Today’s enterprises face a dynamic landscape. As data continues to increase, so does the complexity of the SAP landscape.
To ensure a smooth simplification, an agile business needs to formulate streamlined data management strategies before it even begins the landscape simplification process. After all, a business that properly manages its data will find that many of the steps also promote landscape simplification.
The SAP Data Management and Landscape Transformation (DMLT) platform provides some enlightenment into the process.1 For example, the system focuses on how current business processes and models can provide the best ways to use and apply data, along with allowing the business to migrate its data to updated SAP systems. To do so, it employs tools to maintain high data quality, ensure that data is protected, and assess data management processes.
Andrea Pagliari and Koustubh Waikar from SAP’s Cloud Success Services provided an insightful presentation on SAP DMLT, which goes into depth regarding the system and its applications.2 As slide 12 of the presentation indicates, data maintenance and data migration into the current SAP systems are not the only factors of successful data management. The business must also consider applications (for example, mergers and acquisitions), system consolidation, and other crucial aspects to connect data management to genuine SAP landscape simplification.
With this context in mind regarding data management and its importance in SAP landscape simplification, let’s take a deeper dive into the individual components.
Data Maintenance/Migration
First and foremost, a business that successfully uses data management for landscape transformation needs to ensure that the data is properly maintained as it is migrated to the updated SAP system. Below are the basic steps:
- Documentation: Collect all essential documentation related to the existing landscape, encompassing system architecture, data models, and data flow diagrams.
- Data Analysis: Examine the data to pinpoint information that is obsolete or suitable for archiving.
- Data Cleansing: Purify the data by eliminating duplicates, inconsistencies, and errors.
- Data Migration: Transfer the purified data to the new landscape.
- Testing: Validate the migrated data to guarantee its accuracy and completeness.
- Training: Provide users with training on the new landscape and its associated data.
Mergers and Acquisitions
While Mergers and Acquisitions (M&A) can be crucial business steps to ensuring a strategic, agile organization, they can also create a cluttered SAP landscape with many applications and data sources. As a result, the business needs a quality data management system for M&A, so they can ensure their processes and applications run smoothly. The process of evaluating and clearing through those and similar applications is known as application rationalization. The steps you should take during this process include the following: 3
- Conduct an initial assessment of the applications. Consider what applications you have that are legacy, mislabeled, etc.
- Evaluate how well each current application provides business value (if it does at all).
- Even if an application provides business value, you may have similar, more cost-effective options. What is the TCO (Total Cost of Ownership) of each application?
- With the business value and TCO of each application in mind, evaluate both of those factors to find a proper balance.
- Again, an application may provide business value. However, you should consider how efficiently the application does so, and whether you can find more efficient, productive options.
All of these considerations are known as success metrics. While these are the most crucial metrics, you may also consider aspects such as overlapping functionality, a potential decrease in TCO with time, etc.
System Consolidation
Once you have properly maintained/migrated the data and rationalized applications such as M&A, you are ready to complete the system consolidation and fully simplify your landscape.
- Using a similar procedure to the M&A and other applications, conduct an analysis of other hardware, software, and network infrastructure. This way, you are aware of the TCO, business value, and efficiency each item provides.
- Fully define your target landscape, considering the current state of the market, strengths and weaknesses of your competitors, and best techniques to appeal to your target audience. This way, you can further analyze your success metrics and know which items are best to consolidate.
- Take a look at the backend to ensure the systems are ready for consolidation. For instance, you should archive and back up your current data, along with conducting initial testing.
- Conduct thorough testing and of the landscape to ensure that the data is properly transferred and the system performs well.
- Deploy the consolidated system. At this time, prioritize knowledge transfer and create a learning organization. Continuously monitor the system to avoid any serious setbacks.
Conclusion
As organizations face a constantly changing landscape, data has become more important than ever. Data continues to take on more forms and storage techniques, so businesses can easily find themselves with a scattered, cluttered landscape. Because of this, a business that successfully works with data and landscape simplification needs to be agile. As long as a business fully assesses each component for business value, TCO, and efficiency while considering their current market/competitors, it has taken a strong step towards that agility.
1 “Data Management and Landscape Transformation Services.” https://www.sap.com/services-support/service-offerings/data-mgmt-landscape-transformation.html
2 Don’t let data stop your journey to SAP S/4HANA: Data Management Landscape Transformation. https://assets.dm.ux.sap.com/webinars/mea-sap-services-emea-south-intelligent-enterprise/pdfs/20230328_sap_dmlt.pdf
3 “Application Rationalization: A Guide to Streamlining IT Infrastructure.” https://www.apptio.com/topics/application-rationalization/
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