We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
"Once it is set up, it is easy to use and maintain."
"With Oracle Data Quality, the most valuable feature is entity matching."
"I have found the most valuable features to be data cleansing and deduplication."
"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."
"If the length of time required for deployment was reduced then it would be very helpful."
"Oracle is currently not that intuitive. We need to use programmers to write code for a lot of the procedures. We need to have them write CL SQL code and write a CL script."
"Oracle Data Quality should integrate with data warehousing solutions such as Azure and CWS Office. For example, having the ability to integrate with tools, such as Azure Synapse and SQL data warehousing would be a great benefit."
"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."
"The vendor needs to revisit their pricing strategy."
"The price of this solution is comparable to other similar solutions."
Oracle Data Quality is ranked 7th in Data Quality with 3 reviews while SAP Data Quality Management is ranked 10th in Data Quality with 1 review. Oracle Data Quality is rated 8.0, while SAP Data Quality Management is rated 8.0. The top reviewer of Oracle Data Quality writes "Easy to use and maintain, integrates well, and can handle any type of data". On the other hand, the top reviewer of SAP Data Quality Management writes "Has good scalability, it is easy to add new products". Oracle Data Quality is most compared with Informatica Data Quality, whereas SAP Data Quality Management is most compared with SAP Information Steward, SAP Data Services, Precisely Trillium, Experian Data Quality and Informatica Data Quality.
See our list of best Data Quality vendors.
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.