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Hi 🙂 ,

We always have a discussion (honestly an argument 😄 😉 ) on this burning topic – Information Steward is the best fit with Data Services or MDG.

Here I put my views on both Data Services and 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.

Okay - let's jump to the original topic here as terms and conditions are clear (else refer to my Note above again 😄 )

1st I will go with Information Steward with Data services and followed by a combination of Master data.

SAP Information Steward + Data Services:

SAP Information Steward and SAP Data Services and Data Quality are indeed inseparable and complementary solutions.  In this blog post, we are going to cover both an internal and external view as to why.

We will explore use cases, features, and architecture that make these two solutions the very best of friends.

Typical Use Case Scenarios:

As a Data Solution architect, we always get majorly 2 kinds of scenarios:

  1. Data Quality with Data Migration and

  2. Data quality component along with Data warehouse

Let’s focus on two use case scenarios, data warehousing, and data migration follows.

Data Warehousing solution/scenario:

For 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.

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.

In addition, advanced profiling can help you to identify referential integrity problems.

For 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.

Data Migration solution/scenario:

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:

  • Is the content in the newly acquired source system of similar format, structure, or type to your corporate system(s)?

  • Again, is there a need for mapping tables or data standardization to be used as a part of the data migration process?

You can also perform a data assessment by running the new source system against your already established data standards/quality rules within Information Steward.

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.

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.

Conclusion of Information Steward on Migration Vs Warehouse scenarios:

  • 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.

  • 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.

  • Data Services can additionally be used to cleanse the newly acquired data to meet the quality standards your organization has in place.

  • De-duplication can be performed when redundancy needs to be eliminated when bringing together multiple sources of similar data.

  • 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.

Other Use case scenarios with the same combination

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.  The table below contains some examples of how the products fulfill the use case requirements.

Use Cases on IS VS DS

Our second topic is Data Services with MDG

This block will show how some of the key master data products are integrated: Data Services and Data QualitySAP Information Steward, and SAP Master Data Governance (called MDG from here on out).

We use SAP MDG for many domains including customer, supplier, finance objects, and materials. Where component re-use has really come into being is in the material arena. We have several different material types from purchased components to internally designed parts. We leverage SAP MDG for both and have reused and repurposed our MDG solutions for each new material type that comes our way. This has been helpful and shortens the time to implement (and cost).

The main use cases addressed are these:

  • Data integration and provisioning

  • Data remediation

  • Validation and improvement

  • Data monitoring and alerting

Information policies inform many of these areas, too.


On the left (1. Extraction Area) are the source systems, and on the right (3. Loading) is the master data hub. When doing so, you want to make sure only accurate and clean data is moving into the hub.

  • Extract the master data from the business systems in flat files and then load these flat files into MDG without any changes.

  • Use SAP Data Services to load data into MDG.

  • But, Use the Information steward to profile the data before the load(this scenario, explained below)


All these options take the data as is, without any cleaning or transformation.

However, You should clean, consolidate, and check against governance rules.

To do this, you should use the Consolidation and Data Quality Services.

My suggestion is Information Steward to inspect the data, and then Data Services to cleanse the data.

If you already have data in your MDG hub, then you need to extract data from the hub before you can find duplicates, cleanse, and then re-load it into the hub. This is shown by the Extractor Framework box on the right. The focus of this session, though, is the Consolidation framework.



For example, these enrichment spots are where you can call Information Steward validations. Follow this process:

  1. Write a validation rule in Information Steward.

  2. Expose the validation rule in Data Services as a web service.

  3. In MDG, call the Data Services web services job in an enrichment spot.

However, it does not work the other way. You cannot expose a validation rule in ERP in Data Services and Information Steward.

There is also an option to connect your own external services.

Current solution

You can use Data quality to check for duplicates, execute validations, and for address enrichment. These capabilities are supported out-of-the-box.

  • Prevent creation of duplicates for increased effectiveness and efficiency

    • Checked early and embedded in the process

    • High detection quality of matching using Enterprise Search or Data Services

  • Validations

    • Re-use of existing validation logic in ERP

    • Custom validations can be modeled and programmed (e.g. code lists, simple checks, or modeled rules via BRF+)

  • Address Enrichment

    • A simple check and selection lists

    • Integration with content provided by Data Services

    • Automatically adding Tax Jurisdiction Code re-using existing interfaces/providers

  • Data Enrichment Spots

    • Flexible framework to define enrichment spots

    • Is used by SAP for e.g. Address Validation / Enrichment and will be used for further spots in future

    • Can be used by customers to define further enhancement spots (e.g. D&B services)

You can use enrichment spots to provide your own implementation for validation. For example, when creating a material, you can call an enrichment spot to enhance the information the user entered or perform a standard check against an external provider.


In this way, you can re-use the business rules created in Information Steward or Data Services directly within MDG.

One way to address this is to use Information Steward. Define the information policies and relevant rules. Then monitor master data from all of these systems against the rules specified in Information Steward. You can then view a dashboard that shows if any of these systems are no longer meeting the rules. React to these problematic records with the Data Quality remediation outlined above.

Conclusion of the main topic:

Here, we are using Information Steward and Data Services (or any other ETL tool which can support cleansing – as per SAP, DS tool is best to support) in both the topics and individual supporting use cases.

It matters/ varies from case to case, how you build up a solution.

If you are in a large company, spanning many legal entities, and are using SAP ERP as your main transactional system, SAP MDG is a great choice with many possibilities. Furthermore, if your landscape includes many products in the SAP family where data synchronization is required, it is also a great choice. If you are looking for a generic platform for data replication among many different non-SAP systems, you should carefully study how this would work in practice. Additionally, if you are also looking for a data quality reporting platform, the combination of SAP MDG, Information Steward, and Data Services is very powerful and capable to achieve this.


for more insights and advantages of Information steward, I request and recommend you to please visit other blogs here:

Information STeward Insights and Graphical- real-time Scorecard advantages

Information Steward SP12-Updates for Data services


That’s all about this blog post.

Thanks for reading, please provide your feedback. ?

Happy Learning, see you in my next blog ?


Best Regards,

Venkatesh Golla
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