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SAP S/4HANA Implementations Are A Challenge


S/4HANA migrations/implementations may have many benefits. But first, you have to successfully navigate many risks and uncertainties. 

According to Gartner, 83% of data migrations fail or exceed their budgets and implementation schedules. It’s been like that for a very long time. Clearly, current implementation and data migration approaches are not working. Astonishingly though, many companies continue with the same approach, use the same tools, and hope for a different result.  

A new approach is needed.  One that ensures you are not in the 83%.  Successful approaches will:

  • Reduce risk
  • Provide flexibility, and synergies with your legacy systems
  • Save much of the human effort involved in your standard implementations.

Understanding Data Transformation Risks when Migrating to S/4HANA


The risks involved with any system migration are significant; business disruption, integration and compatibility issues, and financial risk to name a few. 

No matter how firm your grasp of the situation is, it’s important to understand the risks associated with your project before you begin your SAP S/4HANA migration, assess the potential impact, identify how you will mitigate them and develop a contingency plan. 

Migration activities are very difficult to reverse, and a poor migration strategy risks increased costs and project overruns. With this in mind, to successfully migrate to S/4HANA it’s essential to perform a thorough analysis of your current systems and business processes. 

Key differences across diverse system settings are often misunderstood, even within the same SAP system. Some examples include:

  • Different key relations from finance master data and structures, with production and supply chain 
  • differences in cost structure
  • Different approaches to controlling (Production vs Overhead, etc.) 

All of which can have a significant impact, resulting in increased costs and project overruns.

In a recent S/4HANA migration project, r4apps compared a client’s various SAP systems against over 100 different system structure attributes using our automated SAP-specific analytics capability. 

The result? 

The client was able to better plan and facilitate their transformation program, with a far higher degree of certainty, in less than 10 days. 

How to Tackle the Challenges of Data Transformation


Data transformation is where difficulties usually arise.  It’s a major contributor to why 83% of projects fail to meet their objectives. The process of transforming data from one format to another is a relatively straightforward process for standard items, such as customers or suppliers. However, these approaches don’t effectively deal with the major data transformation uncertainties in complex enterprises. Some of the more common questions that arise are:

How do you transform client-specific elements? 

Elements such as cost centres, materials, and projects where there are no pre-built tools.  These are unique and the usual approaches don’t work. However, r4apps leverages AI and other analytics to manage these unique elements.  This effectively identifies and manages duplicates, missing and non-compliant data, etc.  It easily prepares the data that’s always hard to deal with.

How do you ensure only good data is pushed to the new system? 

It’s hard to fully anticipate upfront what objects need to be migrated (structures, processes, data objects, etc.). This is often not known until you are buried in the migration. This is where r4apps excels, allowing you to identify and transform data on the fly, as you work, in real-time, in iterative steps. No matter if there are hundreds of millions of records. This can save you up to 70% of the effort and budget for data migration, according to Gartner. 

Most current vendor tools are inflexible, slow, expensive and require extensive training and experience. 

r4apps has a unique approach, creating a virtual layer from all existing data sources that is easily understood, auditable and agile. A semantic understanding of the data and use of metadata allows for much faster, more flexible and agile modelling. This means data transformations can be executed on virtual models, without reloading data structures like cubes. Filters are then dynamically forwarded to the source for further improved performance.

It’s important to rethink the way the migration will be managed

Every S/4HANA migration project is a major undertaking and an innovative approach using modern platforms and tools will significantly increase your success. As Gartner’s recent research shows, improvements of up to 70% can be achieved with r4apps. 

If you are looking for a S/4HANA data transformation partner, get in touch today to lower uncertainty and prevent project cost overruns. Or join the 83% of people who’ve tried and failed to successfully navigate their data migration initiatives.