The importance of data quality
Jesse Asowata - SAP Master Data Governance Consultant
In an era where data drives every decision, the management and quality of this data is critical for any business. But how do you ensure that the data you rely on is accurate, consistent and reliable?
In this blog post, we explore the role of SAP Data Quality Management (SAP DQM) in achieving this objective. We explore how DQM not only ensures data integrity, but also contributes to better business decision-making, compliance, and cost savings.
In the world of enterprise data and information management, Master Data Governance (MDG) and Data Quality Management (DQM) play an essential role. SAP DQM is an integrated component within SAP MDG for managing, controlling and safeguarding master data. Whereas MDG focuses on managing master data within an organization, DQM focuses precisely on ensuring the quality of this master data.
DQM is available for Products as of S/4HANA 1809 and for Business Partners as of the S/4HANA 1909 release.
Within SAP DQM, it talks about 4 fundamental elements that are essential for effective data management:
1. Define Quality: Defining quality is the first step in the process. Here you establish what "good" data quality means to your organization and what "data quality rules" are needed in the system.
2. Enter Quality: Entering quality data is critical to ensure that new and existing data meet established quality standards. When entering data, the defined "data quality rules" are applied in real time. This ensures that data meets quality requirements from the beginning.
3. Monitor Quality: Monitoring quality is an ongoing process where you regularly assess the state of the data to maintain quality.
4. Improve Quality: Improving quality involves taking actions to resolve existing data quality issues and prevent future problems. Examples include adding new "data quality rules," updating existing data, and optimizing processes for data collection and input.
Features and capabilities of SAP DQM
SAP Data Quality Management thus focuses on improving data quality and does so in several crucial ways. The table below describes the most important functionalities.
| DQM functionality | Description | |
|---|---|---|
| Data normalization and standardization | Normalizes and standardizes data to a uniform format, critical for analysis and reporting. | |
| Data validation and correction. | Checks and corrects data for accuracy and completeness, addressing errors or missing data. | |
| Identification and merging of duplicates | Identifies and merges duplicates to improve the reliability of data in the system. | |
| Data profiling and quality monitoring | Provides tools for analyzing data quality, enabling continuous insight and improvement of data. | |
| Integration with external data sources | Enables linkage to external sources for enriching and deepening data analysis. | |
| Compliance and governance | Supports compliance with regulations and standards by ensuring data meets specific quality requirements. |
Data quality rules
In essence, data quality rules act as the foundation upon which the DQM processes are built. These are rules that are created and activated manually by the organization or can be generated through the rule mining functionality. These rules enable organizations to validate and improve the quality of certain data.
To manage these rules, DQM uses Business Rule Framework plus (BRF+) technology, a 'low-code' solution. This 'low-code' approach to DQM enables a wider range of end users to actively participate in the data quality management process. It allows users to define and manage complex data quality rules themselves without programming knowledge. In addition, it is important to mention that these rules can be easily imported and exported between systems, highlighting the flexibility and scalability of DQM.
To clarify the concept of data quality rules, below is an image illustrating an example of such a rule. This rule is designed to check for the presence of a "Tax Indicator" at all existing suppliers. Through this check, the billing process can be optimized
The versatility of DQM rules is evident in that they not only function on their own, but can also be seamlessly integrated within other MDG processes, such as Central Governance, Consolidation & Mass Processing. For example, the 'Tax Indicator' rule from the example above can also be applied directly when new master data is created through Central Governance. This integration strengthens the scope and effectiveness within the entire data management process.
The use of SAP DQM
The use of SAP Data Quality Management (DQM) is of great importance to companies because of its crucial role in improving master data quality. This improvement brings numerous benefits to organizations.
First, it ensures better decision-making; by providing high-quality data, DQM helps companies gain greater insight and precision in their decisions.
Compliance and risk management are also significantly improved with DQM, as many industries have strict regulations regarding data quality. This helps companies comply with these regulations and minimize their risks. In addition, poor data quality can lead to errors and inefficiencies. By recognizing and improving the quality of this data early, companies can cut costs and streamline their processes.
Here are some real-life examples to illustrate how DQM can help companies in different industries:
A large online retailer is using DQM to standardize and validate product information. This leads to more accurate product descriptions and improved search results, which is essential in a competitive online marketplace.
A bank implements DQM to validate and enrich customer data. This is crucial for complying with KYC (Know Your Customer) regulations and helps deter fraud, which increases the reliability and security of banking services.
In a hospital, DQM is applied to improve the accuracy of patient data. This increases the accuracy of medical records and improves patient care, directly contributing to better care and well-being.
These examples demonstrate how DQM not only contributes to the efficiency of business processes, but also has a direct impact on service quality and customer satisfaction in various industries. This makes DQM an essential tool for any company striving for data excellence and operational efficiency.
Data monitoring, analytics and improvement
As mentioned earlier, DQM offers the ability to perform regular evaluations on the data in your system. These evaluations show the trend in data quality and provide real-time insight into data that does not comply with established rules. Based on these evaluations, improvements and changes can in turn be initiated via single or mass processing, an integral part of MDG. This allows organizations to respond quickly and take action as needed.
In addition to monitoring, DQM also offers data analytics. This analytics functionality dives deeper into this data to identify patterns and trends, enabling companies to act proactively. These insights enable companies to refine their strategies, improve the customer experience, and increase operational efficiency. In addition, integrating data monitoring and analytics into business operations helps support data-driven decision making, allowing organizations to make more informed decisions that lead to sustainable growth and competitive advantage.
Conclusion and outlook
The impact of SAP DQM extends far beyond improving data quality. With SAP DQM, companies are equipped to harness the full potential of their data, leading to informed decisions, improved performance and sustainable growth. It enables companies to make decisions with confidence, comply with regulations and save costs by operating more efficiently.
It lays a robust foundation for companies to not only excel today, but also be ready for a dynamic future. Indeed, SAP recently announced that DQM will remain compatible in Cloud Ready mode.
Furthermore, the integration of advanced technologies such as artificial intelligence (AI) and machine learning promises even greater automation and refinement of processes around data quality. This means that companies will not only operate more efficiently, but also be able to respond more proactively to market changes and customer needs.
More information
For further questions or information on this topic or for other questions on SAP Workflow, Fiori, SAP Invoice Management or SAP Master Data Governance (MDG), please contact Sander van der Wijngaart.
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