Node.js command prompt
Navigate to the folder containing hash256.js
Execute the script

Example output:

main.js: sha256-6134FFLCQYpH7x8gx29H1xJyRbitpf/guj5UGKhJ25Q=
sankeyChartStyling.js: sha256-AR0uDO+9h8nu9EyJotSinZ2APDWD5Ja2JmdFcZCH1Xo=

Note: If additional JavaScript resource files are included, their corresponding sha256- integrity keys must also be added to the index.json file under the resources section.

Similarly, the file names must be updated in hash256.js before generating integrity keys for those files.

Core Enhancement: Top-N Parent and Top-M Child Logic

The following snippets are representative excerpts from the working implementation.
Full scripts are intentionally not published, as this widget was delivered as part of an enterprise engagement.

In the widget, the SAC data binding is first normalized into an array (data) containing parent node, child node, and measure value fields.

Step 1: Aggregate Measure by Parent

const parentTotals = {};
data.forEach(row => {
  parentTotals[row.parentNode] =
    (parentTotals[row.parentNode] || 0) + Number(row.measureValue);
});

Step 2: Rank and Retain Top-N Parents

const topParents = Object.entries(parentTotals)
  .sort((a, b) => b[1] - a[1])
  .slice(0, TOP_N_PARENT_NODES)
  .map(entry => entry[0]);

Step 3: Rank and Retain Top-M Children per Parent

const filteredLinks = [];
topParents.forEach(parent => {
  const children = data
    .filter(row => row.parentNode === parent)
    .sort((a, b) => b.measureValue - a.measureValue)
    .slice(0, TOP_N_CHILD_NODES);

  children.forEach(child => {
    filteredLinks.push({
      source: parent,
      target: child.childNode,
      value:  child.measureValue
    });
  });
});

Step 4: Rebuild Nodes

const nodes = [...new Set(
  filteredLinks.flatMap(l => [l.source, l.target])
)].map(name => ({ name }));

Step 5: Render Sankey

sankey.nodes(nodes).links(filteredLinks).layout(32);

The rendering follows the SAP sample approach; the innovation lies in the data-transformation logic.

Once all required files and script updates are ready, let’s deploy the custom widget into SAP Analytics Cloud.

Deploy the Custom Widget

Create a ZIP File:

Include main.js, index.json, and optional styling files in a .zip file.

Upload to SAP Analytics Cloud:

Navigate to the Custom Widgets section in SAC.
Upload the .json and .zip files.

Test the Widget:

Add the custom widget to a story and configure data sources.

Select the JSON metadata file from your local system.

Next, select the resource file that is .zip file of main.js, index.json and styling.js or any other file related to this chart.

Now you can the widget in "Custom Widgets" section.

Observe the warning messages below with and/without integrity checks in the list of custom widgets

We have done custom widget deployment and will use the same in stories. Let's see how to access this in story 2.0.

From the builder panel, select your data model, set measure, set a hierarchical dimension and edit the default colors of the different depths from the Styling panel.

Below screenshot show the usage of the Styling.JS file.

In case the java script files get modified / tampered you get the below error while creating chart using custom widget in SAC story.

Validated Sankey Variants - Let's validate 3 different scenarios

SAP sample Sankey (baseline):

Top 1 parent + Top 5 child nodes:
- This is the custom code and the main.js file is completely different than standard code with the key components are the below conditions below.

Result of the above changes,

Top 3 parents + Top 5 children:

Result of the above changes,

This incremental approach helped validate correctness before applying the logic to larger Top-N configurations.

Acknowledgment

I would like to thank @marouferchichi for actively engaging in the SAP Community blog comments and helping clarify the capabilities and boundaries of the sample Sankey custom widget.

The final implementation described here represents my independent extension and delivery of that sample to meet enterprise-level analytical requirements.

Conclusion

This blog demonstrates how a SAP sample Sankey custom widget can be extended into a production-grade Top-N hierarchical Sankey visualization using SAP Analytics Cloud’s Custom Widget framework.

For similar enterprise scenarios, this approach provides a reusable and scalable pattern.

Authorization setup for SAC Import Model from S4 ABAP CDS Views

2025-12-24T13:05:47.674000+01:00

Purpose of this document

The purpose of this blog is to explain how to implement data-level authorization in SAP Analytics Cloud Stories when:

Data is extracted from SAP S/4HANA CDS Views
The connection type is Import Model
Authorization must be enforced based on Company Code and Plant

This approach ensures that users can access only the data they are authorized to see, even though the data is physically stored in SAC.

