AI is only as good as the data behind it. For SAP customers, that is a harder problem than it looks. Pulling tables out of SAP and loading them into a data lake gets you rows. It does not get you meaning. The model has no idea why a number exists, how it was calculated, or what business rule sits behind it. AI-ready SAP data is not just clean data. It is data extracted in a governed way, with its business context intact, and delivered into analytics and AI workflows in a form people are willing to defend. This post explains where that preparation work actually happens. A version of this article by Ian James, Account Executive at DVW Analytics, was first published on the Alteryx blog as The Missing Layer Between SAP and Trustworthy AI. Why raw SAP data and AI do not mixNinety-nine of the hundred largest companies in the world are SAP customers, and 86 of them are SAP S/4HANA customers. It is the system the business runs on. It is deeply engineered, and it has to be, given how much sits on top of it. The teams who run it are protective of it. So is SAP. None of that is unreasonable. But it does mean getting meaningful data out of SAP is harder than getting data out of most other systems. That matters more now than it used to. Extract the tables, drop them in a data lake, point a model at them, and you will get an answer. It just will not be an answer you want to stand behind in front of your leadership team. The model does not know why a number exists, how it was calculated, or what business logic produced it. So AI-ready data is not a cleanliness standard. It is a governed way to read SAP data, keep its business meaning attached, and hand it to analytics and AI workflows in a form users trust. Where the preparation actually happensThe DVW Alteryx Connector for SAP does not connect underneath SAP at the database. It works at the application layer, the same layer SAP's own business front ends use. Every user signs in to SAP and runs under their own SAP permissions, and SAP applies its own checks exactly as it would for any other access. That was a deliberate choice early on: build on top of SAP's controls rather than around them. It is why more than 500 enterprises run DVW connectors inside their existing SAP security model. The practical benefit is what arrives on the canvas. Business users can read from the SAP reports, T-Codes, queries, HANA views and Fiori apps they already know, so the data comes with the business logic those objects already apply. That is a very different starting point from raw rows in a table. By the time it lands in Alteryx, most of the heavy lifting is done. What is left is blending it with other data, cleansing it, and shaping it into something an AI solution can use well. The preparation layer is not something your team has to build from scratch. What this looks like in practiceAudit workflows are one of the most common things we build with customers, and they show why context matters. Say you want to audit supplier invoices. The connector reads two things out of SAP: the postings themselves, and the PDF documents attached as supporting evidence, such as the original invoice the supplier sent in. On its own, each half is incomplete. The posting tells you what was recorded. The PDF tells you what was actually submitted. Together, Alteryx can flag mismatches between the two at a scale no team could work through by hand. That build was never framed as an AI use case. It is an obvious one. Once clean, matched, governed data is flowing, putting AI on the end of it instead of a static dashboard is a small step. AI becomes another way of presenting data you already trust. The real deliverable is trustAlteryx built its reputation on being usable by business people, not only data engineers. Getting SAP data into that environment has usually needed a technical specialist. Our tools are built to hold to the same expectation: pull the data onto the canvas using the SAP objects you already know how to find, and skip the deep technical lift. The bigger point is this. The promise of AI was never faster analysis. It was better decisions at scale. That promise only holds if people trust the data behind the output, and for SAP customers that trust starts long before AI enters the picture. It starts with how the data is read and prepared, with the business context that is already in the system left intact. A structured path out of a mission-critical system and into modern analytics, rather than a shortcut around it. Handle the preparation layer properly and AI stops being an experiment off to one 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. See it on your own SAP data Read and write SAP data from any analytics or data platform. Book a 30-minute demo, or start a free 30-day trial of the DVW Alteryx Connector for SAP. Comments are closed.
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