One Data expands SAP BW portfolio: bringing business logic, not legacy baggage, to the cloud

Three new, interconnected solutions provide transparency across existing BW landscapes, reduce legacy complexity, and lay the foundation for AI-ready data.

Passau, October 14, 2026 – One Data is expanding its portfolio for modernizing SAP BW landscapes. With SAP Data Insights, SAP Data Optimization, and Smart SAP BW Migration, the software company offers an end-to-end approach ranging from the analysis and assessment of existing BW structures to their optimization and migration to modern data platforms.

The focus is not on transferring as many technical objects as possible, but on determining which business logic, data, and dependencies an organization needs for the future. Over the years, SAP BW systems accumulate extensive knowledge about how KPIs are calculated, how exceptions are handled, and how information is prepared for business processes. Much of this knowledge is only partially documented.

“An SAP BW migration is not simply about moving data. Companies first need to understand the business meaning behind their existing structures and determine which of them will remain relevant in the future,” says Dr. Andreas Böhm, founder and CEO of One Data. “Simply transferring technical objects into a new environment risks recreating the complexity of the past in the cloud. That is why our approach reverses the traditional sequence: understand and optimize first, then migrate.”

Three entry points along the SAP BW transformation journey

SAP Data Insights first creates transparency across the existing BW landscape. Based on metadata, the BW X-Ray Assessment analyzes usage, dependencies, redundancies, and undocumented logic, among other factors, without moving operational business data. This gives companies a sound basis for deciding which structures should be retained, consolidated, or decommissioned.

SAP Data Optimization builds on this by aligning the migration scope with actual business value. Business and IT teams can jointly decide which data and logic should be retained, harmonize KPI definitions, and preserve the business context of existing structures. At the same time, governance, ownership, lineage, and data contracts are embedded into the future data architecture from the outset.

Building on this foundation, Smart SAP BW Migration supports the technical implementation on target platforms such as SAP Business Data Cloud, BW/4HANA, Snowflake, Databricks, or hybrid architectures. This includes automated analysis of existing landscapes, AI-supported conversion of ABAP logic into SQL or Python, as well as end-to-end lineage and governance beyond go-live.

From semantic model to data product

The approach is based on a semantic model of the existing data landscape. Rather than looking at technical objects in isolation, logic, usage, and dependencies are connected. This makes it possible, for example, to trace where a KPI originates, which transformations affect it, and which reports or business processes depend on it.

Especially in SAP BW landscapes that have evolved over many years, business-critical knowledge is often only partially documented and tied to individual employees. One Data’s semantic approach makes this knowledge permanently transparent and keeps it within the organization, even when experienced developers or business experts leave.

Based on this understanding, companies can define a reduced and business-driven scope. Only then are governed data products created that contain not only the data itself, but also definitions, business logic, lineage, responsibilities, and quality and governance rules.

Automation is applied selectively on top of this semantic foundation. AI-supported methods can convert existing business logic into SQL or native artifacts of the target platform. The key is that they do not merely translate syntax but preserve the previously reconstructed business context.

Less complexity, lower costs, and a stronger foundation for AI

SAP BW landscapes that have grown over many years often contain structures that still exist technically but are no longer required by the business. Without sufficient transparency, such components are frequently migrated as a precaution, creating additional effort for analysis, development, testing, and subsequent cloud operations.

For its approach, One Data cites benefits including up to 80 percent lower migration and cloud costs, 60 percent less time required to build data products, 50 percent less manual data engineering, and 70 percent faster data discovery.

The migration does not end with the technical go-live. The semantic model remains in place as a living documentation of the data landscape, continuing to show which data products exist, how they are connected, who is responsible for them, and whether they meet defined quality requirements.