SAP Landscape Transformation Services
Landscape Transformation (LT), also known as System Landscape Optimization (SLO), provides a holistic set of solutions to transform, adjust, convert, harmonize and migrate a company’s data.
LT/ SLO helps solve business challenges such as merging a new acquisition, divesting a company or other line of business, restructuring a corporation, harmonizing master data or even consolidating the IT landscape using SAP’s tools.
A go-live conversion is performed in the production system after a series of tests on sandbox copies of the customer’s production environment. This approach avoids extensive configuration and reimplementation. A go-live is typically executed during a downtime period over a weekend.
SAP LT/SLO solutions are available to all SAP customers. The types of services are commonly grouped by the following business needs:
- Mergers & Acquisitions
- Corporate Restructure
- Business Process Optimization
- Consolidate IT Landscape
Landscape Transformation (LT) uses a domain-based approach to ensure that all data is converted in a consistent manner. Oftentimes more than one service can be combined into a single project to expedite the process. The breadth and depth of LT services ranges from carving-out company code, changing ownership of plants, harmonizing a chart of accounts, merging or renaming master data, or custom scenarios to satisfy a particular business need.
More details on the SAP LT solutions can be found here.
Landscape Transformation (LT) ensures consistent data transformation across the complete data model of a SAP system.
All relevant open and historical transactional data is converted regardless of status.
All relevant master data objects are adjusted.
All relevant configuration data is adjusted. Relationships that may exist due to organizational data and substructures are kept consistent.
Test Cycles on Production-Level Data
Landscape Transformation (LT) runs test cycles on copies of the production system in almost all cases. This allows testing on similar data volume to the final production run and can provide runtime estimation to assist to provide downtime information. The conversion is not run on the production support landscape in order limit impact since the converted system no longer reflects production. Taking this approach helps avoid the unexpected scenarios during go-live and reduces the impact to the production support landscape.
Tool Approach: LT is ABAP based and performs all the changes at the database level table by table and not at the application layer. The outcome is a system that will appear as if it always existed in that manner.
Transports: LT is delivered with the DMIS add-on as well as transports and any programming changes are captured to transports during each test. The configuration and generation of conversion programs occurs within each test cycle and not via transports.
High Degree of Flexibility
Ability to combine different scenarios and provide iterative adjustments within test cycles.
Package approach: The LT tools have multiple scenarios that can be handled. Many of these can be combined in a single project. Scoping sessions with provide customers with feedback to decide on the best approach.
Iterative approach: Test cycles are on copies and not the production support landscape, allowing for incremental additions and reconversions of subsets of data in many cases. This allows more flexibility in testing each cycle with no impact to the production landscape.
- Little to no impact on business process due to tool and methodology that ensures complete system data consistency.
- All data regardless of state (open or closed), transactional, master data and configuration is adjusted
- Shorter time to value with projects completed in months rather than lengthy reimplementation efforts
- Production changes typically take place over weekends. Friday afternoon has the orginial data state and by Monday morning all data appears as if it was always in the new state
- Flexible solutions that can allow multiple conversions to be executed in the same effort, reducing costs and downtime.
- Iterative approach based on production data that allows adaptation during the test cycles and smooth go-lives.