For those beginning their journey to SAP S/4HANA, the Selective Data Transition approach will invariably be one of the potential routes under consideration. We covered the SAP S/4HANA route continuum and what Selective Data Transition is in the first of this blog series: “Migrating to SAP S/4HANA? A Primer on Selective Data Transition.” In this blog my aim is to dig a little deeper into what makes Selective Data Transition successful.
I’ll be explaining our 5 Selective Data Transition components:
Organizations initiate a shell conversion because it effectively gives them a ‘leg-up’ for their move from ECC to S/4. The step involves creating a shell of an existing SAP ERP solution in S/4HANA, stripped of all data, leaving just the configuration behind. It gives something familiar to build from without having to start completely from scratch. With no data left in the system this allows you to apply basic modifications to the structure of your new SAP S/4HANA system without effecting data or ongoing operations. Natuvion, a Syniti and SAP partner, are worldwide experts in delivering S/4HANA shell creations and Selective Data Transitions with near-zero downtime. Their solution is included in Syniti’s SDT bundle.
One of the great opportunities of a Selective Data Transition is that it creates scope to overhaul the processes that are causing the most capital leakage or general stress. As with any transition to a new platform, in this case, an SAP S/4HANA shell brings with it the chance to really dig into the processes and re-map them to the organization you have today. For example, it might give your manufacturing operations the chance to re-design their process for inventory or spare part tracking or finance the opportunity to rework purchase orders or invoicing approvals. And as you’re doing this in the blank shell, it also eliminates risk as all day-to-day data in use has been stripped out of the S/4 shell for the time being.
For most enterprises, legacy ERPs are modified and customized over time to support your day-to-day business requirements; while helpful at the time, this typically makes upgrading systems challenging as there are significant technical issues with transitioning customized code to a fit-to-standard platform like S/4HANA. This is where Custom Code Remediation comes in as a service and is a step Syniti strongly recommends during an SDT transformation.
By automating the discovery of legacy code, and carrying this over to the new S/4HANA system in an automated manner, you’d dramatically reduce time, cost and effort of having to redesign that customized process all over again. It also ensures your team recognizes familiar processes within the new S/4 platform. This is a sophisticated effort, which is why at Syniti, we partner with smartShift, the leader in this area who help us deliver fast, automated custom code remediation for S/4HANA SDT programs.
Once the shell has been created, custom code remediated, and transactional and master data transformed, you’re at the point of crossover to S/4HANA. One key component of a comprehensive Selective Data Transition approach is to use automation technology to orchestrate the migration of historical data from the legacy system to S/4HANA via direct ABAP table loads. This simplifies the process whilst maintaining access to historical data, which typically doesn’t require the level of transformation that master or transactional data does, throughout the migration. A crucial value-add of in this manner is that it limits downtime during the crossover to near-zero.
In my view, the most important phase of any SDT or data migration program is the data transformation itself; without taking care of your data, the likelihood of project failure increases exponentially. As you build your SDT program, you must decide which data and business areas will you delete, copy over, or transform as you migrate.
This decisive process determines the length, complexity, and resource requirements of your SDT program; naturally, the more departments/countries/entities you want to transform, the more ‘green’ the project becomes on the continuum.
Being selective enables a nuanced approach in how you treat your data. As opposed to a complete overhaul (greenfield) or straight copy (brownfield), SDT allows you to select those areas that are either the most problematic data-wise or the parts of the business where transformation is poised to generate the most value, fastest.
Transforming data is the treatment for any poor legacy master and transactional data quality, ensuring that any data you move into the shell is fit-for-structure, relevant, timely, de-duplicated, and harmonized. In other words, you want the data you move to be of pristine quality.
From reading this blog post, we hope you’ve got a more thorough understanding of the components that make up a successful Selective Data Transition.
If you’d like some specific guidance into how to drive an SDT forward with Syniti and their specialist partners, Natuvian and smartShift, we are here to help.
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