00 — What Is SAP S/4HANA Embedded Analytics?
Masterclass position: Phase 00 — Embedded Analytics: The Big Picture
This tutorial is intentionally limited to subtopic 00. The next subtopic, 00.1 — Why Embedded Analytics Exists, will build on the mental model established here.
1. Learning Objectives
By the end of this tutorial, you should be able to:
- define SAP S/4HANA Embedded Analytics precisely;
- explain why analytics can be delivered inside the S/4HANA operational system;
- describe the high-level architecture from business data to analytical consumption;
- explain the role of SAP HANA, ABAP CDS, the Virtual Data Model (VDM), analytical queries, and analytical applications;
- distinguish the data foundation, semantic/modeling layer, analytical runtime, and consumption layer;
- understand the basic idea of real-time operational analytics and insight-to-action;
- explain why Embedded Analytics is different from simply running SQL against S/4HANA tables;
- understand the boundaries between Embedded Analytics, BW/4HANA, SAP Datasphere, and SAP Analytics Cloud;
- identify the responsibilities of business users, analytics/key users, developers, and architects;
- reason about a simple enterprise scenario using an architect's mental model.
2. Prerequisites
None.
This is the entry point of the masterclass. You do not need prior knowledge of:
- ABAP CDS
- S/4HANA VDM
- analytical queries
- SAP Fiori analytics
- SAP Analytics Cloud
- BW/4HANA
- SAP Datasphere
- OData
Those concepts will be established progressively in later phases.
3. Start With the Business Problem
Imagine a procurement manager opens S/4HANA at 10:00 AM.
A purchase order has just been created. A supplier has missed a delivery date. A material is approaching a shortage threshold.
The manager does not want a report from yesterday.
The manager wants to answer questions such as:
- Which purchase orders are overdue right now?
- Which suppliers are causing the largest delivery delays?
- Which materials are at risk?
- What is the open purchasing value?
- Which plant is affected?
- Can I navigate from the analytical result into the operational process and act?
This creates a fundamental requirement:
Use the operational business data to understand what is happening now, and keep the user close to the business process.
SAP S/4HANA Embedded Analytics addresses this class of requirement by providing analytics capabilities within the S/4HANA environment, using the S/4HANA data model and analytical content rather than requiring the operational data to first be copied into a separate enterprise warehouse for every operational reporting use case. SAP describes Embedded Analytics as combining transactions and analytics in the same system, with the VDM/CDS layer providing the semantic access path to business data.
4. What Is SAP S/4HANA Embedded Analytics?
The short definition
SAP S/4HANA Embedded Analytics is the set of analytical capabilities integrated into SAP S/4HANA that allows business users and technical teams to analyze S/4HANA business data through a semantic analytical model and analytical applications, typically using the Virtual Data Model (VDM) built with ABAP CDS views.
The important words are:
| Term | Meaning |
|---|---|
| S/4HANA | The operational ERP platform containing business processes and business data |
| Embedded | Analytics capabilities are integrated with the S/4HANA application environment rather than being inherently dependent on a separate analytical database |
| Analytics | Capabilities for reporting, KPI analysis, multidimensional exploration, dashboards, and related analytical use cases |
| VDM | SAP's semantic virtual data model built from CDS views |
| CDS | The modeling technology used to define semantically rich data access models |
| Analytical application | The application through which a user consumes analytical information |
SAP states that the VDM forms the basis for data access in S/4HANA, including analytical applications and APIs, and that the VDM is represented by CDS views.
5. What Embedded Analytics Is Not
This distinction is essential.
5.1 It is not simply "SQL reporting"
A developer could query database tables directly.
That does not automatically create a good enterprise analytics architecture.
Raw ERP tables are technical structures. Business analytics needs concepts such as:
- business meaning;
- relationships;
- dimensions;
- measures;
- semantics;
- currency and unit meaning;
- reusable models;
- authorizations;
- analytical behavior;
- consumer-specific metadata.
