SAP S/4HANA-Migration: Implications for the Data & AI landscape (Blog Series Part 1)

[florian_roeder]

With its Cloud ERP applications, SAP offers various SAP S/4HANA deployment options (private, public, or hybrid) tailored to meet each customer’s business needs based on criteria such as organization size, business maturity, process complexity, and existing SAP footprint.

Consider the following scenario which represents a significant portion of the customer base. Many customers are currently on a digital transformation step moving from ECC to SAP S/4HANA Cloud due to several reasons:

• Upcoming mainstream maintenance
• Pursuit of innovation (e.g. AI)
• Industry-specific challenges
• Operational efficiency
• Complex system landscape and integration (no single source of truth)
• Costs and time to market

This transformation is not just a technical upgrade but a strategic move to modernize operations, enhance agility, and position organizations for future success.

Organizations undertaking SAP S/4HANA transformation programs face several key challenges in modernizing their data and analytics backbone. These challenges can be categorized into four main areas (data, people, processes, technology):

1. Data

• Key Question: How do customers integrate SAP S/4HANA data effectively with other sources and external data?
• Challenge: Ensuring seamless integration of diverse data sets to create a unified, real-time data landscape that supports analytics and decision-making.

2. People

• Key Question: How can customers drive agility by empowering all stakeholders?
• Challenge: Enabling collaboration and agility by providing stakeholders (business and IT) with the tools, insights, and skills they need to leverage data effectively in their roles.

3. Processes 

• Key Question: How do customers optimize and streamline business processes to leverage SAP S/4HANA's full potential?
• Challenge: Aligning and reengineering business processes to take advantage of S/4HANA's simplified architecture, automation features, and real-time capabilities while minimizing disruptions and ensuring continuous improvement. Further organizations need to critically evaluate which legacy processes are essential for the new environment.

4. Technology

• Key Question: How do customers modernize their data and analytics backbone while building on existing systems?
• Challenge: Leveraging existing technology investments and integrating advanced capabilities to modernize the data infrastructure, ensuring scalability and efficiency.

Addressing these challenges requires a holistic approach that aligns data, people, processes and technology strategies to achieve successful SAP S/4HANA transformation and unlock the full potential of modern analytics.
Organizations must recognize that the quality of their data underpins the effectiveness of AI, analytics, and decision-making processes.

Because companies that take AI seriously must take data seriously!

Consequently customers need to reconsider their data and analytics strategy, raising the question of what SAP S/4HANA offers in this domain.

The transformation brings several challenges, with the top five being:

• The new processes and data structures within S/4HANA
• Data migration & data mapping
• Adopting modern data warehouse concepts (e.g. data mesh, data fabric)
• Integrating external data
• Leveraging embedded analytics

These challenges are closely interconnected, but this blog will specifically focus on the “Leveraging embedded analytics” which is followed by other editions as part of the blog series.

Embedded Analytics defines the integration of end-to-end analytics and real-time decision-making capabilities in SAP S/4HANA.

One of the significant advantages of S/4HANA is its seamless combination of transactional and analytical data within a single system enabling:

• configurable KPIs 
• role-based “Overview Pages” and cockpits
• insight to action capability: analyze and resolve
• historical data for simulations and predictions
• flexible drill down for maximum transparency
• optimize business processes in ERP by deeply integrating operational analytics data

The following screenshot from a SAP S/4HANA Cloud Public Edition, retail, fashion, and vertical business home screen represents some of the available insight cards as part of the embedded analytics:

These insights also cover end-to-end processes, such as the three examples below, from the SAP S/4HANA Cloud Public Edition, retail, fashion, and vertical business system:

Before we move on it's important to clarify what embedded analytics specifically means in the context of SAP S/4HANA.

Embedded Analytics in SAP S/4HANA is designed to provide real-time insights directly within the system, eliminating the need for separate analytical platforms. The term “embedded” encompasses the following aspects:

Embedded at the Database Level

• There is no distinct storage for analytical data; instead, the analytical data is drawn directly from transactional data in real time.
• Access is provided exclusively to live data, ensuring up-to-the-minute accuracy.
• Necessary data transformations are carried out via the Virtual Data Model (VDM), simplifying the integration of analytics.

Embedded at the UI Level

• The same User Interface (UI) technology, often even the same screens, is used to display both transactional data and analytical insights.
• This seamless integration ensures users can work with analytics and operational data without switching systems or interfaces, using SAP Fiori as the primary platform.

Embedded into Business Processes

• Analytics are deeply integrated into business processes with role-based access to relevant analytical artifacts, ensuring that users see only the information pertinent to their role.
• Intent-based navigation is utilized, guiding users to take appropriate actions based on insights.
• This enables the implementation of an insight-to-action workflow, empowering users to make informed decisions and execute them immediately within the same environment.

