![]() ![]() This block will show how some of the key master data products are integrated: Data Services and Data Quality, SAP Information Steward, and SAP Master Data Governance (called MDG from here on out). Use Cases on IS VS DS Our second topic is Data Services with MDG The table below contains some examples of how the products fulfill the use case requirements. Some of the typical use case scenarios where Information Steward and Data Services work together to provide a solution, include ETL/Data Warehousing, Data Quality Management, Enterprise Master Data Management, IT/System Maintenance, Business Intelligence/Reporting, and Data Migration. Other Use case scenarios with the same combination And, in the case of that data warehouse, Data Services provides you the means to capture the change in order to perform delta loads on a regular basis.De-duplication can be performed when redundancy needs to be eliminated when bringing together multiple sources of similar data.Data Services can additionally be used to cleanse the newly acquired data to meet the quality standards your organization has in place.Then, based on the results of the data profiling and assessment, Data Services can be used to transform the data to standardize the data from multiple sources to meet a common data warehouse or system schema.In both use case scenarios, Data Services is going to provide you the broad connectivity to databases, applications, legacy systems, and file formats that is needed to support your requirements for data extraction and loading. ![]() Conclusion of Information Steward on Migration Vs Warehouse scenarios: If there are duplicate customer or product records found across systems, those records (or a portion of those records) can be manually reviewed with Information Steward’s Match Review feature. ![]() If cleansing needs to occur on the source system due to poor quality, the Data Quality Advisor and Cleansing Package Builder can support you to quickly and easily develop the needed cleansing and matching rules. You can also perform a data assessment by running the new source system against your already established data standards/quality rules within Information Steward. Again, is there a need for mapping tables or data standardization to be used as a part of the data migration process?.Is the content in the newly acquired source system of similar format, structure, or type to your corporate system(s)?.With a data migration project, let’s say one that arose as a part of an acquisition, Information Steward will help you gain familiarity with the newly acquired source system through data profiling, helping you to understand: In addition, advanced profiling can help you to identify referential integrity problems.įor example, Information Steward could highlight the fact that the CUSTOMER_DETAIL table contains ORDER IDs that do not exist in the CUSTOMER and CONTRACT table. Profiling results such as word, value and pattern distributions will help you understand the need for mapping tables, or perhaps standardization of the data during the ETL process. Let’s focus on two use case scenarios, data warehousing, and data migration follows.įor data warehousing, Information Steward is going to support you in analyzing your source data to understand what content is available at the source as well as the quality of that data. Data quality component along with Data warehouse.We will explore use cases, features, and architecture that make these two solutions the very best of friends.Īs a Data Solution architect, we always get majorly 2 kinds of scenarios: In this blog post, we are going to cover both an internal and external view as to why. SAP Information Steward and SAP Data Services and Data Quality are indeed inseparable and complementary solutions. Okay – let’s jump to the original topic here as terms and conditions are clear (else refer to my Note above again □ )ġ st I will go with Information Steward with Data services and followed by a combination of Master data. ![]() Note: I am composing my views based on my recent experience with a top IS-Retail customer and a very convoluted solution delivery of Master and billions of transactional data. Here I put my views on both Data Services and Master data. We always have a discussion (honestly an argument □ □ ) on this burning topic – Information Steward is the best fit with Data Services or MDG. ![]()
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