Introduction
SAP Historical Data Migration, in essence, is a process that moves data from a legacy system into a new SAP infrastructure. In this case, the data transportation can be done from a non-SAP or an SAP-based environment without any problem.
The process of SAP Historical Data Migration usually includes –
- Data extraction,
- Transformation, and
- Loading
This entire procedure, known as ETL, is used in almost every SAP-based transformation for better data handling and management.
The Historical Data Migration process is crucial, as it can ensure that the historical data in the old SAP system stays adequately preserved – even when you have transported it in the newer one.
The data might play an important role when tackling a specific business operation or while complying with a financial auditing and reporting regulation.
In this blog, we’ll explore the following to enrich your knowledge about the procedure and how you should proceed with it. So, without any further ado, let’s get started with it.
What Does the Procedure Look Like?
The SAP Historical Data Migration is a complicated procedure. Thus, we have run a step-by-step guide here to help you understand how everything works.
So, let’s begin.
Step – 1: Data Analysis
Firstly, you must evaluate the data you are trying to transport to the SAP infrastructure from the legacy system. This, in turn, can help you identify the relevant information you want to migrate. Moreover, the evaluation will also help you determine the scope of the migration –
- The volume of the data
- The type of information you are working with
- The quality issues available in the data
It would be best to record everything mentioned above and promptly address the issues. Once you finish it, you must focus on the next step…
Step – 2: Data Mapping and Transformation
Data mapping is the procedure of matching the fields of one database to another. It’ll be crucial for almost any data migration procedure, as it ideally helps you organize the information you currently have. It usually doesn’t take too much time to complete too.
The process above usually involves the following steps –
- Cleaning out the data you are working with
- Validating whatever information is available within it
- Enriching the data with additional stats and informational backing
The better you map out the entire thing, the easier it’ll be for you to meet the quality standards of the SAP system. It might also reduce the time consumption of the process to some extent.
Step – 3: Data Extraction and Loading
Once the mapping procedure is done and dusted, you can extract it from the legacy or older system. It can be done manually, or you may automate it with a tool or script.
The extracted information is typically stored in an intermediate file so that you can load it into a system. If you don’t map it out correctly, it might affect the entire procedure.
Then, you must load the data you extracted from the older infrastructure carefully. This has to be done either through a third-party program or a data migration tool offered by SAP.
The data is usually loaded in the new system in batches so that you can manage the procedure well while reducing the time. It’ll also lower the risk of your system getting overwhelmed.
Step – 4: Data Validation and Verification
Once you have loaded the data, you must verify and validate it accordingly. The step will involve running a thorough data quality check and comparing the same to all the original data from your legacy system. The validation can be done through –
- A specific software program
- Through a manual process
The former will take less time than usual, while the latter will be time-consuming. So, we ask you to talk to your team and consult about the best option for your company.
Step – 5: Data Transformation
Data transformation is converting information from one specific format to another. The process might include –
- Converting different data types
- Aggregating the available information you are currently working on
- Applying the business rules you’ve recently created
Once the data has been transformed, you can focus on loading them into a single format. This way, the level of time consumption of the process will get lowered pretty soon.
FAQs – Frequently Asked Questions
We’ve only talked about what SAP Historical Data Migration is and how it works. So, in this section, we’ll offer additional information regarding the same that we didn’t previously. And it’ll be in a question-answer format, too, so you can understand it better.
Let’s get started with it, then.
Why Is Historical Data Migration Important In SAP?
Historical data migration is essential in SAP as it helps organizations to have a complete record of their past transactions and processes. This data can be used for auditing purposes, reporting, and analysis.
What Are The Challenges Of SAP Historical Data Migration?
Some common challenges of SAP historical data migration include data quality issues, mapping, transformation, and validation. These challenges can lead to data loss, errors, and inaccuracies in the SAP system.
How Long Does It Take To Migrate Historical Data To SAP?
The time required for historical data migration to SAP depends on various factors such as the volume of data, complexity of the data, data quality, and available resources. Typically, historical data migration can take a few weeks to several months.
How to Ensure The Accuracy Of Historical Data During Migration To SAP?
To ensure the accuracy of historical data during migration to SAP, organizations should follow a comprehensive data migration strategy that includes data mapping, data cleansing, data transformation, and data validation.
It is also essential to involve key stakeholders and subject matter experts in the process to ensure the completeness and accuracy of the data.
Conclusion
When it comes to working with Historical Data Migration, it’ll be vital for you to ensure that you’re testing everything properly. Or else the entire integration system might get into an inconclusive state of an error. Taking the help of an expert might be helpful in this matter too!
