Data has served as an essential function in the progress of an organisation since the beginning of digitisation in the commercial industry. To stay ahead of your competitors, maintaining a strong approach to data management is no longer a choice—it’s a necessity. But why is this so crucial? Let’s explore two primary reasons.
First, precise and comprehensive data is necessary to understand your business operations and make strategic decisions effectively. Second, by observing customer data, you gain informative insights into consumer behaviour. This can help you identify pain points and tailor solutions that enhance the customer experience.
That being said, handling data within specialised landscapes like SAP can be difficult.
Compounding the issue, data stewardship practices vary across teams, making it difficult for a business to maintain consistency and accuracy. If you’re working with something as crucial as SAP Data Migration, the complexity increases significantly. Additionally, over time, the quality of data can degrade, further complicating the issue.
This is why having a robust data management strategy in place is essential to maintaining high-quality data throughout the SAP landscape. Keep reading to explore best practices for managing data quality in SAP Landscapes and beyond.
What is Data Quality in SAP Landscapes?
Data quality refers to the assessment of information based on five crucial parameters: completeness, consistency, timeliness, reliability, and accuracy. It serves as a key metric for analysing the situation and usefulness of data within an organisation.
Measuring data quality goes beyond mere analysis—it permits you to recognise and address data issues that could hinder your organisation’s growth. By proactively resolving these problems, you can pave the path for more informed decision-making and strategic advancements.
Moreover, high-quality data is instrumental in improving an organisation’s IT infrastructure. It strengthens operational efficiency, enabling your systems to perform optimally and support business objectives smoothly.
What are the Parameters of Good Data Quality?
Data accuracy is an essential component of reliable information. Aside from that, it must also possess the following characteristics:
- Completeness of the data (it should be relevant to the facts you are obtaining)
- Consistency (there should be no contradictions between any parts within the obtainable information).
- Uniqueness (there should be no duplicate entries).
- Validity (data values are accurate and have a suitable format)
- Timeliness (the accessible information should be renewed periodically)
Dealing with all these elements may help you create sets of information that are both dependable and accurate. But if you believe it is insufficient, you may add any additional characteristics, such as credibility, dependability, relevance, and usefulness.
Importance of Data Quality in SAP Landscapes
Knowing the significance of upholding excellent data integrity is critical for maximising the worth of your SAP systems. Preserving reliable data when moving to SAP will significantly assist your case. Let us read further to learn more about them:
1: Accurate Reporting
Ensuring data integrity ensures that the data in your new SAP system is comprehensive, accurate, and consistent. As a result, an organisation can provide accurate statistics. Therefore, you may be able to utilise identical data to boost your firm’s development, acquire an edge over others, and make better decisions.
2: Improved operational efficacy
Upholding data quality can lead to an enhanced company process, reducing the likelihood of rework, errors, and other issues. So, if you employ it correctly, the output of your activities will increase to a certain amount. You would also have a significantly lower chance of committing a mistake.
3: Enhanced Data Assessment
Quality information can assist you in analysing the data more efficiently and identifying various trends.
Best Practices for Managing Data Quality in SAP Landscapes
Guaranteeing accurate data, particularly when controlling accessibility, will be crucial. Here are some pointers that will help you:
- Define Your Data Quality Standard
You must first define the quality of the data you currently possess to get started. Keep in mind that this should correspond to the needs of your organisation. So, anything you do, be sure to do it with your company’s greatest benefit in mind.
This will ensure that you set the criteria for assessing the correctness of your data accurately. It may further assist you in uncovering holes in the calibre of your key data.
2: Conduct data audits regularly
Whatever you do, completing data checks regularly will benefit you. This will make it easy for you to assess the accuracy of the given data. It could also show you how to do it without making errors.
Regular data audits involve verifying comprehensiveness, preserving accuracy, and ensuring data consistency. It is possible to accomplish it manually or using a specialist tool.
- Validate Your Data Inputs
Making a mistake in data capture or registration is the most prevalent reason for obtaining incorrect data. To reduce the possibility, you must ensure that the data entered is genuine and legitimate.
Additionally, ensure that there are no missing data linkages to it. If you are concerned about completing all this manually, employing tools such as cbs MDV or SimpleMDG may be helpful.
- Create a proper management system
Establishing oversight of data policies and processes can help ensure that data is authentic and of high standards. The procedure will comprise the following elements:
- Setting data ownership,
- defining access regulations, and
- implementing data handling processes.
Each of them must be completed in collaboration with your team so that anyone can contribute and take control of the project without difficulty. Furthermore, all of these may be accomplished with the use of a tool.
- Keep track of information throughout time
Finally, consistently monitoring data quality over time is crucial for maintaining data accuracy. This involves establishing processes to identify and resolve data quality issues as they arise.
There are two key benefits to tracking your data over time. First, thoroughly review the data to identify any issues with the system. Second, it allows you to assess whether your approach to managing the data is practical. If things are on the right track, you can continue with the same strategy. However, if necessary, you should adjust your approach and start afresh.
Conclusion
The management of data quality in SAP environments is crucial for the proper functioning and informed decision-making process. By adhering to these suggestions, you can maintain stable, trustworthy, and valuable data. A strategic approach to data management not only enhances efficiency but also lays a solid foundation for corporate longevity.
When it comes to Master Data Management (MDM), cbs Corporate Business Solutions is widely regarded as one of the best in the business. With over 25 years of experience in SAP transformation and MDM projects, cbs offers a holistic, tool-neutral consultancy across the entire master data lifecycle. Their expertise spans strategy development, SAP MDG implementation, data profiling, cleansing, and governance. cbs integrates SAP MDG into tailored MDM strategies that unify processes, systems, and organisational structures—ensuring businesses operate at peak efficiency. Their proprietary tools, like cbs MDV, further enhance data validation and quality, making them a trusted partner for sustainable business transformation.
