Blogs

Explore the transformative power of the "Disaggregate by Measure" functionality in SAP Analytics Cloud (SAC) with the recent release in the 2024 Q1 version. In this blog, Fillippo Naggi, delves into the game-changing feature, allowing seamless allocation of costs, budgets, and values in SAC models.

In the last blog, we touched on SAP build and how its low-code drag-and-drop environment democratizes the playing field for non-technical folks, helping these business users focus on higher-order activities such as the creation of enterprise applications. Continuing this theme, today we bring another blog that focuses on seamless application development.

Despite investing in various digital transformation projects, many organizations overlook ensuring the success of each project before moving on to the next. This haste creates a digital adoption gap, where the promised benefits of technology capabilities remain unrealized due to limited user engagement.

In the evolving landscape of technology education, there's a notable shift towards No-Code, Low-Code, and Code-Gen tools, acknowledging the varying importance of coding tasks. These solutions automate routine coding elements, granting users the bandwidth to focus on higher-order activities such as code design and user experience.

SAP S/4HANA has emerged as a key catalyst in driving digital transformation for organizations across various industries. Its advanced capabilities and architecture enable companies to streamline operations, gain real-time insights, and accelerate innovation.

Embarking on a successful digital transformation requires a harmonious blend of technology and human adaptability. Despite the longstanding call for change, many companies are still finding their footing in this evolving business landscape. The key lies in crafting a visionary roadmap that seamlessly integrates technological advancements with a culture of continuous learning.

The ML Scenario Manager in SAP Data Intelligence serves as a centralized platform to efficiently organize, manage, and execute machine learning (ML) scenarios. An ML scenario refers to a comprehensive workspace encompassing various components crucial for ML projects such as datasets, pipelines, Jupyter Notebooks, and monitoring tools.