Discovering SAP Business AI Platform

Introducing SAP Business AI Platform25 min

Describing Joule Studio

Performance and Troubleshooting

Exploring Basic Administration

Discovering Basic Administration Tasks47 min

Exploring Basic Planning Functions

Exploring Story Design

Discovering Basic Administration Tasks

Objectives

After completing this lesson, you will be able to:

Discover Cloud Modularization
Summarize key updates

What is SAP Business Data Cloud?

SAP Business Data Cloud (SAP BDC) is a fully managed Software as a Service (SaaS) solution that unifies and governs data from SAP and non-SAP applications to power advanced analytics and AI.

SAP BDC provides the data layer that bridges the applications and AI. SAP BDC provides a data platform to manage all SAP and non-SAP data and is used to build advanced analytical applications and AI.

A diagram that shows how SAP BDC is the middle layer in the AI-Data-Application stack.

SAP Business AI Platform

The SAP Business AI Platform is the foundation layer that supports the SAP Autonomous Suite. The SAP Autonomous Suite comprises AI-infused business applications that brings AI agents and humans together for optimal business process outcomes.

The SAP Business AI Platform provides many technology services that are required by SAP Business Data Cloud including:

Unified Customer Landscape (UCL) is used to securely connect the SAP BDC components, such as SAP Datasphere and SAP Analytics Cloud.
SAP HANA Cloud provides the data lake storage and compute layer.
Security and Compliance is used to manage data access and permissions as well as regulatory compliance.
SAP Build provides developers with tools to build custom applications on top of SAP BDC data.

This means IT teams will already have the skills needed to work with the infrastructure, tooling, and underlying technology of SAP BDC that underpins many other SAP solutions.

SAP BDC is a Key Part of the SAP Business AI Platform

Note

The Business AI Platform now provides the technology services previously provided by SAP Business Technology Platform (BTP).

Architecture of SAP Business Data Cloud

SAP Business AI Platform

Before we explore the architecture of SAP Business Data Cloud (SAP BDC), we need to take a step back and first look at how SAP BDC fits into the Autonomous Enterprise.

The Autonomous Enterprise is SAP's vision of how a modern organization optimizes and adapts itself using AI, automated workflows and real-time data.

The Autonomous Enterprise is built using three technology layers delivered by SAP:

Joule - The new AI interaction layer that connects the business users and AI agents.
SAP Autonomous Suite - SAP and non-SAP AI Agents work together across business domains to optimize outcomes.
SAP Business AI Platform - The AI / data platform that powers the SAP Autonomous Suite.

SAP BDC is a Key Part of the SAP Business AI Platform

Within the SAP Business AI Platform there are three pillars that work together to provide an enterprise-grade AI platform. The pillars are:

Build
Contextualize and Reason
Govern

Each pillar includes various software components.

SAP BDC sits under the Contextualize and Reason pillar of the SAP Business AI Platform and provides the data foundation that provides AI agents with the trusted data they need make reliable decisions.

Note

The Business AI Platform incorporates all of the services of what was previously known as SAP Business Technology Platform (BTP). SAP BDC requires many of the BTP services.

Software Components in a SAP BDC Landscape

SAP BDC is not a single software product. SAP BDC includes many different software components that work together.

Some SAP BDC components will be familiar, best-of-breed software solutions such as SAP Datasphere, SAP HANA Cloud, and SAP Analytics Cloud. Some components, such as SAP Databricks, SAP Snowflake and SAP Reltio are more recent additions to the SAP software portfolio.

Customers can decide which software components they would like to include in their SAP BDC landscape. The chosen components are tightly integrated with each other using an SAP Business AI Platform service known as the Unified Customer Landscape.

The software components of SAP BDC

Let's describe each software component and its role in SAP BDC.