Business Use Case

In many finance and controlling scenarios:

A single SAC Story is shared across multiple business users
Users belong to different Company Codes and Plant
Data is periodically imported from SAP S/4HANA into SAC

Since Import Models store data in SAC, runtime authorisation from S/4HANA (CDS DCLs) is not applied. Therefore, authorization must be designed and enforced within SAC.

Authorization Concept for Import Models

When working with SAC Import Models, authorization is handled as follows:

Layer	Responsibility
SAP S/4HANA CDS	Defines which data is extracted
SAC Model	Enforces row-level data authorization
SAC Story	Automatically respects model-level security

Key points to note:

CDS DCLs are evaluated only at data extraction time
SAC Data Access Control (DAC) governs user visibility
Story designers do not need to manage authorization logic

Example Used in This Blog

The same example is used consistently throughout the blog.

Attribute	Value
Authorization Dimensions	Company Code, Plant
Sample Company Code	0100
Sample Plant	RA01, RA02, …
SAC Connection Type	Import
Source System	SAP S/4HANA CDS View

Step-by-Step Authorization Setup

Step 1: Prepare CDS View in SAP S/4HANA

Ensure that the CDS View contains the fields required for authorization, i.e., Company Code and Plant.

Example CDS Used:

@AbapCatalog.sqlViewName: 'ZCDEMO002V'
@AbapCatalog.compiler.compareFilter: true
@AbapCatalog.preserveKey: true
@AccessControl.authorizationCheck: #NOT_REQUIRED
@EndUserText.label: 'Demo Authorization Consumption VIew'
@Metadata.ignorePropagatedAnnotations: true
@Analytics.query: true
@VDM.viewType: #CONSUMPTION
define view ZC_DEMO_AUTH_2
  as select from ZI_DEMO_AUTH_1
{
  PurchaseOrder,
  PurchaseOrderItem,
  PurchaseOrderCategory,
  CompanyCode, // Authorization field
  Plant,       // Authorization Field
  @Semantics.currencyCode: true
  DocumentCurrency,
  @DefaultAggregation: #SUM
  @Semantics.amount.currencyCode: 'DocumentCurrency'
  PurOrderNetAmount
}

Important notes:

Authorization-relevant fields must be part of the CDS output
CDS DCLs are not used for runtime authorization in SAC Import Models

Step 2: Create Import Model in SAP Analytics Cloud

Navigate to Connections in SAC.
Create an SAP S/4HANA Import Connection.
Import the CDS View as a Model.

At this stage, the data is physically stored in SAP Analytics Cloud.

Step 3: Enable Data Access Control (DAC)

Open the imported Model
Navigate to Settings
Navigate to Model Preferences
Enable Data Access Control

This step activates row-level authorization for the model.

Step 4: Mark Authorization-Relevant Dimensions

Enable data access control for the required dimensions.
Select CompanyCode, and Plant.

These dimensions are now evaluated during data access.

Step 5: Create Data Access Roles

Navigate to Security → Data Access
Create a new Data Access Role

Provide Role information, Role template, and review before creating. Template helps to create roles fast for general sections like BI Admin or Planner. In this we will choose template as ‘Blank’.

After role is created, we will move to ‘Select Model’, choose your import model.

Select dimensions required for authorizations and provide values for each.

Step 6: Assign Data Access Role to Users

Navigate to Security → Roles
Select the relevant user
Assign Business Role – SAC_ROLE_DEMO_001 to users for Row-level authorization.
The user now inherits Company Code and Plant restrictions.

Step 7: Validate Authorization in SAC Story

Create or open a Story based on the model.
Add a table or chart in story for validation.
Include Company Code and Plant as dimensions.
Authorization is automatically enforced at runtime.

Results

After completing the setup:

Data visibility is restricted at row level.
A single Story can be reused for multiple user groups.
No duplication of Stories or Models is required.
Authorization is centrally managed in SAC.
Import performance remains unchanged.

Best Practices

Enable Data Access Control immediately after model creation.
Use consistent authorization dimensions across models.
Follow clear and business-aligned naming conventions for roles.
Do not rely on CDS DCLs for Import Model security.
Always validate authorization using multiple test users.

Conclusion

When working with SAP Analytics Cloud Import Models based on SAP S/4HANA CDS Views, authorization must be implemented within SAC.

By using:

Data Access Control
Authorization-enabled dimensions
Data Access Roles

you can achieve secure, scalable, and reusable Story-level authorization based on Company Code and Plant fully aligned with standard SAP practices.

