Data Professionals Blog posts

Working with BDC in the Autonomous Enterprise: The Enterprise Architect Perspective

AI will not replace enterprise architecture. It will depend on it.

Supporting Your SAP Data Architecture Learning Journey Through Community Discussions

Following the SAP Data Architect learning journey or working on data architecture initiatives? Join a community of practitioners to discuss real-world challenges, implementation approaches, governance...

Business Data Architecture: Artifact Roles - Controlled Execution Units

The clock is ticking. It's month-end closing. Revenue numbers don't match between the executive dashboard and detailed report. You open the Revenue Data Product and find ten artifacts with technical n...

From Silos to Strategy: What 20 Years of SAP Data Management Taught One Practitioner

I recently sat down with Rakesh Ramakrishnan, a longtime SAP practitioner who started his career as a C and Visual C++ programmer before an accidental encounter with an SAP customer in Malaysia in the...

Business Data Architecture: Transformations - Making Architectural Boundaries Enforceable

You understand the concepts - modeling layers organize data flow, data product types structure reuse, platforms own domains. But when a developer asks: "Can I add Revenue Growth % to my Definition lay...

Business Data Architecture: Data Products - Structured Reusable Analytical Assets

Now we define what is built - Data Products.Without clear contracts, analytical assets fragment across platforms - the same metric calculated differently in SAP Datasphere, Snowflake, and Power BI, wi...

Business Data Architecture: Modeling Layers for Structured Transformations

Transformations end up everywhere when teams lack a shared understanding of what's been done to data and what still needs to happen. Modeling layers solve this - layers that define where transformatio...

Business Data Architecture: Platform Responsibility in a Multi-Platform Analytics Landscape

Without explicit platform ownership, organizations end up with the same metric calculated differently on each platform, duplicate ingestion pipelines creating version conflicts, and teams unable to an...

The Architecture Question Your AI Strategy Can't Avoid

The ODI's supplementary research on data fabrics, released alongside the main framework, maps this to three specific capabilities: integration, cataloguing, and governance.

Business Data Architecture: Defining Business Meaning

Why do analytics environments break down as they scale? Not because of data - but because meaning gets mixed together. In this post, we unpack a simple but powerful shift: separating what you measure ...

Your AI Agent Has Three Different Definitions of "Customer." That's a Problem.

MDM is no longer a data governance discipline. In an agentic world, it's AI infrastructure.

Business Data Architecture for Modern Analytics Landscapes

Business meaning fragments as analytics scales. Metrics calculated differently across teams. Logic duplicated in reports and data models. The same term meaning different things to different stakeholde...

Business Data Architecture: Why Business Meaning Matters in Modern Analytics Landscapes

When business meaning isn't defined explicitly, it hides - fragmented across Power BI reports, HANA views, dbt models, BW queries, and platform solutions. Which one is right?Without explicit architect...

The AI Isn't the Problem. The Data Underneath It Is.

The Open Data Institute (ODI), SAP's independent research partner on the Interchange for Data and Enterprise AI (IDEA), is releasing A Framework for AI-Ready Enterprise Data in late June. It's a rigor...

From “AI in Applications” to “AI on Applications”

What SAP’s Autonomous Enterprise strategy means for data professionals.

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