Ease Of Management Within ODI.
Updated April 10, 2019

Ease Of Management Within ODI.

Gurcan Orhan | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User

Overall Satisfaction with Oracle Enterprise Data Quality

Oracle Enterprise Data Quality (OEDQ) helped my organization to define Data Quality issues from a business perspective by business users and ability to manage those issues within our ETL tool (Oracle Data Integrator - ODI).

Pros

  • Ease of management within Oracle Data Integrator (ODI) and effectivity of EDQ Director
  • Ease of installation with web based client.
  • Data Quality issues solved quickly, easily and jobs are controlled within Oracle Data Integrator (ODI) seamlessly.
  • Setup is straightforward. Java web application (OEDQ Director) helped us for not installing any client application.

Cons

  • Mobile support and mobile app can be implemented, since business users generally prefers to work with not only their laptops, but also mobile phones or tablets.
  • Data profiling could be standalone option instead of OEDQ Director. A business user generally wants to see the format or templated counts or other aggregations based on a full dataset.
  • Data Quality issues solved quickly, easily and jobs are controlled within Oracle Data Integrator (ODI) seamlessly.
  • Business users created their data quality packages and those packages are called from Oracle Data Integrator easily and merged into data warehouse.
  • OEDQ jobs can be created by not only IT teams, but also business teams. That is why we called a "DQ job is not a part of our development, it is a part of our DWH solution".
Trillium and SAS DataFlux was considered but not having in-ETL management and licensing cost.
SAS DataFlux, Trillium Data Quality
  • 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.

Oracle Data Quality Feature Ratings

Data Quality
9.2
Data source connectivity
10
Data profiling
8
Master data management (MDM) integration
8
Data element standardization
9
Match and merge
10
Address verification
10

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