SAP Cloud ERP 2602（S/4HANA Cloud Public Edition ）亮点前瞻：分析报表

2026-01-20T02:00:00.078000+01:00

最新S/4HANA Cloud Public Edition 2602版本的发布，为分析带来了强大的功能增强，旨在提升可用性、简化工作流程并提供更深刻的业务洞察。这些新功能对于依赖及时准确数据来驱动业务发展的业务分析专家、财务相关业务人员以及决策者尤为重要。下面我们将深入探讨这个版本的重要功能。

新应用：日记账分录分析器（新版本）
新应用：我的注释
视图浏览器图形化展现
审核手册增强
多维分析增强
【重要】CE2602 弃用的CDS 视图

新应用：日记账分录分析器（新版本）

此次发布的一大亮点是全新的日记账分录分析器应用，是旧版日记账分录分析器的后继版本。新版本经过重新设计，深度集成了强大的多维分析（Multidimensional Analysis）框架，为财务业务人员提供可交互、可追溯、可探索的日记账分析工作台。它不再是静态报表，而是一个支持灵活切片、动态分组与深入下钻的分析环境，显著提升对复杂财务关系与异常模式的发现能力。

亮点功能包含：

多维分析原生集成：深度利用 Multidimensional Analysis 框架，支持任意维度的切片、筛选与聚合，使复杂分析在应用内即可完成。
UI5 组件与计算度量对话框：内置 UI5 交互组件与计算度量对话框，用户可在应用内即时创建自定义计算度量，无需切换工具，方便回答复杂财务问题。
灵活维度与透视分析：支持按分类账（Ledger）、公司代码、会计年度/期间（Fiscal Year/Period）、总账科目（G/L Account）等任意维度组合查看；并可在行/列层面钻取，以透视表（pivot）方式交叉比较与切换数据视图。
明细可追溯性：从汇总层级可直接下钻到单条日记账分录，查看凭证明细、主数据属性与文本，保证分析结果可核查。
结果排序与补充信息：查询结果按公司代码与总账科目排序，便于查看每个科目的过账明细；支持显示主数据属性与文本等补充上下文信息。
用户体验与发现能力：现代化界面、交互切片、计算度量与透视钻取，将静态报表转为面向发现的多维分析工作台，提升分析效率与决策支持能力。

业务与部署相关说明：

New Experience Switch：新版支持“新体验切换”（New Experience Switch），如果用户同时有旧版与新版权限，可在两者间切换使用。
授权与 IAM 应用：为使用户访问此应用，需要分配 IAM 应用：Journal Entry Analyzer - New Version (F0956B_TRAN)。注意：底层 IAM 应用默认以“非激活”方式交付，默认不会向用户显示新体验切换，需要管理员激活并分配。我们强烈建议激活并分配，以便让业务用户知晓并切换到新版。
对现有自动化/测试的影响：启用 New Experience Switch 会导致受影响应用中的信息提示气泡自动展开；如果系统中有测试自动化（如 RPA 机器人），此行为可能影响测试结果。请参照相关指南（Tips for Test Automation）调整测试脚本或配置以规避影响。

总结：新版本在 Multidimensional Analysis 的深度集成、UI5 交互组件与计算度量能力、改进的派生逻辑与检核提示等方面都有显著提升，使日记账分录分析更加动态、可追溯且面向发现，有助于财务团队快速洞察与决策。

新应用：“我的注释”

本次发布引入了全新的“我的注释”应用，作为管理协作反馈和讨论的集中枢纽，显著提升注释的可见性、可追溯性与处理效率。该应用会实时汇总您在启用注释功能的审核手册应用（Review Booklets）和多维分析（Multidimensional Analysis）应用中创建、复制或回复的注释，便于统一查看与管理。主要功能与亮点包括：

同时查看页注释与单元格注释，集中管理所有评论线程；
支持按常见属性检索与筛选（如注释标题、内容、分类、状态），快速定位需要关注的讨论；
支持复制单元格注释的整个讨论线程，便于复用或在其它上下文中引用；
可从注释一键导航回原始报表，快速回到完整的数据上下文进行核对或跟进；
在应用的 Comments 面板中也可直接跳转到“我的注释”，实现不同入口间的无缝流转；
实时显示用户创建的注释，帮助及时跟踪新回复与变更；
注释按讨论线程组织（而非按版本分散显示），便于沿着一条讨论脉络查看历史与当前状态。