SAP's VDM exists partly because raw S/4HANA tables are not intended to be the primary business-consumption interface for analytics. SAP describes the VDM as a semantic layer that makes business data easier to consume.
5.2 It is not automatically a data warehouse
Embedded Analytics is optimized for analytical access to the S/4HANA operational context.
It does not eliminate the need for:
- historical enterprise analytics;
- multi-system integration;
- cross-domain data products;
- long-term analytical persistence;
- enterprise data warehousing;
- broader non-SAP data integration.
Those architectural boundaries will be developed later in the masterclass.
5.3 It is not the same thing as SAP Analytics Cloud
SAP Analytics Cloud can consume analytical data from S/4HANA, including live scenarios.
But S/4HANA Embedded Analytics and SAP Analytics Cloud occupy different architectural positions.
A useful first mental model is:
Embedded Analytics is primarily about analytical capabilities around S/4HANA business data and processes. SAC is a separate cloud analytics platform that can consume and analyze data from S/4HANA and many other sources.
SAP documents both live and imported data scenarios for SAC, while Embedded Analytics provides analytical capabilities directly within the S/4HANA environment.
6. The First Mental Model
Do not memorize product names yet.
Think in layers:
┌──────────────────────────────────────────────────────────┐
│ BUSINESS USER │
│ Monitor → Analyze → Drill → Decide → Act │
└──────────────────────────┬───────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────┐
│ ANALYTICAL CONSUMPTION │
│ Fiori analytical apps | Queries | Data Analyzer | SAC │
│ AFO | other analytical consumers │
└──────────────────────────┬───────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────┐
│ SEMANTIC / ANALYTICAL MODEL │
│ ABAP CDS → Virtual Data Model → Query │
│ │
│ Business meaning | relationships | measures | semantics│
└──────────────────────────┬───────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────┐
│ S/4HANA DATA │
│ Transaction data | Master data | Business objects │
└──────────────────────────┬───────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────┐
│ SAP HANA │
│ Persistent operational data + database processing │
└──────────────────────────────────────────────────────────┘
This is the first architecture you should carry forward.
SAP's learning material describes the core architecture as a HANA database foundation, a VDM/CDS middle layer, and analytical applications at the top.
7. The Three-Layer Architecture
For the first phase, simplify the architecture into three major layers.
Layer 1 — SAP HANA Data Foundation
S/4HANA uses SAP HANA as its database platform.
At the bottom are the persisted business data structures used by the S/4HANA application.
Examples include data associated with:
- sales;
- procurement;
- finance;
- inventory;
- manufacturing;
- business partners;
- products and materials.
At this level, the data is primarily organized for the application and its business processes.
Important: the database layer is not the business-friendly analytical interface.
Layer 2 — VDM / CDS Semantic Layer
The VDM sits above the database structures.
SAP describes the VDM as a structured semantic model built from CDS views. It exposes business data in a form that is easier to consume and supports reuse across analytical applications and APIs.
Conceptually:
Technical database structures
↓
ABAP CDS Views
↓
Virtual Data Model
↓
Business meaning
This layer is one of the most important concepts in the entire masterclass.
Later phases will go deeply into:
- CDS;
- VDM layers;
- associations;
- semantics;
- annotations;
- analytical modeling;
- analytical queries.
For now, remember:
The VDM is the semantic bridge between S/4HANA's technical data structures and analytical consumption.
Layer 3 — Analytical Consumption
The upper layer is where users consume the information.
Depending on the scenario, users may work with:
- SAP Fiori analytical applications;
- analytical queries;
- Data Analyzer;
- Multidimensional Reports;
- Review Booklets;
- SAP Analytics Cloud;
- Analysis for Office;
- other supported analytical consumers.
SAP Learning explicitly identifies analytical applications and the enterprise-wide VDM as the two major components of Embedded Analytics, with different tools supporting business users, analytics experts, and IT/developer personas.
8. Where Does the Analytical Engine Fit?
A common beginner mistake is to assume:
CDS View = complete analytical runtime.
That is too simplistic.
For analytical query scenarios, SAP documents an Analytic Engine that evaluates and executes analytical queries at runtime.
