Exploring SAP Analytic Cloud Reports


After completing this lesson, you will be able to:

  • Describe the environment of margin analysis in SAP S/4HANA

Describe the Environment of Margin Analysis in SAP S/4HANA

What Is Margin Analysis?

To better understand how margin analysis works, let's use the example of Bike Company.

Suppose that the company sells three types of bicycles: mountain bikes, road bikes, and electric bikes. By using margin analysis, the company can determine the profitability of each bicycle type. They consider the direct costs (such as raw materials and labor) and indirect costs (such as overhead expenses) associated with their production. This information helps Bike Company to make informed decisions about pricing, production volume, and resource allocation for each bicycle type.

Within SAP S/4HANA, margin analysis is the tool that enables us to measure profit margins across various market segments, such as product types, customer groups, and sales regions.

Have you ever spent days trying to provide a report for how a product is performing in the local market? Then, your manager complains that they don’t understand how you came up with the numbers and can’t really understand the data presented. Not to mention that you must provide the data, excluding the previous financial period, because closing isn’t yet finished?

In SAP S/4HANA all financial data is stored in real time on a detailed line-item basis to the universal journal. All actual data relating to the profitability segments of margin analysis are stored in the same universal journal (ACDOCA) used across all finance. Therefore, you can always trust the data to be true to their source, skipping complex and time consuming data reconciliations. In margin analysis, costs and revenues are grouped using cost and revenue general ledger accounts. Anyone can expand a report and drill down to the detail-line items that make up a figure.

Let’s look at an example of how margin analysis is updated across the steps of the order, to cash business process for a make-to-stock scenario.

The image illustrates the Order-to-Cash process in a Make-to-Stock scenario, depicting the flow of financial information from quotation to payment. The process includes stages such as sales order, delivery/goods issue, and invoicing. The diagram also showcases the financial impact of each stage on various accounts, such as receivables, revenues, and statistical warranty costs. The values associated with these accounts are recorded in the standard ledger (ACDOCA) and extension ledger, which are used for margin analysis. The presence of specific values like 100.00 for receivables and revenues, and 3.00 and 6.00 for statistical warranty costs and reserves, respectively, provides a numerical representation of the financial flow throughout the Order-to-Cash process.

The Order-to-Cash process covers all the steps from receiving a customer order to the point where billing data is recorded as line items in the universal journal (ACDOCA). Upon sales order entry, the system posts line items to an extension ledger that is used for predictive accounting (which doesn't show up in your external financial statements). Moving to delivery and billing, the system deletes the statistical entries and creates actual line items in the standard ledgers. The entries, either statistical or actual carry the full detail for the relevant profitability segments you have defined.


In this chapter, we introduce you to the core of margin analysis. Namely, reporting with SAP Analytics Cloud and the possibilities analytical stories offer. We also explain the concept of market segment reporting as well as the development of accounting results analysis.

In SAP S/4HANA you have two options to analyze business results:

  • SAP Analytics Cloud (SAC) gives you access to fast, real-time analysis.
  • Embedded analytics provide simple operational reporting using standard queries.

SAP Analytic Cloud Reports - Basics

SAP Analytics Cloud

SAP Analytics Cloud is a software as a solution (SaaS) for analytics, planning, and smart prediction. Currently, SAP provides two types of SAP Analytics Cloud platform options: embedded SAP Analytics Cloud and standalone SAP Analytics Cloud. SAP S/4HANA Cloud fully manages the embedded SAP Analytics Cloud and exposes the embedded SAP Analytics Cloud functionality through SAP Fiori apps. It is offered with no additional license fee. In the standalone option, each tenant has a dedicated SAP Analytics Cloud tenancy. In both options, each tenant’s data is isolated and remains invisible to other tenants. In terms of architecture, SAP Analytics Cloud accesses the ABAP core data services (CDS) views live, using the transient analytical queries generated automatically for the CDS views.

CDS-Views and Semantic Tags


The virtual data model (VDM) forms the basis for analytical (and other) applications to access data in SAP S/4HANA. It is represented by CDS views. The CDS views expose business data (stored in database tables) in a format that is based on business semantics and therefore easier to consume. 

Semantic Tags

Semantic tags are financial data objects aiming to simplify and enhance the use of financial statement (FS) item, general ledger account, and functional area use in analytical applications. Semantic tags are short text IDs that you define centrally for an organization, meaning they can be reused across many analytical applications. They are flexibly used in report columns and rows, and for calculating key performance indicators (KPIs).

SAP delivers standard semantic tags that are used in analytical apps. These tags are assigned to the general ledger accounts for the delivered chart of accounts and financial statement versions (FSVs). Many analytical apps use these standard semantic tags so it’s important to not change them. Instead, it is recommended to define new organization-specific semantic tags as needed and use them in the organization-specific custom analytical applications and custom queries.

You can define financial reporting items such as revenue, COGS, labor, sales deductions, recognized margin, as semantic tags. You then use them directly in reports, instead of defining them each time a new query is built. When you must adjust the definition of a semantic tag, for example, if you introduce a new functional area, you update the semantic tags centrally. This way, all relevant queries and apps using the same semantic tags are automatically updated.

To see an introduction to SAP Analytics Cloud, watch this video.


The main benefits of SAP Analytics Cloud include:

  • Ease of viewing content.
  • Connectivity to trusted data.
  • Access to various visualization tools.
  • Augmented analytic capabilities.
  • Financial planning features.

In a single cloud system, you can analyze, ask, predict, plan, and report.

The technical basis for analytics in SAP systems is CDS views and semantic tags. Core Data Service (CDS) Views are virtual data models of SAP HANA which allows direct access to the underlying tables of the HANA database. Semantic tags are short text identifiers that you use to represent key figures. They are assigned to calculate key performance indicators (KPIs) for analytical reports.

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