视图浏览器图形化展现

试图浏览器应用在本次发布中获得了实用性与可视化两方面的增强，便于开发与业务团队更快理解和定位 CDS 视图及其关联关系。主要改进包括：

图形化视图与依赖关系展示：现在可查看所选 CDS 视图的图形化表示，以及视图之间的依赖链路，直观呈现数据模型结构与上下游关系，便于分析影响面与排查问题。

Fiori Launchpad 搜索条中的 View Browser 搜索模型：将 View Browser 的搜索能力整合到 Fiori Launchpad 的搜索栏，支持自然语言查询，可以查询并展示 CDS 视图的技术细节、业务上下文、建模模式等信息，并支持组合检索，提升查找效率与可发现性。

这些增强使得从业务语义到技术实现的贯通更顺畅——无论是快速定位某个视图的依赖链，还是通过自然语言快速检索相关建模信息，都能显著加快分析、协作与变更影响评估的速度。

审核手册的增强

审核手册（Review Booklets）在新版本中进行了多项实用性与效率提升，主要亮点如下：

自动应用已保存视图：在 My Views 中保存视图时可勾选“自动应用”，更改筛选器后表格会即时刷新，无需手动点击更新。
更灵活的层级显示：支持仅显示根节点或全部节点、按层级或平展展示、显示指定层级深度、将子节点置于父节点上方/下方，以及合并仅含单一子节点的节点，便于按需展开或收拢分层数据。
导出功能：新增“Download Exported Data”按钮，并在“Export Data to Spreadsheet”对话框中增加多项选项，如：

可选择后台导出以处理大数据量、直接或后台下载导出的表格。
可“重复合并单元格的内容”以保证导出表格的可读性。
可为货币或单位生成独立列，便于后续汇总与分析。
在启用评论功能的情况下，可一并导出单元格与页面评论；
选项语义已优化（如将“Show repetitive member names”重命名为“Repeat Content of Merged Cells”），提升导出结果的结构化与可用性。

已忽略的凭证过滤器横幅：界面即时显示哪些过滤器被忽略及其影响，提升报表上下文的透明度与可理解性。

这些改进聚焦于提升分析速度、分层可视化与导出可靠性，特别是导出功能在大数据场景下显著增强了可用性与灵活性。

审核手册设计器增强

2602版本下的审核手册设计器带来更直观、更高效的设计体验，主要亮点如下：

所见即所得（WYSIWYG）保存布局

在预览中直接编辑布局并实时查看效果，支持移除度量/维度/结构、调整行集与列集，完成后选择“Save Layout”并为更改的行集与列集命名，必要时设为默认；保存后这些行列集会出现在表格上方的取值帮助中，便于复用。
可隐藏底部包含“Save Layout”的工具栏以仅查看预览，需时再启用布局编辑模式；同时可在历史记录中查看已保存的布局变更。

增强的“生成内容”（Generate Content）功能

在设计器的 General 页或 Analytical Queries 页的 Add Analytical Queries 区，可选择 Generate Content，系统会基于默认查询自动生成业务页面、行集、列集和变量，您只需对生成内容进行微调即可，大幅加速初始报表构建。
支持通过自定义 InA 服务添加分析查询：在取值帮助中选择自定义 InA 服务后，可预览（Display）、删除或对选定查询执行 Generate Content，自动生成相应的页面/行列集/变量，随后继续调整与配置。

多维分析增强

Multidimensional Analysis 在新版本中对导出能力进行了显著增强，既支持更细粒度的数据结构控制，也提供了适应不同数据量与使用场景的导出方式，便于后续分析、归档与共享。主要亮点包括：

导出目标与默认命名：导出到表格或 PDF，可在对话框指定文件名；在 Multidimensional Analysis 中默认使用查询标题，若从 Review Booklet 导出则默认使用页面名称。
层级展开（Expand Hierarchy）：开启后导出文件会将行列中的层级一路展开到叶子节点，适合需要逐行查看明细的场景。
重复合并单元格内容（Repeat Content of Merged Cells）：可将合并单元格中重复的内容填充到每个单元格，提升导出表格的可读性与加工性。
货币/单位独立列：可将货币码或单位导出到独立列，便于按币种或单位筛选与汇总（仅在度量未放在行上时有效）。
导出评论：支持导出单元格与页面评论（前提是已启用评论功能且用户有相应权限）；页面评论在导出表格中以独立列呈现。
两种导出方式可选：

后台导出（推荐大数据量）：异步将导出文件生成到服务器，完成后系统通知并需从服务器下载，避免阻塞前端操作；
直接导出：导出文件直接下载到本地，适合小数据量快速获取。