A simplified runtime picture is:
User
│
▼
Analytical Consumer
│
▼
Analytical Query
│
▼
Analytic Engine
│
▼
CDS / VDM
│
▼
S/4HANA / HANA
This distinction becomes extremely important later when we study:
- analytical cubes;
- analytical queries;
- measures;
- restricted measures;
- calculated measures;
- exception aggregation;
- query execution;
- performance.
Do not try to learn those details yet.
9. Why "Virtual" Data Model?
The word virtual is important.
It does not mean:
"There is no data."
It means the VDM represents a semantic data model that is primarily defined through views rather than being a separate persistent warehouse copy of the business data.
SAP describes the VDM as virtual because the semantic model differs from the traditional persistent database model; the consuming application can access business data through the model without needing to understand the underlying tables.
Think of it like this:
Physical reality
────────────────────────────────────
S/4HANA tables and persisted data
Business interpretation
────────────────────────────────────
"Supplier"
"Purchase Order"
"Material"
"Plant"
"Open Amount"
"Delivery Date"
Virtual Data Model
────────────────────────────────────
CDS views that express those
business concepts and relationships
The VDM therefore gives us a business-oriented interface over technical data.
10. CDS Is the Modeling Foundation
At this stage, you only need one conceptual definition.
ABAP CDS is the SAP modeling technology used to define semantically rich data models that form the foundation of the S/4HANA VDM.
A CDS view can describe more than:
SELECT column1, column2
FROM table
The CDS model can also express concepts such as:
- relationships;
- semantics;
- annotations;
- business meaning;
- reusable modeling structures;
- analytical behavior where applicable.
SAP describes CDS as an infrastructure for creating semantically rich data models and identifies CDS views as the basis of the S/4HANA VDM.
You will learn the actual syntax much later.
11. Basic Example — Purchase Orders
Suppose an organization wants:
"Show open purchase order value by supplier."
A naïve approach might look like:
Purchase Order Table
+
Supplier Table
+
Item Table
+
Status Logic
+
Currency Logic
↓
Report
But an enterprise analytical model aims to provide a reusable business representation.
Conceptually:
S/4HANA business data
│
▼
CDS / VDM
│
├── Purchase Order
├── Supplier
├── Material
├── Company Code
└── Currency semantics
│
▼
Analytical model
│
▼
"Open Purchase Order Value by Supplier"
The important lesson is not the syntax.
The important lesson is:
The report is built on a reusable semantic model rather than forcing every consumer to rediscover the underlying ERP tables.
12. Intermediate Example — Procurement Performance
Now increase the complexity.
Requirement:
"Identify suppliers with delayed deliveries and compare open purchase order value, ordered quantity, and average delivery delay by purchasing organization."
Now the analytical model needs multiple business concepts:
PROCUREMENT ANALYTICS
│
┌─────────────────┼─────────────────┐
▼ ▼ ▼
Supplier Purchase Order Delivery
│ │ │
└─────────────────┼─────────────────┘
▼
Semantic Model
│
┌───────────┼───────────┐
▼ ▼ ▼
Measures Dimensions Semantics
│ │ │
Open Value Supplier Currency
Quantity Plant Unit
Delay POrg Date
│
▼
Analytical Query
│
▼
Fiori / SAC / AFO / etc.
The model now has to understand not only data retrieval but business meaning.
This is why Embedded Analytics is an architectural topic rather than merely a reporting tool.
13. Advanced Example — Insight to Action
Now consider a real operational scenario.
Requirement
A procurement manager wants to:
- see suppliers with abnormal delivery delays;
- filter by purchasing organization;
- identify the affected purchase orders;
- navigate to the relevant operational transaction;
- take corrective action.
The flow becomes:
BUSINESS PROCESS
│
▼
S/4HANA Transactions
│
▼
Operational Data
│
▼
CDS / VDM
│
▼
Analytical Processing
│
▼
Analytical Consumer
│
▼
Business Insight
│
▼
Drill / Navigation
│
▼
Operational Action
This is the beginning of the insight-to-action mental model.
