SAP BW Migration as a Strategic Leadership Task: When Data Is Not Trustworthy, AI Scales Bad Decisions

Passau, March 24, 2026 – In many companies, the migration of SAP Business Warehouse is still primarily viewed as a technical IT project. From One Data’s perspective, this view falls short. The real challenge lies in using the transformation to create a data foundation that business teams, management and, increasingly, AI systems can trust. Because when data is not trustworthy, AI does not scale business value — it scales bad decisions.

In many long-established SAP BW landscapes, several parallel “truths” exist today. Dashboards contradict one another, KPIs have evolved historically and are defined differently, and the origin of figures is often just as unclear as their business meaning or ownership. As a result, many migration projects may succeed technically but fail from a business perspective because existing problems are transferred unchanged to the new platform.

“The key question is not how companies can replace SAP BW as quickly as possible,” says Dr. Andreas Böhm, Founder and CEO of One Data GmbH. “The key question is what data foundation future decisions will be based on — and whether that foundation is trustworthy at all. If data is semantically vague, contradictory or not organizationally governed, AI will not scale the value of a company. It will scale bad decisions.”

AI Readiness Starts with Trustworthy Data

The increasing use of AI makes this situation even more critical. AI systems do not merely analyze data; they operationalize it. They automate processes, support decisions and, in some cases, directly influence business operations. For companies, this means that AI readiness does not start with models, agents or tools. It starts with data whose semantics, origin, timeliness and responsibilities are clearly defined.

At the same time, many organizations are under growing pressure to modernize their SAP BW landscapes. Mainstream support for SAP BW 7.5 is currently scheduled to end at the end of 2027. Yet this pressure often leads to a consequential misconception: migrating as much as possible as quickly as possible. In practice, a significant share of existing data, reports and objects is often no longer actively used. Business-critical structures, however, are frequently particularly complex and insufficiently documented.

Companies that rely on a one-to-one migration in this situation are not only investing in irrelevant data; they are also carrying the most critical business problems unchanged into the target architecture. In cloud environments in particular, this has direct economic consequences. Unnecessary data and inefficient structures not only increase migration effort but also drive long-term operating and storage costs.

Before a migration begins, companies therefore need transparency across their existing BW landscape. Which data is actually being used? Which logic is business-critical? Which dependencies exist? Where are redundancies? Which KPIs are bindingly defined — and which exist in multiple versions?
Business Logic and Governance as the Key to Transformation

The real value of an SAP BW landscape lies not only in the data itself, but in the business logic that has been built up over many years: transformations, KPI definitions, semantic models, process knowledge and business rules. Transferring this logic without scrutiny perpetuates existing complexity. Rebuilding it entirely, on the other hand, carries the risk of losing valuable knowledge.

Successful migration strategies therefore deliberately distinguish between business logic worth preserving, redundant structures and necessary architectural redesign. Modern data architectures should not simply replicate old BW structures. Instead, they should transform business-critical data into transparent, governed and reusable data products.

Data products can help migrate central process fragments in a controlled way, including business logic, quality rules, documentation, access rights and responsibilities. They provide a structured framework in which data becomes not only technically available, but also understandable from a business perspective, quality-assured and usable — for reporting, analytics and AI applications.

Automation and AI can significantly accelerate this process and reduce manual effort. However, their value only materializes when there is clarity about data, logic and the target architecture. Automation does not replace architectural work. It amplifies good decisions — or scales bad ones.

Governance must therefore be an integral part of the target architecture, with clear ownership, binding KPI definitions, documented transformations and end-to-end lineage. Only then can companies create a data foundation that is not merely migrated, but sustainably trustworthy, auditable and AI-ready.

“The SAP BW migration is not just an IT project. It is a strategic intervention in a company’s ability to steer itself,” says Böhm. “CIOs who approach this transformation purely technically will replace systems. CIOs who lead it strategically will renew the foundation of corporate decision-making. That is where the real value of the migration lies.”