注意事项与限制：若选择“货币/单位独立列”或启用“导出评论”，则不能使用后台导出；相关选项在对话框中清晰呈现，便于根据数据量与需要选择最优方式。
PDF 导出同样支持重复成员名/合并单元格的处理选项，保证在不同输出格式下都能获得结构化且可读的报表。

【重要】CE2602 弃用的CDS 视图

在CE2602版本中，以下CDS视图将被弃用。

名称	描述
C_RAOPENREVENUEPERPERIODQRY_2	Rmng Perf Oblgn w Time Bands - Query
I_RAOPENREVENUEPERPERIODCUBE_2	Rmng Perf Oblgns wt Time Bands - Cube
I_UNITOFMEASURECOMMERCIALNAME	Unit of Measure Assignment of commercial to internal
I_UNITOFMEASURETECHNICALNAME	Unit of Measure Assignment of technical to internal

用户需及时根据系统内的使用情况，将正在使用的弃用CDS视图更新为其后续版本。

如何及时发现受影响的CDS视图？请参考下方的博文。

SAP S/4HANA Cloud Public Edition ：如何及时发现受影响的项目并采取升级行动？

更多关于 SAP S/4HANA Cloud Public Edition分析报表话题:

SAP S/4HANA Cloud嵌入式分析业务用例汇总及简介查看
SAP S/4HANA Cloud公有云版本中三系统开发扩展性与二系统关键用户扩展性报表开发对比查看

Learn How to Build Analytical Queries Using ABAP CDS

2026-02-18T13:04:50.160000+01:00

The data foundation for SAP S/4HANA embedded analytics is the Virtual Data Model (VDM). The VDM is built using ABAP CDS views that form a stack. The stack starts at the bottom with dimension and fact CDS views reading from ABAP tables. The next layer is the cube that combine the dimensions and measures. Finally we have the analytical queries that are built using a special type of ABAP CDS view called a CDS analytical projection view and they sit at the top layer.

SAP provide thousands of CDS views for all the levels of the stack, out-of-the-box and for all application areas. So why would you want to learn how to build them if SAP already provide them?

Firstly, it is helpful to be able to explore the SAP-supplied CDS views and make sense of them. To do this you need to understand the special syntax that is used. Secondly, you may want to develop your own custom CDS views from scratch for consumption by the analytics tools of SAP S/4HANA. And thirdly, SAP Business Data Cloud consumes ABAP CDS views to generate Data Products. So being familiar with this technology is a very useful skill to have in a BDC project.

In the learning journey Discovering SAP S/4HANA embedded analytics we dip our toe in the water and introduce you to the CDS views of the virtual data model. But we don't go into detail.

For those who want to really dig deep into this topic we developed the learning journey:
Developing Analytical Queries using ABAP CDS

In this learning journey you will learn how to to create your own analytical queries using ABAP CDS from scratch. There are lots of videos and code snippets that show you how to express your query requirements.

We even provide you with all the code that is used in the learning journey so you can follow along in your own system.

As well as the learning journey, we provide the following Live Sessions that bring to life various aspects of analytical query building.

Develop Queries using CDS Analytical Projection Views

Building Cubes using ABAP CDS Views

Developing Hierarchies using ABAP CDS

From Cube to Analytical App in 10 minutes Using the ADT Generator

We highly recommend you attend these planned Live Sessions (or watch the recordings) to help you quickly develop your skills and to ask questions and learn more about planned enhancements.

Developing analytical queries using ABAP CDS is a hot topic. Many customers need help and this is why we created this learning journey. Remember, the virtual data model of SAP S/4HANA depends on ABAP CDS views. Understanding how they work will ensure you are a much sought after member of your SAP project team.

What is SAP S/4HANA Embedded Analytics and How Can I Develop My Skills?

2026-02-19T12:48:09.432000+01:00

SAP's ERP applications have always provided out-of-the-box reports and tools to develop custom reports. If you have been around as long as I have you in the SAP world you will remember SAP R/3 and it successor ECC that provided reporting tools such as LIS and CO-PA. Back then SAP also provided a large number of static, ready-made ABAP reports. Reporting was regarded as a separate function to transactional processing. First work through a transaction, then look back and evaluate the data you created using reports. There was usually a delay, often overnight, while the transaction data was extracted and prepared then copied into a readable and summarized structure with batch processes having to run before the users could get at the reports.

