Oracle Enterprise Data Quality vs. SAP Data Quality Management, microservices for location data

• Ease of adding localized and customized data quality formulations (for instance asking for Turkish Citizenship Number from governmental web service and checking whether data is the same with the output).
• Finding the "golden record" from the duplicated records.
• Adding and/or merging missing information from different data sources.
• Using patterns and statistical functions to complete the data.
• Address verification (can be localized).
• Parsing of combination of records.
• Enriching required data from free text (story told) inputs.

Incentivized

• Address verification helps you filter bad data out and improve your data quality.
• Strength by DnB enriched information to address the correct customer by address.
• expert data profiling to help us quickly understand our current data health.

Incentivized

• Updates are few and far between as this has transitioned to the Cloud and is the core engine for DIPC (Data Integration Platform Cloud).
• Needs governance around the tool if multiple people are running reports (e.g. if someone is running a process, it locks out other users).

Incentivized

Oracle Enterprise Data Quality is affordable and I find it more resourceful. Using it is easy too thanks to the improved UI.

Incentivized

• Details verification, for instance, address ensure the quality of every operation is sustained.
• Further, sales analytics improves the market share as customers get to apprehend the business needs.
• Oracle Data Quality has consistent data connectivity, a standard way of improvising business requirements.

Incentivized