How to Approachan SAP BW Migration
Approaching an SAP BW migration successfully requires one decision: whether to move your data as-is, or to first extract business logic and rebuild it as governed data products.
Organizations that migrate meaning – not just data – arrive with a clean, trusted foundation. Lift-and-shift merely relocates decades of complexity onto a more expensive platform.
Why organizations are migrating SAP BW now
Mainstream maintenance for SAP BW 7.5 ends in December 2027, with extended maintenance available until 2030. However, extending beyond 2027 comes with significantly higher costs¹.
At the same time, SAP is steering customers toward SAP Business Data Cloud, SAP Datasphere, and BW/4HANA, while many organizations are adopting Snowflake or Databricks for greater flexibility across structured and unstructured data.
Two forces make waiting expensive. The pool of specialists who understand classic BW is shrinking, and the cost of maintaining aging systems continues to rise while delivering no new business value.The deadline is not the reason to migrate well. It is simply the moment the decision can no longer be postponed.
¹SAP charged approximately 2% of net licence value per year for Extended Maintenance on top of existing Enterprise Support fees. That means enterprises already paying 22% annually see their effective maintenance rate rise to approximately 24% during the extension period. For an enterprise with €50M in SAP licence value, that’s an additional €1M per year — purely for maintaining the status quo on a product SAP wants to retire.
Why SAP BW migrations fail
Large SAP transformation programs have a poor track record.
A 2025 study by Horváth ↗ found that more than 60% of SAP transformation projects exceeded budget and schedule, while nearly two-thirds failed to meet quality expectations. The most cited causes included expanding project scope, underestimated migration effort, weak governance, and insufficient planning.
The common thread is not technology. It is visibility. Organizations often begin transformation before they fully understand what exists in their current landscape.
Every SAP BW migration must answer three questions before it can succeed:
What does the system do?
Why does it do it?
Does it still need to do it?
Three predictable patterns of failure emerge when these answers are missing.
Knowledge loss
A typical BW environment is the result of 10 to 20 years of organic growth – thousands of InfoCubes, DataStore Objects, MultiProviders, and transformations, often built by people who have long since left. Documentation is incomplete or outdated, and the original intent behind logic is lost. Teams are forced to reverse engineering rules that no one fully understands anymore.
ABAP complexity
A significant share of business logic lives in custom ABAP code – start routines, end routines, expert routines, and field-level transformations that encode business rules, filters, and exceptions.
Modern SAP platforms perform best with SQL. This means migration is not a translation exercise, but a re-engineering effort: understanding what each piece of logic means, not just how it is written. This is where most projects lose time and budget.
Technical debt
Over years of incremental change, systems accumulate redundant objects, overlapping logic, and inconsistent definitions. Left unaddressed, this technical debt is recreated on the target platform, increasing both operational complexity and cloud costs.
SAP BW migration approaches:Which strategy is right for you?
Every SAP BW migration follows one of three strategies. The difference is not where you end up, but how much of the legacy system you are willing to question.
| Lift-and-shift | Brownfield | Greenfield | |
| What it is | Move existing objects 1:1 to the target system | Preserve and selectively modernize existing logic | Rebuild the data model from scratch around business needs |
| Business logic | Recreated as-is, including flaws | Retained, with key logic optimized | Redefined; legacy logic referenced, not reused |
| Speed to value | Fast start, slow realization of value | Balanced | Slowest upfront |
| Cost & risk | Low effort now, high cost and risk later | Moderate, controllable | High effort now, lowest long-term debt |
| Best when | Time pressure outweighs everything | Existing logic has value but is inconsistent | The legacy model no longer reflects business reality |
A decision framework
The right approach depends less on ambition and more on the condition of your landscape.
You cannot choose a migration strategy intelligently without understanding what is running in your system.
A practical way to decide:
Lean greenfield
when the legacy model no longer reflects how the business operates today.
Lean brownfield
is the right choice for most organizations – when logic still carries value but is buried under complexity and redundancy.
Reserve lift-and-shift
for narrow, time-bound cases where short-term deadlines outweigh long-term architectural consequences.
The semantic migrationapproach
A semantic approach starts by creating visibility into the BW landscape. By mapping dependencies, lineage, and embedded business rules, organizations gain the context needed to make informed migration decisions.
The goal is straightforward: understand the landscape, modernize the logic that matters, and establish governance that prevents complexity from returning.
The result is a migration that preserves business knowledge while reducing technical debt and creating a more trustworthy foundation for analytics and AI.
How data productspreserve business logic
The greatest risk in any SAP BW transformation is losing the business intelligence embedded in calculations, transformations, and process rules. A data product prevents this loss by packaging logic in a reusable, platform-independent form. Each data product contains:
Business logic
definitions and calculations expressed independently of any platform
Data contract
quality standards, access rules, and service-level commitments
Documentation and Lineage
traceability from source to metric, ensuring shared understanding
Why this mattersfor AI and analytics
Migration decisions determine the quality of future analytics and AI.
AI systems are only as reliable as the data they consume. When data contains undocumented assumptions or inconsistent logic, AI outputs scale those errors with confidence. A semantic, governed data foundation solves this by ensuring:
- Metrics are traceable
- Definitions are explicit
- Data quality is enforceable over time
This makes the semantic layer not just a migration tool, but a long-term foundation for analytics, KPIs, and AI-driven automation. The same foundation that makes migration safer also makes analytics more trustworthy and AI more reliable.