Joule - Joule supports the business user by supporting natural language queries over analytics generated by the Intelligent Content. Joule also supports the IT developer who uses natural language to automate and assist in the build of new intelligent content, such as new data products, data models and dashboards.
Intelligent Content - Intelligent Content is the prebuilt dashboards and data models delivered and managed by SAP. Customers choose to install the Intelligent Content that is relevant for their lines of business (finance, spend etc) and for their industry (oil and gas, consumer packaged goods etc).
SAP Datasphere - At the heart of SAP BDC is SAP Datasphere, which provides the data integration and data modeling layer on top of the data products. Ready-made data models are provided by SAP to support the dashboards that are included in the intelligent content. Custom data models can also be developed. Non-SAP applications can be connected to Datasphere to combine SAP data with non-SAP data. SAP Datasphere also serves as a central tool for managing analytical roles and data access control, helping you to determine which business users can view specific data.
SAP Analytics Cloud - SAP Analytics Cloud is the dashboard / visualization component of SAP BDC. SAP Analytics Cloud stories are used to provide the required dashboards which are part of the delivered intelligent content. With its advanced visualization and planning functions, SAP Analytics Cloud serves the business user as a central tool for gaining business insights and executing planning functions. Furthermore, using SAP Analytics Cloud, business users can run ad-hoc analysis, explore the provided data with a built-in AI-driven natural language capability, and act on their insights with AI-driven suggestions, bridging the gap between analytics and the business processes.
SAP Master Data Governance (MDG) - SAP MDG provides master data governance for SAP data focusing on improving the quality of master data originating from ERP systems, such as SAP S/4HANA. Master data plays an important role in building a trusted data foundation, so SAP MDG is an essential component of SAP BDC, for customers who run SAP ERP systems.
SAP Reltio - SAP Reltio focuses on providing master data governance for non-SAP data. A trusted data foundation needs reliable master data. SAP Reltio extends master data governance beyond SAP originated master data, to include master data that originates from all applications. SAP MDG and SAP Reltio work together to cover all sources of master data.
SAP HANA Cloud - Within SAP BDC customer can deploy an SAP HANA Cloud instance. SAP HANA Cloud consumes data products as data sources for developing multi-model analytical applications, which includes spatial, graph, vector, relational and dimensional modeling. SAP HANA Cloud can generate custom data products from its results sets. SAP HANA Cloud connects directly to a wide range of SAP and non-SAP data sources using its Smart Data Integration (SDI) technology to access live operational data. It combines this data with the curated data products in SAP BDC to provide a dual-sourced data set that can be used to power custom AI agents.
SAP Databricks - SAP Databricks is a tool to enrich your SAP data using AI and machine learning. SAP Databricks provides data scientists with a comprehensive set of tools for data engineering projects. SAP Databricks is the embedded version of Enterprise Databricks and has been specially developed through the partnership with SAP and Databricks to bring data science tooling to SAP customers.
SAP Snowflake - Similar to SAP Databricks, SAP Snowflake is also a tool for the data scientist who would like to use the data products of SAP BDC in AI and machine learning projects. SAP have partnered with Snowflake to provide the full feature set of the enterprise version of Snowflake to SAP customers.
SAP BW - Customers who run SAP BW can include it in the landscape of SAP BDC. Customers who do that are able to generate data products from their BW data either as a one-time onboarding exercise, or as a continual data pipeline where existing BW extractions continue to run and feed the SAP BDC data products with live data.

When a customer subscribes to SAP BDC, they purchase credits known as Capacity Units. The customer decides how to spend their Capacity Units on the various components. They do this by considering the use-cases that SAP BDC will support so they know which components are required. For example, if the customer decides to enrich SAP-managed data products using machine learning, they must provision either the SAP Snowflake or SAP Databricks component into their SAP BDC landscape If they have non-SAP system that generate master data they might consider including SAP Reltio.

The group of software components that the customer selects is known as an SAP BDC formation. A customer can define multiple formations combining different SAP BDC components. Some components can be shared across formations.

Connecting to 3rd Party Data Platforms

A key part of SAP's data fabric strategy is to allow customers to mix their preferred tooling to create their own data management landscape. Customers can include certified partner solutions in their SAP BDC landscape.

SAP BDC Connect to share data with 3rd party platforms

SAP BDC Connect is the component of SAP BDC that enables customers who already run third-party data platforms to share their SAP BDC data products. It is important to emphasize that data is shared and not copied. The shared data products are still managed and governed by SAP BDC.

Data from third-party platforms can also be shared with SAP BDC using SAP BDC Connect. SAP BDC Connect supports bi-directional data sharing.

Let's highlight the key points covered in this lesson:

SAP BDC is part of the SAP Business AI Platform.
SAP BDC sits under the Context and Reason pillar of the SAP Business AI Platform
.
SAP BDC combines existing, best of breed SAP software components with new components.
Components of SAP BDC include Joule, Intelligent Content, SAP Datasphere, SAP Analytics Cloud, SAP Databricks, SAP Snowflake, SAP HANA Cloud, SAP Reltio, SAP MDG and SAP BW.
SAP BDC bi-directionally shares data with 3rd party data platforms using the component known as SAP BDC Connect.

Exploring Joule's Capabilities

In today’s fast-moving workplace, employees face increasing demands - more complexity, tighter timelines, and higher expectations. Switching between systems and searching for data often slows things down. Joule was created to change that.

