Understanding SAP data
In today’s businesses the speed of change is continuously accelerating. And every change in an SAP system drives test data needs. A Test Data Management (TDM) strategy and solution, is critical and enables testers to find and use the relevant test data for SAP systems.
In SAP, data is built up through various modules such as Material Management (MM), Sales and Distribution (SD), Financial Accounting (FI), and Production Planning (PP). These modules allow the entry and management of different types of data, such as material master data, customer master data, vendor master data, and financial data. The data is stored in tables within the SAP database and can be accessed and modified through transactions within the SAP system. It is also possible to extract or input data using API’s (Application Programming Interface) to interface with SAP. The data structure in SAP is organized in an hierarchical manner, ensuring data consistency and integrity throughout the system.
Configuration data and master data are a prerequisite to start testing in any SAP project. All other test data can be generated using the master data.
Understanding the use of Master Data in testing SAP E2E processes is critical, regardless of whether testing is taking place on ECC or S/4HANA, as well as external applications.
SAP has the following data classes:
- Configuration Data: Defines the system and the limits of all elements, e.g. Organizational structure, warehouse set-up, and product-specific configuration (preconfigured settings are adjusted to better fit the needs of the business).
- Master Data: Defines the material-, vendor-, customer-, and financial data and how they will behave in the system.
- Conditional Master Data: Applies only in specific situations (e.g. for this customer and material, use this price)
- Transactional data: Depends on conditional data and master data, includes key operational data.
- Reporting: Depends on transactional activity.
Master data will determine the behavior of SAP (for example condition records).
Ideal test data set
Software development and testing will only be successful with carefully prepared test data. Do not use just some data or just a random test case because in that case you do not know what result to expect. To test a software application effectively, you’ll need good and representative data. The ideal test set covers all relevant application parts with the smallest possible test data set. In short, you need a relatively small test data set that is realistic, valid, and versatile.
Manually generating test data is a time-consuming activity. Without a structured approach it does not guarantee an appropriate test coverage.
Using a copy of the live production data has several drawbacks:
- Development and testing teams will have access to sensitive information, so data needs to be scrambled and/or anonymized (to comply with privacy rules such as the General Data Protection Regulation of the EU, also known as GDPR).
- Creating copies of production data into non-production environments typically requires a lot of time, leading to a lack of environment availability.
- Non-production environments need to have the same capacity as the target landscape, which means that infrastructure costs will be higher than necessary.
- Data requirements can be different for different applications and can have different formats.
Synthetic data is artificial data that is generated from original data and a model that is trained to reproduce the characteristics and structure of the original data. Selecting and using test data in SAP can be time-consuming. Even generating synthetic test data instead of copying data from the live environment, has a number of challenges. Examples are:
- Complex data structures: SAP systems have complex data structures, making it difficult to locate the specific data required for testing.
- Data duplication: SAP systems often have duplicate data, making it difficult to determine which data is relevant for testing and which data is not.
- Data privacy: There may be sensitive data in the SAP systems that needs to be masked or removed before it can be used for testing purposes.
- Data consistency: The data in SAP systems may not be consistent, making it difficult to find and use accurate data for testing.
- Lack of standardization: There may be a lack of standardization in the way data is stored in the SAP systems, making it difficult to locate the specific data required for testing.
- Specific data & systems settings especially for any Test Environment to avoid and block any outbound communication to the real world.
Below we describe a number of possibilities to properly create test data sets for an SAP system.
Sub-setting is the process of selecting and retrieving a subset of data from a larger dataset, based on certain criteria. Data sub-setting and extraction can help to achieve the ideal test data set. The goal of data sub-setting and extraction is to extract the relevant information from a large dataset, so that it can be analyzed and used to support decision making or other purposes. Data sub-setting involves selecting a portion of the data that is of interest and excluding the rest. This process is used to reduce the amount of data that needs to be processed, which can make the testing more efficient and manageable. The criteria used to select the data can vary, but they often involve specific attributes, such as date, location, or type of data. Sub-setting can also be sustainable as less data is stored in the database, so less resources are used.
Handling sensitive data during testing in an SAP system requires a careful approach to ensure that the data is protected and that the testing process does not compromise security and privacy regulations. Here are some key considerations for handling sensitive data during testing in SAP:
During testing, sensitive data should be masked or replaced with fake values that still preserve the overall structure and relationships of the data. This can be achieved using data masking techniques, such as substitution, redaction, or encryption.
Access to sensitive test data should be restricted to only those who have a legitimate need to access it. This can be achieved through role-based access control, which assigns permissions based on the user's role within the organization.
Secure testing environment
The testing environment should be secure and separate from the production environment to prevent unauthorized access or data breaches.
Organizations must comply with relevant laws and regulations regarding the handling of sensitive data, such as the General Data Protection Regulation (GDPR) in Europe and the Health Insurance Portability and Accountability Act (HIPAA) in the United States, these apply even during the testing phase.
It is important to keep in mind that the specific measures required for handling sensitive data during testing in an SAP system will depend on the nature of the data and the specific regulations and laws that apply to the organization. A risk assessment should be performed to determine the specific security measures that are required, and a security plan should be developed to ensure that the data is protected during the testing process.
The end-to-end process in SAP
It is important to keep in mind that the end-to-end (E2E) process in SAP is a complex and interdependent process, and any changes or updates to one part of the process can impact other parts of the process. A thorough understanding of the E2E process is crucial for successful implementation and use of SAP in an organization.
Testing provides a comprehensive view of the behavior of the business processes that are supported by the SAP system. Testing with an E2E perspective shows whether all components of the system work together seamlessly, and if the system meets the business requirements.
Test data plays a crucial role in the testing of the E2E process in SAP, testers typically use a subset of the production data that represents various business scenarios. The test engineer needs to charter the E2E process and detail what’s happening with the master data and transactional data.