Planning the future with SAP Analytics Cloud (SAC) – How and Why

While my colleague Andreas already wrote a blog post about SAC Planning with BPC Live connection, this blog covers the part of Planning with SAC Native Planning and can help you decide when this type of application could be suitable for your needs. SAP Analytics Cloud (SAC) has been gaining attention in the field of business intelligence and planning, providing businesses with tools to optimize financial and operational planning. With its seamless integration of analytics, planning, and predictive capabilities, SAC helps organizations make real-time, data-driven decisions. In this blog, I’ll walk you through the benefits and challenges of SAC Native Planning, explain how it works, and show you how it can enhance your planning processes. Whether you’re just starting to explore SAC or comparing it to other solutions, this post will help you understand when and why SAC Native Planning could be the right choice for your business. Let’s dive in!

Why choose SAC Planning

Here are the main advantages of Planning in SAP Analytics Cloud:

Unified Environment
- Native planning in SAC integrates analytics, planning, and predictive capabilities into a single platform, enabling streamlined workflows and reducing complexity.
Flexibility in Model Design
- SAC allows users to create planning models tailored to their specific business needs. Features like custom hierarchies, allocation logic, and versioning can be easily adjusted to meet changing requirements.
Real-Time Data and Performance
- Leveraging in-memory technology, SAC ensures fast access to data and real-time updates, making planning processes more efficient and responsive.
Simplified Architecture
- With native planning, there’s no need for other systems like a Business Warehouse or complex integrations, reducing the risk of data inconsistencies and simplifying system maintenance.
Collaboration and Accessibility
- The cloud-based structure of SAC supports real-time collaboration, enabling teams across different locations to work together seamlessly on live data and align strategies effectively.
Cost Efficiency
- By combining planning, analytics, and predictive functionalities into one tool, SAC eliminates the need for multiple systems, reducing operational costs and maintenance efforts.
Integrated Predictive and Advanced Analytics
- SAC’s predictive capabilities and machine learning integration allow users to incorporate advanced insights directly into their planning processes, enabling more informed and forward-looking decisions.

By leveraging these advantages, native planning in SAC empowers organizations to streamline their planning processes, foster collaboration, and make fast data-driven decisions.

How to get to SAC Planning

Let me show you a simple example of planning functionality in SAC using a copy function. Imagine we have an activity challenge where colleagues enter their steps per day. For this example, we’ll assume you want to copy all the entered steps from one challenge to another. This is a straightforward way to demonstrate planning in SAC while keeping the effort manageable. This is how the architecture looks like:

Before we begin, it’s important to note that this example assumes you already have some basic experience with SAC, even if not specifically with its planning features. You’ll also need access to a SAC Tenant and the correct license to enable planning functionality – specifically, the Planning Professional license. This license is designed for developers and administrators of planning applications and includes additional features beyond the standard Business Intelligence license, such as creating planning models, capturing plan data, working with private and public plan versions, and implementing planning logic.

Now, let’s dive into the single parts of how to implement this small planning feature!

User Management

Effort Estimation: Low-Medium (depending on the number of users and roles)
Difficulty: Easy
Necessary Action: Ensure the user has the Planning Professional license.

Model + Dimensions

Effort Estimation: Low-Medium (for simple, non-public dimensions <10 minutes)
Difficulty: Easy – Advanced (depending on dimension complexity)
Necessary Actions:

Create a new Model.
Add necessary dimensions (save as public when required) with hierarchies and master data.
Set the Model’s settings to Planning (default).

Story (for simple input layouts)

Effort Estimation: Medium
Difficulty: Easy
Necessary Actions:

Add a new table to the story and link it to the Planning Model.
Ensure all dimensions are unique.
Select the appropriate dimensions for rows and columns.
Tip: To highlight input-enabled cells, consider changing their color.
Navigate through the dimensions and select „unbooked data“ from the context menu to view master data.

Analytic Application (for complex Input Layouts)

Effort Estimation: High
Difficulty: Advanced
Necessary Actions:

Use the Analytic Application when a more complex input layout is required, such as user-dependent elements or derived parameters for data actions.
Here are some blogs that could help with scripting:
- https://www.zpartner.eu/sac-scripting-array-handling-part-1/
- https://www.zpartner.eu/sac-analytic-application-scripting-for-beginners-part-1-how-to-read-and-change-master-data-of-dimensions/

In this example we keep it simple and only use the story.

Planning Functions

Effort Estimation: Medium
Difficulty: Easy
Necessary Actions:

Create a Data Action with a copy step to replicate data from one challenge to another.
Define simple Input Parameters for the Data Action.
Fill the Input Parameters in the story or analytic application when executing the Data Action.

Reference Data by Excel Import

Effort Estimation: Medium

Difficulty: Easy

Necessary Actions:

Prepare an Excel file containing the reference data with the required format and structure.
Navigate to the Model in SAP Analytics Cloud and select the Import Data option.
Choose Excel as the data source and upload the file.
Map the columns in the Excel file to the appropriate dimensions and fields in the model.
Validate the data mapping and import the reference data into the model.

Challenges: Performance and Complexity

When working with SAC Planning, challenges often arise in balancing performance and complexity. Larger models with complex dimensions and data actions can slow down performance, especially when dealing with extensive datasets or frequent calculations. Complexity increases with user-dependent scenarios, derived parameters, or dynamic data actions, requiring careful optimization to ensure smooth operation. Additionally, within SAC, there is currently no concept for reusing logic in for example different Analytic Application or Data Actions . This means that when the same logic is needed in different places, users must repeatedly script the same functionality, which is both inconvenient and prone to errors. It’s important to keep models streamlined and test performance regularly to avoid potential bottlenecks. For more tips on optimizing performance, check out our blog post on SAC Performance Do’s and Don’ts https://www.zpartner.eu/sap-analytics-cloud-performance-dos-and-donts/ )

Conclusion

SAP Analytics Cloud (SAC) Native Planning is a powerful tool for modernizing planning processes, enabling real-time decision-making and seamless data integration. It’s ideal for businesses looking for simplicity and flexibility. However, for complex planning scenarios or large datasets, performance could be a challenge. In such cases, a hybrid approach or further customization might be needed. By carefully evaluating your needs, SAC can help streamline your planning and unlock new insights for future growth.

Looking ahead, we will explore how Seamless Planning will work. In Seamless Planning, SAC Planning is used in combination with SAP Datasphere, which is, according to SAP the cloud based successor of BW/4HANA (Link to Blog will be added soon).

Related Blogs:

Future proof your SAP planning: BPC / IP transformation to SAC Planning with Live Connection – How and Why

SAP Analytics Cloud – Performance Do`s and Don‘ts

Minimum Viable Product with Seamless Planning

Autor: Svenja Dechert

Svenja has been working as a Business Intelligence Consultant at ZPARTNER since 2020. She specializes in the conceptual design and implementation of integrated planning solutions using SAP BI IP, SAP BPC, and SAP Analytics Cloud (SAC). With more than seven years of experience in SAP BW, ranging from version 7.4 to BW/4HANA, she brings extensive expertise in reporting and planning to her projects. Svenja’s in-depth knowledge and practical experience enable her to develop tailored solutions that meet complex business requirements.