Managing Data through your Cloud ERP Transformation
We recently reviewed some general considerations for a cloud ERP migration, including moving to a shared environment, inability to customize, and changes to reporting structure. As referenced, in order to navigate these changes, data management needs to become a primary workstream of your digital transformation. Focus and timing are two key components for managing data through your cloud ERP migration:
Back in the day (10+ years ago), it was standard practice to put data in the back seat. It was a concern, but not a big one. Now, it is one of the primary workstreams of your transformation. If it is not given the attention deserved early on, it will later rear its head and become a serious risk for transformation failure. Consider the following:
We had mentioned previously that because implementing companies are often not able to bring over their previous custom requirements into a more generic shared cloud application environment, they are facing increasingly complex integrations into ancillary applications and databases to replace their necessary business process customized functionality. This integration effort will require extra focus on data, meaning you cannot assume data will simply transfer to and work properly in the new system. With each point of integration or each API, you will need to thoroughly test and, in many cases, restructure your data according to required new data format.
In the case of a large ERP conversion, there can be a huge list of “RICEFW” (Reports, Integrations, Conversions, Extensions, Forms, and Workflows) that will mean developing a “RACI” Chart (a matrix for each item indicating the persons who are Responsible, Accountable, Consulted, and Informed). Each item on the RICEFW will need to be reviewed for potential adjustments, tested, and then redocumented. That’s a lot of work! And if you wait until you are in the later stages of the project to begin building your RICEFW and tracing the data you are currently using that supports these items, then you will likely run out of time and be forced to skip vital testing, crossing your fingers that you didn’t break anything critical.
Under new accelerated implementation models, System Integrators are no longer gathering extensive requirements, and are instead fitting clients into a standard, off-the-shelf functional model. This new focus on retrofitting into pre-programmed processes, rather than building processes from scratch, puts extra stress on data modelers who need to ensure that the data produced by the new system will be congruent with the legacy system’s data outputs. Many careers have been thwarted by rushed and poorly planned or executed ERP and Digital Transformation projects because the new C-suite reports just don’t line up with the legacy reports that were produced prior to Go-Live.
Referring again to “back in the day”, historically data was a component that would receive attention towards the later stages of implementation. Now, with the above referenced issues, it is no longer an option to delay efforts on data planning, management, and cleansing:
- You should ideally begin your data strategy with an initial Data Assessment conducted during your system or system integrator selection process. This will de-risk your project implementation timeline and could reveal vital information which could impact how the system is to be configured, as well as associated timing and costs. This “head start” saves you work later during the project crunch time and can shed light early on for high-risk areas of your upcoming project. We see numerous cases where delays in the start of the project’s data effort adds unnecessary risk and costly friction to the entire implementation. By assessing early on how, where, and why data is used in your existing systems, you will better understand the trouble you may run into when trying to migrate your data into your new system. This perspective will be essential in proper implementation planning.
Due to accelerated implementation models and the increased stress on data outlined above, data efforts must begin early to keep the implementation on track. Data cleansing can be a tedious, time-consuming activity which ideally should be tackled prior to implementation kick-off. The benefit here is that you are accessing data owners’ availability while they have capacity during the ramp-up period between system selection and the official start of systems implementation. During implementation everyone is stressed to the max. Adding data cleansing duties during implementation will often lead to errors and gaps because people are so distracted with other aspects of the transformation.
Along with completing an assessment prior to implementation, be sure to have dedicated data experts as part of your implementation team. They will need to be part of planning to make sure data is managed properly and will be responsible for the quality of data that goes into the new system. This is especially important if you are on a rapid or agile implementation track. When pressed to sprint, people will get tired, mentally drained, and data will become an afterthought.
If you have questions or would like to discuss more on how you can best prepare for a cloud migration, please reach out. We also welcome you to subscribe to our blog and receive current posts on data management, security, and technology transformation best-practices.
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