We performed a comparison between SAP Data Quality Management and SAP Data Services based on real PeerSpot user reviews.
Find out in this report how the two Data Quality solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."Our primary use case is for us to inspect the results from the product and material, and for releasing or leaving the status of the product."
"We work with API standards or norms for internal applications, so it's essential for SSE to have tests and pass those tests according to the criteria, which makes SAP Data Quality Management very important for our products."
"The product's most valuable features are data validation and rules."
"Its integration capabilities and the data migration capabilities are the most valuable. It is very good for SAP and non-SAP tools. It has very good integration with SAP, but it also has the capabilities to connect to other systems. We find it very helpful and stable."
"The most valuable features of SAP Data Services lie in its ability to effectively observe and interpret the information within data related to people or facts, stands out."
"Data Services' table comparison mechanism is very powerful. It's pretty hard to find a similar feature in other solutions."
"The maintenance of data services is the solution's most valuable feature."
"The HANA database, which is very fast, is a valuable feature."
"The logic is also simple. It makes it easy to build your extraction."
"The most valuable feature is the logging capability."
"SAP Data Quality Management would be better if it directly integrates with the ME system. Right now, the company has a lot of machines on the shop floor working as a standalone, so you have to use all methods to ensure that the data interface appears on the ME system and that SAP Data Quality Management records the QM results. It would be much easier if the ME system could be integrated directly with SAP Data Quality Management."
"I would like for them to develop a feature to able to record all of our inspections; so all the data can go through SAP. It's not user-friendly or easy to get further analysis, so we mostly skip this step."
"Newer feature integration is lagging behind the company acquisitions and the product could do more to service a broader range of devices."
"With SAP Data Services, the problem arises when I try to connect to Azure Databricks."
"It would be nice if this solution was a bit easier to move from development to production."
"Data Services SAP is lacking in sources and target databases compared to Informatica. SAP Data Services should have more connectivity."
"There should be some kind of enhancement that can be done on the admin side of certain sites where we can assign the roles and responsibilities. We should be able to control who is using the tool and how."
"The initial setup is complex."
"The solution could improve the overall features. There is a lot that can be done in the solution, therefor there are areas where it can improve. Additionally, there is a need to make it easier for one to create connections to other non-SAP systems. The flexibility to connect to other non-SAP systems is needed."
"The solution should offer more machine learning and automation."
SAP Data Quality Management is ranked 5th in Data Quality with 2 reviews while SAP Data Services is ranked 2nd in Data Quality with 45 reviews. SAP Data Quality Management is rated 8.6, while SAP Data Services is rated 8.0. The top reviewer of SAP Data Quality Management writes "Scalable, stable, and offers good technical support". On the other hand, the top reviewer of SAP Data Services writes "Responsive support, scalable, and beneficial integration". SAP Data Quality Management is most compared with SAP Information Steward, Melissa Data Quality, Informatica Data Quality and Informatica Address Verification, whereas SAP Data Services is most compared with Azure Data Factory, Syniti Data Quality, Informatica PowerCenter, SAP Process Orchestration and SAP Data Hub. See our SAP Data Quality Management vs. SAP Data Services report.
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We monitor all Data Quality reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.