Data has been playing an immense role in the growth of an organization since the commencement of digitization in the business industry. And, if you want to stay ahead of your competition, it’ll be vital for you to ensure the same going forward.
I can think of two reasons behind such a declaration. Firstly, you need access to accurate and comprehensive data to analyze your business and strategize its moves.
Secondly, you might understand more about your consumer base and their behavior by observing customer data. It can aid you in finding out their pain points and solving them accordingly.
However, meddling with industry-specific data can be pretty complex.
To complicate it further, data handling tends to vary from person to person. Thus, it becomes difficult for an organization to track and find the most accurate information. And, if you are dealing with something as critical as SAP Data Migration, things might get even more perplexing. Besides, the data you’ve collected can deteriorate over time too.
Hence, having a perfect data management strategy will be vital if you maintain high-end data quality during SAP Data Migration. Please keep reading to know more about it.
What is Data Quality?
Data quality is defined as a measure to determine the condition of a piece of information based on five factors – completeness, time-relevancy, consistency, reliability, and accuracy.
Quantifying the level of your data quality can help you identify data errors that should be resolved soon to further the growth of your organization. That’s not all, however.
It can also help an organization improve its IT system by boosting its operational prowess. The emphasis on data quality is usually intricately linked to business operations.
What are the Attributes of Good Data Quality?
Data accuracy is a core attribute of high-quality data. Apart from this, it has to feature the following qualities as well –
- Completeness of the data (it should be related to the information you are getting)
- Consistency (the aspects of the available data should not have any conflicts at all)
- Uniqueness (there should be a lack of duplicate data)
- Validity (the values of the data are valid and come with a proper structure)
- Timeliness (the available information must be updated frequently)
Working with all of these factors can be beneficial in producing different data sets that are highly trustworthy and reliable. However, if you feel it isn’t enough, you can include a few other dimensions, such as – credibility, reliability, relevance, and usability.
What is SAP Data Migration?
SAP Data Migration, in essence, is the method or process of moving your organizational data to a new SAP-based system from a non-SAP one. It’s an essential part of almost any SAP integration.
So, if you’re considering upgrading, you’ll have to start your journey from the migration procedure. It usually involves the following –
- Data extraction (it involves getting the data from your legacy system)
- Data mapping (in this case, you’ll have to define the relationship between the data you’ve received or retrieved with your legacy system)
- Data cleansing (identifying errors and correcting the duplications and inconsistencies in the data you have received)
- Data profiling (analyzing the completeness and quality of the data), and
- Data transformation (converting the information into a specific format so that you can load it into your brand-new SAP system)
Apart from this, you might have to perform a validation program before starting the process. Using a set of techniques and tools will also be required here.
Is Maintaining Data Quality Important in SAP Data Migration?
Maintaining data quality while migrating to an SAP-based can significantly benefit your case. Let’s keep reading to know more about them.
1: Accurate Reporting
Maintaining data quality can ensure that the information in your new SAP system is complete, accurate, and consistent. It, in turn, allows an organization to create accurate reports.
Therefore, you might use the same information to improve your business growth, gain a competitive advantage, and support informed decision-making.
2: Enhanced Operational Efficacy
Maintaining data quality can allow a more streamlined business process, which, in turn, can lower the risk of reworks, errors, and everything related to it.
So if you can use it properly, the productivity of your operations will shoot up to some extent. And the probability of you making an error would also be much lower.
3: Improved Data Evaluation
High-quality data can also enable you to analyze your data more effectively and help you find many different patterns.
How Do You Ensure Accuracy While Managing Data Quality?
Ensuring data accuracy, especially when managing the quality of the information, will be highly important. Here are some tips that can help you out accordingly.
Tip – 1: Define Your Data Quality Standard
To begin with, you will need to start your journey by defining the quality of the data you have at this moment. Remember, this should reflect the requirements of your organization. So, you must ensure that whatever you do – you’re doing it while keeping your company’s best interest in mind.
This will ensure that you establish the benchmarks for measuring the accuracy of your data correctly. Also, it can help you identify gaps in your core data quality.
Tip – 2: Perform Data Audits Regularly
No matter what you do, performing data audits regularly will be instrumental for you. This way, it will be easier for you to determine the accuracy level of the available data. And it can also allow you to discover how to make the same error-free.
Regular data auditing can include – checking for completeness, maintaining accuracy, and ensuring the information is consistent. It can be done both manually or through a dedicated tool.
Tip – 3: Validate Your Data Inputs.
Making an error in data capture or entry is the most common cause of receiving inaccurate data. If you want to mitigate the risk of the same, you’ll need to ensure that the inputs are proper and valid.
Apart from these, you must ensure no missing data links to it. If you are uncomfortable doing all these through a manual process, using tools might be helpful.
Tip – 4: Establish a Proper Governance Policy.
Implementing data governance policies and procedures can help ensure that data is accurate and high-quality. The process will include the following –
- Establishing data ownership,
- Defining data access controls, and
- Creating data management processes
Each of these has to be done with your team so that everyone may participate and take care of the project without any issues. Also, all of these can be done by using a tool.
Tip – 5: Monitor Data Over Time.
Finally, monitoring data quality over time is essential to maintain data accuracy. This includes implementing processes to identify and correct any data quality issues.
But why should you do it?
You can ensure two things by monitoring your data over time. Firstly, you can recheck the entire thing and identify if something is wrong with the system.
Secondly, it can also help you understand whether what you did with the data is correct. You can repeat the same pattern if it’s going in the right direction.
You should change your strategy and start again if needed.
FAQs – Frequently Asked Questions
In this section, we’ll try to answer some questions that might have come to your mind if you’ve read this article till now. Let’s get you some answers, then.
Q: What are the consequences of poor data quality in SAP migration?
A: Poor data quality might result in inaccurate reports, incorrect financial data, delays in decision-making, and increased operational costs. Furthermore, it can also make your consumers dissatisfied with the entire process and lose your trust.
Q: How can data quality be ensured in SAP data migration?
A: Data quality can be ensured in SAP data migration through data cleansing, data profiling, data mapping, and data validation. It is also essential to involve data experts, define clear data governance policies, and conduct regular data audits.
Q: What are the best practices to ensure data quality in SAP migration?
A: Some best practices for ensuring data quality in SAP data migration include
- defining clear data quality objectives,
- involving stakeholders from different departments,
- using automated tools for data profiling and validation, and
- conducting thorough testing and validation of data before and after migration.
So, that will be all for this article. If you want to know anything, comment in the next section. We will answer your question as soon as we can.
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