By embedding analytics across these layers, SAP S/4HANA provides a unified system that combines operational and analytical capabilities, promoting efficiency and data-driven decision-making.

Let's have a look into the technical components:

SAP S/4HANA Embedded Analytics is designed as a multi-layered architecture, integrating real-time analytics seamlessly into the system. This architecture consists of three interconnected layers, each playing a critical role in enabling advanced insights and decision-making directly within SAP S/4HANA.

1. Data Layer

The foundation of the architecture is the S/4HANA Tables, where core transactional and operational data is stored. These tables encompass essential business areas such as Finance, Production, Procurement, and Transactions. The data in this layer is accessed in real time, ensuring that all analytics are based on up-to-date information.

2. Semantic Layer

The Virtual Data Models (VDM), built using ABAP Core Data Services (CDS) views, bridge the gap between raw transactional data and meaningful insights. These models define both analytical and transactional queries, transforming the underlying data into a semantic structure that is easier to interpret. The VDM eliminates the need for data replication by providing real-time access to transactional data. It is the critical layer that ensures data from the Data Layer is structured and ready for use in analytics, forming the backbone for interconnectivity.

3. Analytic Layer

The Analytic Layer provides the tools and interfaces that enable users to interact with the insights derived from the data.

• The SAP Fiori UI delivers an intuitive, role-based user interface for accessing dashboards, reports, and other analytical tools.
• The SAP Analytics Cloud (SAC), embedded within S/4HANA, offers advanced analytics, visualization, and data storytelling capabilities. This ensures that users can analyze, visualize, and act on insights within the same environment.

Interconnection Between Layers

These layers are tightly integrated to provide a seamless analytical experience. The Data Layer serves as the source of truth, ensuring real-time, accurate data. The Semantic Layer processes and structures this data, making it ready for analytics by leveraging VDMs. Finally, the Analytic Layer presents this information to users through Fiori dashboards and SAC visualizations, enabling insights-to-action workflows. Data flows between layers through federation or replication, ensuring that users always have access to real-time insights without the need for separate analytical systems.

This interconnected architecture ensures that SAP S/4HANA Embedded Analytics is not only robust and efficient but also highly user-centric, empowering business users to make informed decisions based on reliable, real-time data.

What does this mean for the end user?

There are two types of embedded analytics tools that empower end users to drive their business:

• Tools to display for the business users
• Tools to create and adapt for analytics specialists

Let me share some insights into the advantages of embedded analytics for business users:

SAP S/4HANA Embedded Analytics provides a comprehensive suite of tools for business users, enabling real-time insights and decision-making directly within the platform. The offerings include:

Further let me also share an overview of the advantages for an analytics specialist.

In conclusion: SAP S/4HANA’s embedded analytics offers intuitive and actionable tools for business users and analytics specialists, seamlessly combining operational and analytical capabilities to drive better outcomes.

Lets make it concrete to the context of a retail value chain and the respective personas within the SAP S/4HANA Cloud Public Edition, retail, fashion, and vertical business system:

Let's pick one specific area as an example.

Value Chain: Store Commerce & Connectivity

Persona: Store Manager

Example:

The Store Manager can leverage multiple embedded analytics features to optimize store operations and enhance business outcomes:

1. Goods Movement Analysis

   • Use Case: Store Manager tracks inventory flows such as stock transfers, receipts, and returns in real time. For instance, the manager can identify slow-moving items and prioritize their promotion or clearance, ensuring optimal inventory levels across the store.

2. Sales Performance

   • Use Case: Store manager analyses the store’s sales performance, identifying top-performing products, underperforming categories, and trends in customer purchasing behavior. The manager can use this data to adjust product placement, refine sales strategies, and focus on high-demand products.

3. Current/Upcoming Promotions

   • Use Case: Store Manager monitors the success of ongoing promotions and their impact on sales. By comparing promotion-driven sales against historical performance, the manager can evaluate their effectiveness and refine future promotional strategies. This enables the store manager to align inventory levels with anticipated demand and avoid stockouts during promotions.

4. Upcoming Goods Receipt

   • Use Case: Store manager gains visibility into expected deliveries to prepare the store for incoming stock. This helps with resource planning, such as scheduling staff for restocking and organizing storage areas.

By utilizing these embedded analytics features, the Store Manager can make informed decisions to boost sales, optimize inventory, and enhance the overall customer experience, all while improving operational efficiency.

With that I want to close the first edition of my blog series around "SAP S/4HANA-Migration: Implications for the Data & AI landscape".

In my next blog I will focus on the differentiation when embedded analytics is most effective and when strategic reporting becomes more relevant.

Stay tuned.