SAP describes the ability to navigate between transactional and analytical applications in S/4HANA, supporting real-time insight-to-action without requiring data replication for that use case.
14. Enterprise Example — Procurement Control Tower
Consider a global enterprise with:
- 8 countries;
- 20 plants;
- thousands of suppliers;
- millions of purchasing document items;
- centralized procurement governance.
The business wants:
"Give procurement managers a current view of purchasing exposure, supplier performance, and delivery risk, and allow them to investigate the underlying operational documents."
An architect might start with:
USERS
│
┌────────────┼────────────┐
▼ ▼ ▼
Fiori SAC AFO
│ │ │
└────────────┼────────────┘
▼
Analytical Consumption
│
▼
Analytical Query /
Analytical Model
│
▼
CDS / VDM
│
┌──────────────┼──────────────┐
▼ ▼ ▼
Purchasing Supplier Material
semantics semantics semantics
│ │ │
└──────────────┼──────────────┘
▼
S/4HANA Business Data
│
▼
HANA
But the architect should not immediately conclude that everything belongs in Embedded Analytics.
The next questions are:
- Is this operational or historical?
- Does it require only S/4HANA data?
- Are multiple source systems involved?
- Is long-term history required?
- Is enterprise harmonization required?
- Is data replication justified?
- Should SAC consume the S/4HANA model directly?
- Would BW/4HANA or Datasphere provide a better enterprise architecture?
Those decisions are intentionally deferred to later phases.
15. Embedded Analytics vs Traditional Reporting
A useful conceptual comparison:
| Traditional reporting mindset | Embedded Analytics mindset |
|---|---|
| Start from tables | Start from business meaning |
| Report-specific data extraction | Reusable semantic models |
| Reporting often separated from transactions | Analytics integrated with operational processes |
| Data may be copied to another system | Live access can be used where appropriate |
| Technical structures exposed to reporting logic | VDM abstracts technical structures |
| Reporting is often a downstream activity | Analytics can participate directly in the business process |
This does not mean traditional reporting architectures are obsolete.
It means the architectural question has changed:
Where should this analytical workload live, and what data architecture best satisfies its business and non-functional requirements?
That question is the foundation of the architect track.
16. Embedded Analytics vs BW/4HANA — First Boundary
Do not memorize a product comparison yet. Use workload characteristics.
Embedded Analytics is a strong candidate when:
- the primary data is in S/4HANA;
- the business needs operational reporting;
- low latency matters;
- the analytical context is closely tied to the operational process;
- real-time insight-to-action is important.
BW/4HANA becomes important when requirements include:
- enterprise data warehousing;
- multiple source systems;
- historical persistence;
- harmonization across systems;
- governed enterprise reporting;
- warehouse-specific transformation and lifecycle requirements.
The masterclass will make this decision framework explicit in Phase 23.
17. Embedded Analytics vs Datasphere — First Boundary
SAP Datasphere addresses broader data integration, semantic modeling, and enterprise data scenarios across sources.
A simplified mental model:
S/4HANA Embedded Analytics
│
│ operational analytics
│
▼
S/4HANA
│
│ integration / federation /
│ replication as appropriate
▼
Datasphere
│
┌────────┼─────────┐
▼ ▼ ▼
SAP Non-SAP Enterprise
sources sources analytics
The architect's question is not:
"Which product is better?"
It is:
"Where should the analytical responsibility live?"
That depends on data scope, latency, persistence, integration, governance, and consumption requirements.
Phase 24 will build this decision framework.
18. Embedded Analytics vs SAP Analytics Cloud
Think of the distinction as:
S/4HANA
│
├── Embedded analytical applications
│
└── Analytical models / queries
│
└──────────────► SAC
│
├── Stories
├── Dashboards
├── Planning
└── Cross-source analytics
SAC can act as a consumer of S/4HANA analytical content, including live scenarios. SAP also supports imported-data scenarios, so SAC should not be mentally reduced to "just a live S/4HANA screen."
