Identification	Correctly identify inaccurate, incomplete, or duplicated data from a data source. 16 reviewers of Oracle Data Quality have provided feedback on this feature.	92% (Based on 16 reviews)
Correction	Based on 16 Oracle Data Quality reviews. Utilize deletion, modification, appending, merging, or other methods to correct bad data.	86% (Based on 16 reviews)
Normalization	Based on 16 Oracle Data Quality reviews. Standardize data formatting for uniformity and easier data usage.	88% (Based on 16 reviews)
Preventative Cleaning	As reported in 16 Oracle Data Quality reviews. Clean data as it enters the data source to prevent mixing bad data with cleaned data.	86% (Based on 16 reviews)
Data Matching	Based on 14 Oracle Data Quality reviews. Finds duplicates using the fuzzy logic technology or an advance search feature.	85% (Based on 14 reviews)

Management

Reporting	As reported in 16 Oracle Data Quality reviews. Provide follow-up information after data cleanings through a visual dashboard or reports.	90% (Based on 16 reviews)
Automation	As reported in 16 Oracle Data Quality reviews. Automatically run data identification, correction, and normalization on data sources.	82% (Based on 16 reviews)
Quality Audits	Based on 14 Oracle Data Quality reviews. Schedule automated audits to identify data anomalies over time based on set business rules.	87% (Based on 14 reviews)
Dashboard	Based on 14 Oracle Data Quality reviews. Gives a view of the entire data quality management ecosystem.	88% (Based on 14 reviews)
Governance	Allows user role-based access and actions to authorization for specific tasks. This feature was mentioned in 14 Oracle Data Quality reviews.	88% (Based on 14 reviews)

Generative AI

AI Text Generation		Allows users to generate text based on a text prompt. This feature was mentioned in 14 Oracle Data Quality reviews.	86% (Based on 14 reviews)
AI Text Summarization		As reported in 14 Oracle Data Quality reviews. Condenses long documents or text into a brief summary.	87% (Based on 14 reviews)