Joule is SAP’s generative AI copilot that helps people work smarter. It brings intelligence, automation, and contextual insights directly into business processes, reducing friction and freeing up time for higher-value work.

What is Joule?

Joule is built into all major SAP applications and available wherever you work. With a single click, or a simple prompt, you can ask Joule to handle everyday tasks, find data, or explain insights. Joule currently supports over 1,800 capabilities across SAP systems, covering around 80% of the most common business transactions.

Joule in action for business users. Joule can perform thousands of everyday tasks - such as entering time off, reviewing invoices, or managing deliveries - through simple natural language. Most users see productivity gains of around 30%. And with Joule Studio in SAP Build, organizations can even create custom skills tailored to their workflows and industries.

Explore more: To see Joule in action and learn how to get started, explore the Joule End-User Enablement Guide. This interactive handbook provides an end-user overview, practical examples, and sample prompts to help you make the most of SAP’s AI copilot across applications.

Joule for consultants and developers. For consultants and developers, Joule is more than an assistant, it’s a partner. It provides on-demand guidance during design, implementation, and customization, and can even generate or optimize code snippets. This helps teams deliver solutions faster, with fewer manual steps and greater accuracy.

Joule Agents

Intelligence that scales. Beyond individual tasks, Joule coordinates intelligent agents that work together across departments. From automating workflows, sharing data, and optimizing decisions in real time. This means HR, finance, supply chain, and IT no longer work in silos but as one connected ecosystem.

Joule for Every Role

Joule adapts to the needs of every user, offering the right support, in the right context:

CHROs: Accelerate hiring, improve engagement, and deliver personalized employee experiences.
CFOs: Automate financial processes, enhance compliance, and improve cash flow visibility.
COOs: Optimize logistics, forecasting, and inventory planning in real time.
Developers: Generate and validate code faster and streamline app development.
Consultants: Access instant insights and documentation, cutting research and project delivery time.

Note

The analytical insights capability in Joule, powered by SAP Analytics Cloud, allows business users to explore their data and gain insights into their business. Joule itself is not available in SAP Analytics Cloud, but instead users can ask questions to the Joule analytic insights feature from other SAP products that have enabled this capability.

Modeling Options

Dimensions

Dimensions represent categories that provide perspective on your numeric data; for example, product category, date, region, cost center, and so on. Dimensions can contain properties that further describe a dimension. For example, you may have a dimension for customer which has properties such as phone number and address to further describe the customer dimension.

Dimensions can also be rolled up into a hierarchical view; for example, time (year, quarter, month), geography (country, region, location), employee structure (executive, manager, employee), and so on.

Measures represent the numeric values that you are analyzing; for example, sales revenue, salary, number of employees, quantity sold, and so on. Sometimes these quantities are contained in a single dimension referred to as an Account type dimension (and probably with the name Account, or something similar). In this situation, the numeric values represent the line items on a corporate balance sheet, income statement, profit/loss statement, and so on. But you can also present the numeric values as individual elements called Measures.

Together, dimensions and measures are the framework for viewing data, whether it be a trend line of revenue over time or a tabular comparison of gross margin across different regions.

Dimensions and measures displayed in a planning model.

Models

Models are comprised of dimensions and measures and represent a specific subset of data; for example, sales, production, financial, shipping, etc.

Models are the primary data sources for SAP Analytics Cloud stories.

In SAP Analytics Cloud there are two styles of models:

Analytic models are read-only.
Planning models are read/write.

Analytic models are used strictly for read-only data reporting and analysis. A date dimension is available but is not required, and you can remove it from the model during the design stage.

Why is a date dimension optional? One scenario is that the model represents only current data. Because users know the data is always "current," there is no need for a date dimension.

Screen shot of a sample analytic model, with the model preferences inset.

Planning models are pre-configured with required dimensions for Date and Version. These dimensions are required because planning activities are dictated by time frames, and the planning numbers are intended for different purposes – budget, forecast, and planning. Planning models offer support for security features at both the model level and dimension level.

When working with a planning model in a story, users with planning permissions can create their own versions of model data. These users can also write data to the model by typing new values, copying and pasting data, and using data actions.

Screenshot of a sample planning model, with required dimensions highlighted and the model preferences inset.

The Modeler

The Modeler area of SAP Analytics Cloud is where you create models. According to your data integration strategy, you can create a new model one of 2 ways:

Create a model.
Create a live data model.

Datasets

A dataset is a simple collection of data usually presented in a tabular format. You can use a dataset as the basis for a story.