19. Who Uses Embedded Analytics?
SAP identifies different user personas around Embedded Analytics, including business users, analytics experts/key users, and IT administrators/developers.
Business User
Typical concern:
"What is happening in my business?"
They consume:
- KPIs;
- reports;
- analytical applications;
- multidimensional analysis;
- drilldowns.
Analytics Expert / Key User
Typical concern:
"Can I create or adapt analytical content?"
They may work with:
- queries;
- KPIs;
- reports;
- dashboards;
- analytical configurations.
Developer
Typical concern:
"How do I create the underlying reusable analytical model?"
They work with technologies such as:
- CDS;
- VDM;
- analytical models;
- services;
- extensions.
Architect
Typical concern:
"Should this analytical requirement be implemented here at all, and if so, what is the right architecture?"
This is the perspective the masterclass ultimately develops.
20. What Happens at Runtime?
Keep the first runtime model simple.
Suppose a user requests:
"Open purchase order value by supplier."
A simplified flow is:
1. User opens analytical application
│
▼
2. Consumer requests analytical data
│
▼
3. Analytical query/model is resolved
│
▼
4. Analytical runtime processes the request
│
▼
5. CDS / VDM resolves the semantic model
│
▼
6. HANA processes the underlying data access
│
▼
7. Result is returned
│
▼
8. Consumer renders the analytical result
This is intentionally simplified.
Later phases will distinguish:
- CDS activation;
- metadata;
- analytical query definition;
- analytic engine processing;
- SQL execution;
- filter pushdown;
- aggregation;
- authorization;
- consumer-specific behavior.
21. Why This Architecture Matters
Without a semantic layer, every analytical consumer could independently implement:
- table joins;
- business rules;
- calculations;
- text handling;
- currency logic;
- unit logic;
- authorization assumptions.
That creates multiple versions of the truth.
The VDM provides a reusable semantic foundation.
WITHOUT REUSE
Report A ──► Tables
Report B ──► Tables
Report C ──► Tables
SAC ──► Tables
AFO ──► Tables
Business logic duplicated
↓
Inconsistency
↓
Maintenance complexity
WITH VDM
CDS / VDM
│
┌────────────┼────────────┐
▼ ▼ ▼
Fiori SAC AFO
Shared semantic foundation
↓
Reuse
↓
Consistency
This is one of the most important architectural reasons the VDM exists.
22. Common Beginner Mistakes
Mistake 1 — "Embedded Analytics means reporting directly on tables"
Why it is wrong:
Embedded Analytics is built around a semantic model and analytical content, not merely unrestricted table access.
Better mental model:
Database data → semantic model → analytical consumption.
Mistake 2 — "VDM is another database"
Why it is wrong:
VDM is a virtual semantic model represented through CDS views.
Better mental model:
VDM describes how business data should be understood and consumed.
Mistake 3 — "CDS and VDM are unrelated"
Why it is wrong:
The S/4HANA VDM is represented by CDS views.
Mistake 4 — "SAC equals Embedded Analytics"
Why it is wrong:
SAC is a separate cloud analytics platform that can consume S/4HANA analytical content.
Mistake 5 — "Embedded Analytics eliminates BW/4HANA and Datasphere"
Why it is wrong:
Different analytical architectures solve different problems.
Mistake 6 — "Real time means every analytical workload should query S/4HANA directly"
Why it is wrong:
Real-time access is valuable, but workload isolation, historical requirements, source-system scope, performance, governance, and enterprise architecture still matter.
23. Troubleshooting Mental Model — The First Version
At this stage, troubleshooting should be architectural rather than technical.
If a business user says:
"My analytical report is wrong."
Do not immediately change CDS code.
Start with:
SYMPTOM
↓
Is the requirement understood?
↓
Is the correct analytical content being used?
↓
Is the correct semantic model being consumed?
↓
Is the data itself correct?
↓
Are filters / authorizations involved?
↓
Is aggregation correct?
↓
Is the consumer interpreting the model correctly?
