When introducing new systems or applications to your company, it’s necessary to go through the data migration process, which consists of moving data from one place, format or application to another. Nowadays, data migration is happening a lot when companies move from on-premise datasets to cloud-based storage and systems to gain productivity with better costs.
Cloud-based business software and applications are becoming more popular as companies are making the definitive move to cloud solutions, especially when cloud tools can fix and work on most of the problems that the on-premise tools face constantly and have features that make them different from the on-premise applications.
One of them is the possibility of making the data migration much faster with rapid deployment solutions and rapid data conversion. The rapid data solutions – also known as RDS – are a type of pre-configured application that allows companies to make systems and software deployment in less time, usually for a few weeks or a few months, against the long time that it takes to deploy data migration with on-premise systems.
RDS is not a one-size-fits-all application, and it can be designed and modified according to your company’s needs or the SAP functionality you have available to work with. But according to SAP itself, the standard rapid deployment solution they provide is made to meet around 70% of all business’s needs and the rest can be customized.
Normally, applications come with a step-by-step guide with a standardized approach on how to perform the data migration deployment with all the needed assets required during the process, but there are some pretty common steps to be followed in order to perform data migration with rapid deployment solutions.
What Is Rapid Data Migration And Deployment?
Rapid data migration is the process of full data transferring that enables your company to migrate the information and keep the accuracy, trustworthiness and readiness of everything while keeping the integrity of the applications.
The SAP rapid data solution for data migration and deployment will allow the users not only to ensure data quality, but also optimize it. It’s compatible with most software and will transfer and store everything with maximum efficiency.
Both SAP and non-SAP systems will go through a one-time migration process in which the old system will be retrieved and incorporated into the new one with all relevant information with minimized downtime and possible errors that can appear during it.
Also, migrating your data with rapid deployment solution will transfer the information and the structure with the help of:
Mapping Process To Analyze The Sources And Destinations;
Verification of the destination to ensure that the format matches with the source;
Validation that the source was working on the old application and will work on the new systems;
Enrichment of the data into the destination and new datasets.
And with that, there are generally three main options for data migration with rapid deployment solutions:
1. New Implementations
This option is for the companies that need to retrieve information from old and complex on-premise systems and can use the rapid deployment solution from the ground and upgrade it to make a one-time migration.
2. System Conversions
This deployment option is more commonly used by companies that need to convert their current SAP system to a new and updated SAP system as well. If the structure is in place and only the data needs to be transferred, this is the best option to cause the least amount of downtime and process disruptions.
The transformation option is normally used by companies that need to migrate information to consolidate various systems into one. It normally involves an update in the current on-promise or cloud SAP systems.
How To Migrate Your Data With Rapid Deployment Solutions
Below, we’ll list a quick how-to guide on which steps to follow in order to successfully migrate your data with rapid deployment solutions.
Step1: Start by Analyzing the Source and Target Applications
Before starting your data migration with rapid deployment solutions, the target environment and the source application must be compatible, and you have to ensure that the information will be transferred right without any damages in order to maintain data quality and accuracy.
When working with SAP applications, the rapid deployment solution has a data migration package that will accept many types of data from non-SAP environments, from connectivity to databases, legacy applications, flat files, or XML.
Step 2: Make the Data Extraction and Create a Profile for Them
In the second step, the data will be extracted, and it’s important to profile them from the source application. Profiling the data is a key step to having the right insights on what’s the state of data and the sources.
It’s on the second step that you’ll check if there are patterns across the data or any inconsistencies – for example, if you’re dealing with ZIP codes you may need to check if all of them have five digits or if any of them is a four-digit one. Any patterns or inconsistencies found, need to be addressed on this step, or the destination datasets won’t have accurate and consistent data.
Step 3: Time to Clean, Transform and Validate the Data
This step should be completed along with the second one, in which the data has to be updated and transformed to meet the needed patterns your company may need and according to the rules and validations of the system you’re using.
The updates and transformations can involve several factors, including the combination of two fields into one, splitting one field into two or more fields, updating the data to match the right format (like ZIP codes and phone numbers), and validating the data to ensure that it’s right to the system configuration.
Step 4: Apply a Data Reconciliation Processes
In this step, it’s time to look if the data loaded was as expected to be loaded. This can be achieved through data reconciliation processes, in which the data is verified after a migration process, so the target data gets compared against the original source data to check if the migration was done correctly.
During data migration, even with all the verification and standard procedures, issues can still happen, like a network problem or a broken transaction, so at the end, the data arrives at the destination with problems that will make it invalid, like missing records and values, incorrect values, duplicated information, and so on.
If you don’t run a robust data reconciliation analysis, those problems can pass without being noticed, damaging the data quality and accuracy, leading to inaccurate insights and flawed decision-making.
Step 5: Dashboards and Reports Developments
During the whole migration process, dashboards and reports are usually provided by the systems, so all the people involved in the project and all the data users can see the status of the migration with full details. Some SAP rapid deployment solutions provide additional reports to show if the data quality is as expected and if the management is according to the governance.
Data migrations projects are relevant for all companies that deal with data and at some point will need to move this data from one system to another. But migrating data is always a major task, with multiple people and departments involved, and can be a major headache if not done appropriately.
That’s why doing migration projects with rapid deployment solutions will avoid a myriad of problems, from delayed production to bad quality data, missing data, and issues with the go-live of the new applications.
Cleaning the data, transforming it to make it attend to your business needs while fitting well to the destination system, and loading it into this new application will require some care, but it’s much easier and safer to complete it with rapid deployment solutions.
Most data migration software will make the steps mentioned below until the data is good enough to be loaded into the target application:
- Profile and extract all the information from the source system
- Map the sources and the targets
- Validate if the data has patterns
- Reconcile the data between the source and the target
- Repeat everything until the data is 100% correct and no inaccuracies are found.