SAP Analytics Cloud has two types of datasets:

Embedded datasets are embedded into a story and are unique to that story. They cannot be shared outside the story or refreshed.
Public datasets are standalone datasets and can be shared among different stories.

Both types of datasets can be enhanced with basic data preparation and transformation functionality.

Neither dataset can be scheduled for a refresh; you must manually re-import the updated data. SAP Analytics Cloud automatically matches the columns of the newly acquired data to the columns of the existing data, but any previous data transformations will be lost.

If you import data from a flat file, you can only re-import a compatible file: a file that has the same number of columns as the original file, and with the same column names and data types as in the original file.

Both datasets can be secured to allow users access to the dataset or not. Specific column-based or property security, however, is not supported for any datasets.

Converting Datasets

You can convert an embedded dataset to a public dataset. However, a limitation to a public dataset is that you cannot change its data source. For example, if your public dataset was originally created from a flat file but you now want to use an SAP Business Warehouse query, you have no option to make that change. Embedded datasets, on the other hand, do allow you to change the data source via the Add New Data option.

You can also convert an embedded dataset into a model, but any transformations you made to the dataset are lost and must be recreated in the model. A public dataset, however, cannot be converted to a model.

Compare Datasets and Models

Overall, datasets and models complement each other. Datasets are perfect for ad hoc, ungoverned use cases based on acquired data. Models are used when the use case requires more governed data analysis and planning scenarios.

Diagram summarizing the data included in this concept above.

In summary, the key differences between datasets and models are as follows:

Datasets	Models
For simple, quick, ad hoc data analysis.	For formal, governed data analysis.
Can access live data only from on-premise SAP HANA.	Can access many live SAP data sources.
Does not support planning use cases.	Supported for planning use cases.

Custom Widgets

Custom widgets can be bookmarked in the optimized story experience, and the bookmark's property values and data bindings result for the widget can be utilized. The custom widget developer must set the supportsBookmark property to true in the widget's JSON file.

Scheduling Publications

As a Schedule Manager or Schedule Administrator, you are notified via email whenever the status of your scheduled publication changes from Open to Successful, Partially successful, Failed, or Canceled. You can view details of the status by clicking the Open Task button on the email.

In addition, you can now use the Include Formatting option while scheduling a publication with the CSV file type and keep the numeric formatting precisely as it appears in the table or chart. The value will be a string in the CSV file.

Screen shot of retaining formatting when publishing as a .csv file

Modeling Improvements

Data Disaggregation

Data disaggregation refers to how data is redistributed to leaf members when changing data on a parent member in a table cell. It is only available in the Measure Details panel for models with planning capabilities enabled. Based on different settings, such as aggregation mode, validation rule and state of the data, disaggregation can behave differently. You can also have even more control by selecting the disaggregation type in the Measure Details panel. There are two options:

Standard
Reference to Another Measure

Screen shot of the disaggreation type options in the SAC Modeler

Note

Reference to Another Measure is only available if there is no exception aggregation, and the aggregation mode is either empty or set to SUM.

Standard

Standard is the default disaggregation behavior, derived from the measure's aggregation type and/or state of the data. For example, if you have a measure that already contains data across the leaf members and you change the value of a parent member, the value is then disaggregated proportionally based on the values of the leaf members.

Reference to Another Measure

Reference to Another Measure gives you with the ability to drive the disaggregation process based on business rule proportions from another measure. It also unlocks access to two disaggregation modes:

Total Disaggregation - disaggregates the entire value. In the following figure, 15,000 for Texas is distributed among the stores based on their floor sizes.
Delta Disaggregation - disaggregates only the difference to the value entered. In the following figure, 2,474 for Texas (the difference between 12,526 and 15,000) is distributed among the stores based on their floor sizes.

Note

Locked records are not overridden by data disaggregation

Time-to-Date Functions

As a modeler or planner, you can now create calculated measures or calculated accounts using YTD (Year-to-Date), QTD (Quarter-to-Date), and MTD (Month-to-Date) functions at the model level. These functions display running totals across year, quarter, or month levels of date granularity, enabling users to compare values against a budget, a target, or previous periods.

Screen shot of the MTD function used in the modeler's calculated measure

Calculation Dependencies

A graphic tool is available in the Calculations screen to help you visualize relationships and dependencies between objects. For all measures, conversion measures, calculated measures or account members that you select, the application automatically displays all objects that are directly connected in the calculation. It updates in real time as you make changes to a formula within the editor.

Screen shot of the graphic showing a calculated measure's dependencies

The graph does not feature any editing capabilities, and you cannot create or edit any existing dependencies. However, it is available for both account-based and measure-based models.

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