Later phases will turn this into a detailed troubleshooting framework.
24. Performance — First Principles
Performance begins with architecture.
A common misconception is:
"Because S/4HANA runs on HANA, every analytical query will automatically be fast."
That is not a safe engineering assumption.
Performance can be affected by:
- data volume;
- joins;
- associations;
- cardinality;
- filtering;
- aggregation;
- calculations;
- query design;
- authorization;
- consumer behavior;
- network/API overhead;
- unnecessary fields;
- inefficient modeling.
The performance principle for this masterclass is:
Push the right work to the right layer, reduce unnecessary data movement, and design reusable models that allow the underlying platform to execute efficiently.
SAP's CDS and VDM architecture is explicitly intended to improve consumability and performance, but model design still matters.
Detailed performance engineering is Phase 19.
25. Security — First Principles
Embedded Analytics does not mean:
"If the user can access S/4HANA, they can see every analytical record."
Analytical access must respect the S/4HANA authorization architecture and the security controls associated with the analytical model and consuming application.
For example:
Same analytical model
│
├── User A → permitted organizational scope
│
└── User B → different organizational scope
The result may legitimately differ between users.
Security and CDS access control are covered systematically in Phase 16.
26. Integration — First Principles
Embedded Analytics also does not exist in isolation.
An enterprise may have:
S/4HANA
│
├── Fiori
├── SAC
├── AFO
├── BW/4HANA
├── Datasphere
├── Data Lake / Lakehouse
└── Non-SAP applications
The integration architecture determines whether data is:
- accessed directly;
- exposed through an API;
- replicated;
- extracted;
- federated;
- consumed through an analytical connector;
- integrated through events or other mechanisms.
The dedicated integration architecture phase is Phase 15.
27. Architect's Mental Model
If you remember only one diagram from this tutorial, remember this:
BUSINESS NEED
│
▼
┌──────────────────┐
│ S/4HANA PROCESS │
└────────┬─────────┘
│
▼
┌──────────────────┐
│ HANA DATA │
│ FOUNDATION │
└────────┬─────────┘
│
▼
┌──────────────────┐
│ CDS / VDM │
│ SEMANTIC MODEL │
└────────┬─────────┘
│
▼
┌──────────────────┐
│ ANALYTICAL │
│ MODEL / QUERY │
└────────┬─────────┘
│
┌───────────┼───────────┐
▼ ▼ ▼
Fiori SAC AFO
│ │ │
└───────────┼───────────┘
▼
INSIGHT
│
▼
ACTION
An architect then asks:
- Is this an operational analytical requirement?
- Is S/4HANA the authoritative source?
- Does the requirement need live data?
- Can the requirement be satisfied using standard VDM content?
- Is custom CDS required?
- Which analytical consumer is appropriate?
- What authorization model is required?
- What performance characteristics are required?
- Does the requirement actually belong in BW/4HANA or Datasphere?
- Does the solution need integration with non-SAP data?
That is the beginning of architectural thinking.
28. Release and Edition Awareness
Embedded Analytics exists across S/4HANA deployment models, but capabilities and implementation approaches can differ.
At minimum, distinguish:
- SAP S/4HANA On-Premise
- SAP S/4HANA Cloud Private Edition
- SAP S/4HANA Cloud Public Edition
SAP's current documentation describes the VDM/CDS foundation across these environments, while the exact available content, extensibility options, applications, and supported development approaches can vary by edition and release.
For example:
- current SAP S/4HANA on-premise documentation includes release-specific VDM content such as 2025 FPS02;
- SAP S/4HANA Cloud Public Edition documentation is release-specific and currently exposes release-level documentation such as 2608.
Therefore, throughout this masterclass:
Never assume that an implementation detail documented for one S/4HANA edition automatically applies unchanged to another.
Where a later tutorial contains release-sensitive syntax, annotations, APIs, tools, or extensibility behavior, the applicable release will be identified explicitly.
29. Standard Content vs Custom Content
One of the first architect decisions is:
Can we reuse SAP-delivered content?
