SAP Information Steward Reviews

Our primary use case is for data quality assessment. We are partners with SAP and I'm the senior manager, SAP Data Services. 

Data insight is the most valuable feature. The solution is user-friendly even for those who are dealing with it for the first time. 

SAP Information Steward is a data quality tool. We use the solution to build data quality rules to detect issues in source systems. The solution has many components, the data inside that is being used for data quality and data that is being used for the business glossary and metadata management. 

Information Steward has a component called, 'Metapedia' that describes technical fields in business terms. It is equivalent to a business glossary in other systems. 

When we see a business intelligence report, we want to be sure that the figures we are seeing are consistent with the transformation. We want to be able to see where the number is coming from. For example, we want to know if the data is from an ERP and is being transformed by an ETL and then loaded into a warehouse. Using metadata management in the Information Steward, we can see all of these steps. 

SAP Information Steward also has a cleansing package that helps in the matching process in data services. For example, the name of a person. In many systems, the same person can have their name displayed in many different ways. In one system an individual could be Bob Smith, in another system it could be Bobby Smith and another could show Robert Smith. The cleansing package will try to get all of these synonyms in one place and link it to a standard one. By applying these cleansing packages in data services, we are able to consolidate all the different terms into just one.

For most SAP customers, Information Steward is enough because it is able to build quality data rules to detect issues in the source systems like SAP HANA, Business Warehouse, or other systems.

A business user can first organize their data into several data domains. For example, procurement, human resources, and logistics setup. The domains can build data quality dimensions where you can describe the kind of rule that you are going to use. The user then can immediately see if something is wrong with their data using traffic lights.

Another great feature of SAP Information Steward is the accuracy that the content is followed by validating against the reference tool. With the solution, you are creating data quality dimensions. Within these dimensions, you are creating business data quality rules that are looking for specific fields. From these rules, you can create a scorecard. The scorecard will highlight the percentage of good data and ensure the user can feel confident that the data is accurate within predetermined limits.

SAP tables have field names that are very cryptic, making them hard to understand the meaning of the fields. Metapedia helps describe these fields in business terms.

I do not recall which version we are using. 

We use the solution for data profiling for customers and contacts in respect of contact data.

A problem with the solution is that it does not allow us to review the results of Information Stewards for other analogies. All data resulting from Information Steward remains there, which prevents us from utilizing it to do more deep analysis. We would like to see the export of the resulting data and this is something we have requested from SAP but, as of now, without effect. We wish to be able to extract the resultant information. 

My primary use case is writing and implementing rules for customer information across our enterprise.

Data integration is much easier with Information Steward - irrespective of the data sources, integration is very smooth and easy.

We were developing the profiling reports on the basis of the source data that we were getting from our legacy system, and those profiling reports we shared with the business. Once we got the input from them, once they validated the data, that would be the kind of data that we have to access accordingly.

We were using SAP BODS and gathering extraction criteria. Similarly, there were a couple of rules that we were validating in the SAP Information Steward as well.

The ability to analyze the data even before we start the transformation of it, and generating the user-friendly interface, giving analytical reports, and helping create the transformation rules before we proceed with the data migration part was the most helpful part of the solution for our company. The user was moving from a legacy system to S/4HANA version. It was really helpful in collating the reports and getting the data inside.

Our primary use case of SAP is for data governance, access management, and historical reporting. We're a global enterprise company that uses multiple solutions. We have about five or six of them in place right now, some of which are direct competitors to SAP. 

We are customers of SAP and I'm the information protection senior adviser.

The product has improved company efficiency because we're able to categorize the need for user access at the folder level across storage enterprise wide.

My main use case is to further our data quality monitoring. We are also using this solution to write business rules for the data in clients' systems and find the quality of the score they have in their system.

The most valuable features are data quality insight, metadata management, and metadata dictionary.

It is very easy to deploy. 

The data that they give us is good. We use it in our data quality domain.

My primary use of SAP Information Steward is for S/4HANA business progress.

The most valuable feature is that I can centralize data in cloud form with multiple connections and then consume that data from HANA database data connections.