Ninety-nine of the top 100 companies in the world run SAP. It’s the central cog of the business – the system everyone trusts to keep operations running. That trust is well earned. SAP is deeply engineered, and it has to be, given how much sits on top of it.
But that same strength creates a real problem for anyone trying to bring AI into the picture. SAP systems are, by design, protected. The teams who run them are protective of them, and SAP itself is protective of its data. That’s understandable. Nobody wants to compromise the system that runs the business. But it also means that pulling meaningful data out of SAP can be challenging.
Why raw SAP data and AI don’t mix
AI is only as good as the data behind it. That’s not a new idea, and it’s one we’ve believed for years. But it matters more than ever now with the use of AI models and applications across organizations.
For SAP customers, it’s not just a matter of extracting SAP tables, dumping them in a data lake, and pointing an AI model at them. At that point, the AI has no context. It doesn’t know why a number exists, how it was calculated, or what business logic sits behind it.
Give AI that much freedom and it will give you an answer. It just won’t be one you can trust, or one you’d want to stand behind in front of your leadership team.
That’s the real definition of AI-ready data. It’s not just about getting data out of SAP. It requires a governed way to extract SAP data, preserve its business meaning, and deliver it into analytics and AI workflows in a form that users trust.
Where the prep work really happens
DVW Analytics takes a different approach from simply connecting at the database level. The Alteryx Connector for SAP from DVW, connects through the SAP application layer, in the same way that SAP business front-end tools interact with SAP.
That means every user still authenticates through SAP, SAP still runs its own checks and authorizations, and the built-in safety belts are fully respected. We made a deliberate choice early on to build on top of SAP’s guardrails rather than around them. That’s why more than 500 enterprises run our connectors inside their own SAP authorizations.
The practical benefit is that business users can pull data from SAP reports that already carry real business context and logic, not just raw rows in a table. That’s a starting point well beyond raw data.
By the time it lands in Alteryx, most of the heavy lifting is already done. What’s left is combining it with other data sets, cleansing it, and shaping it into something an AI solution can use well. We see our role as closing the distance between “data sitting in SAP” and “data that’s ready for AI,” so the prep layer isn’t something your team has to build from scratch.
What this looks like in practice
One of the most common examples we build for customers is an audit workflow, and it’s a good illustration of why context matters so much.
Say you want to audit supplier invoices. Our connectors extract two things directly from SAP: the actual postings and the PDF documents that were uploaded as supporting evidence, like the original invoice a supplier submitted.
On its own, either piece is incomplete. The posting tells you what was recorded. The PDF tells you what was actually submitted. Put them together, and Alteryx can automatically flag mismatches between what was posted and what the evidence supports, at a scale no team could manage by hand.
And while the original build wasn’t framed as an AI use case, it’s a natural one. Once you have clean, matched, well-governed data flowing through, adding AI on the other end, instead of a static dashboard, is a small step. AI just becomes another way of presenting data you already trust.
The real deliverable is trust
Alteryx built its reputation on being usable by business people, not just data engineers. Getting SAP data into that environment has usually needed a technical specialist. We wanted our tools to live up to that same expectation: pull it onto the canvas, use the data you already know how to find in T-codes, Queries, HANA views, Fiori apps etc., and skip the deep technical lift.
But the bigger point is this: the promise of AI was never just faster analysis. It’s better decisions at scale. And that promise only holds up if people trust the data behind the output.
For SAP customers, that trust starts long before AI enters the picture. It starts with how the data is accessed and prepared, preserving the business context that’s already built into the system. Giving users a structured path out of a mission-critical system and into modern analytics and AI workflows, instead of a shortcut around it.
That’s why we don’t think of AI-ready SAP data as just clean data. It’s governed data. It’s meaningful data. It’s data that’s been extracted and prepared in a way the business can understand and defend, not just data that happens to load into a model without errors.
Handle that preparation layer properly, and AI stops being an experiment sitting off to the side. It becomes an extension of the business processes you already trust, built on SAP data, prepared through DVW, and brought to life in Alteryx.