SAP provides a large VDM and analytical content library. The View Browser allows users to explore available CDS views, and SAP documents released/reusable CDS content through its VDM and stability-contract mechanisms.
A strong implementation sequence is therefore:
Business Requirement
↓
Search standard analytical content
↓
Can existing content satisfy it?
│
┌───┴───┐
YES NO
│ │
Reuse Analyze gap
│
▼
Extend / create
appropriate model
Avoid custom development simply because creating a new view feels easier.
30. Practical Exercise — Architecture Only
No coding yet.
Scenario
A company wants a dashboard for:
Open Purchase Orders by Supplier and Plant
Requirements:
- current operational data;
- supplier;
- plant;
- purchasing organization;
- open purchase order value;
- drilldown to purchasing documents;
- business users work in S/4HANA;
- no requirement for multi-system historical analytics has been stated.
Your task
Draw the architecture using these boxes:
Business User
↓
Consumer
↓
Analytical Model / Query
↓
CDS / VDM
↓
S/4HANA Business Data
↓
HANA
Then answer:
- Why should the report not start by directly joining raw tables?
- What role does CDS play?
- What does VDM contribute?
- Why might Embedded Analytics be a suitable starting architecture?
- What additional requirement could make BW/4HANA or Datasphere more appropriate?
Do not write CDS code yet.
The goal of this exercise is to establish the architecture-first mindset.
31. Knowledge Check
Try answering these without looking back.
Question 1
What does "embedded" mean in Embedded Analytics?
Question 2
What is the role of the VDM?
Question 3
Why isn't direct table access the preferred business-consumption model?
Question 4
Where does CDS fit?
Question 5
Where does the analytical engine fit?
Question 6
What is the difference between Embedded Analytics and SAC?
Question 7
Why does Embedded Analytics not eliminate BW/4HANA or Datasphere?
Question 8
What is the difference between a developer's question and an architect's question?
Question 9
What does "virtual" mean in Virtual Data Model?
Question 10
Describe the end-to-end flow from business requirement to insight.
32. Key Takeaways
If you remember only ten things from 00, remember these:
- Embedded Analytics brings analytical capabilities into the S/4HANA operational environment.
- SAP HANA is the database foundation.
- The VDM is the semantic data-access layer built from CDS views.
- CDS is fundamental to S/4HANA's semantic modeling approach.
- Analytical queries can be evaluated by the Analytic Engine at runtime.
- Fiori and other analytical consumers sit above the analytical model.
- Real-time operational analytics is a major Embedded Analytics use case.
- Embedded Analytics is not synonymous with SAC, BW/4HANA, or Datasphere.
- Architecture decisions depend on latency, data scope, history, integration, security, performance, and governance.
- The goal is not merely to build a report; it is to build the right analytical architecture.
33. What You Should Know Before 00.1
Before continuing, you should be able to explain this sentence in your own words:
"SAP S/4HANA Embedded Analytics uses S/4HANA business data, exposed through a semantic CDS/VDM layer and consumed through analytical capabilities and applications, enabling operational analytics close to the business process."
You should also be comfortable drawing:
S/4HANA / HANA
↓
CDS / VDM
↓
Analytical Model / Query
↓
Fiori / SAC / AFO / Other Consumers
↓
Insight → Action
If that model is clear, you are ready for:
00.1 — Why Embedded Analytics Exists
34. Research Basis
This tutorial was researched and cross-checked against current SAP material before writing.
Primary SAP sources
- SAP Help — Virtual Data Model and CDS Views in SAP S/4HANA
- SAP Learning — Describing Embedded Analytics
- SAP Learning — Identifying Key Aspects of SAP S/4HANA Embedded Analytics
- SAP Learning — Describing SAP S/4HANA Embedded Analytics
- SAP Learning — Listing the Key Components of SAP S/4HANA Embedded Analytics
- SAP Help — Custom CDS Views
- SAP Help — Manage KPIs and Reports
Community cross-check
Research principle: SAP Help and SAP Learning take precedence over secondary explanations for SAP behavior and terminology.