But today, with the latest evolution of SAP's ERP suite, SAP S/4HANA, reporting has evolved into embedded analytics which provides insights to the business user in real-time and throughout the transactional processes, not just at the end.

Unlike previous tools, there is no redundant copy of the data. All analytics come straight from the live transaction and master data tables. Instead of static reports we now have sophisticated dashboards that provide flexible, ad-hoc drill-downs from a top level KPI down to the individual live transactions.

Embedded Analytics is an included component of SAP S/4HANA on-premise, private cloud and public cloud.

Embedded Analytics can be broken down into two main components: content and tools.

Content: There are actually two types of content.

1) A Data Model: SAP provide a complete data model covering all business areas. This is the foundation for all analytics and is known as the Virtual Data Model (VDM). It is built using ABAP Core Data Services (CDS) views that provide real-time access to all operational data without redundancy. They are built in a stack so that each view is reusable and they cover master data and transaction data..

2) Analytical Content: SAP provides ready-to-use analytical content such as business KPIs, dashboards and queries for the business user to use right away in their line of business. The analytical content is embedded within the end-to-end business processes.

Tools:
Embedded Analytics includes developer tools to create brand-new analytical content, such as KPIs, queries, and dashboards. The tools can also be used to extend the SAP-delivered content such as adding extra fields to queries. These tools are mostly graphical tools so they are easy to learn and accessible by the business users who do not have to rely on IT.

A key thing to remember it that the focus of SAP S/4HANA Embedded Analytics is operational analytics within the SAP S/4HANA system. SAP S/4HANA Embedded Analytics does not combine data outside of S/4HANA. Embedded Analytics is not a data warehouse for collecting historical data, like SAP BW. The idea behind embedded analytics is to provide real-time access to analytics throughout the business processes of SAP S/4HANA so that optimal decisions can be made at the moment of risk or opportunity. Focus on the word embedded which means embedded in the business processes. What this means in reality is that analytical information appears on the same screen as the transaction, right where you need it. For example, imagine you are creating a purchase order for a missing component needed urgently in your assembly plant. You need to find a local, reliable supplier who stocks the component. In this case, the real-time analytics relating to suppliers will appear right in front of you without having to leave the purchase order, to help you immediately choose the best supplier.

SAP S/4HANA embedded analytics touches all business processes such as order-to-cash, procure-to-pay, hire-to-retire and many more so it is relevant for everyone who works with SAP S/4HANA.

With the buzz around SAP Business Data Cloud (BDC), you might be wondering where S/4HANA embedded analytics fits. Is S/4HANA embedded analytics replaced by BDC? The answer is no - BDC does not replace S/4HANA embedded analytics.

SAP Business Data Cloud is a data platform that brings all data (SAP and non-SAP) together into a unified storage architecture. Data is piped to BDC from all applications, including SAP S/4HANA. Data is physically copied from S/4HANA to BDC storage and combined with all data across the organization. The focus of S/4HANA embedded analytics is to provide analytics on live operational data (not copied to a central store) that is deeply embedded in the ERP business processes so that it can be used at the time of a transaction. Whereas BDC is a holistic data platform that focuses on combining data from all applications (SAP and non-SAP from all LoBs) in a central data store (data lake). That data is then enriched and used for AI development and strategic analytics. In short, S/4HANA embedded analytics and BDC have different uses cases. you will get a better perspective if you learn a little about SAP BDC.

So back to S/4HANA embedded analytics - how do you get started developing your skills?

You should start by working through the free-of-charge learning journey Discovering SAP S/4HANA embedded analytics

We also have a instructor-led version of the content if you prefer to learn in a virtual classroom with others. S4H400 - SAP S/4HANA Embedded Analytics Foundation | SAP Training
(Note: there is a fee for this).

Want to develop skills on a real system and follow along with the course exercises? No problem! Get access to a fully configured SAP S/4HANA practice system and follow the included exercise scripts from the course, or, as Fleetwood Mac sang, you can go your own way and work on your own ideas. (require a subscription to learning.sap.com)

For a deeper dive on selected topics, join the regular Live Sessions where you can interact with the experts. You can also access the recordings of past sessions (requires a subscription to learning.sap.com).
Getting Started with Review Booklets in SAP S/4HANA Embedded Analytics
Develop Queries using CDS Analytical Projection Views

And don't forget to ask your questions at any time in this learning community where you have access to the experts who create and deliver the learning content for all data and analytics topics. They are here to support your learning!

Get learning today and build your skills in S/4HANA embedded analytics and add this popular topic to your resume to increase your value in the SAP ecosystem.