Amazon Technologies, Inc.

Sean Michael Gregory Corrigall

Gregory Louis Chappell

Alexandros Zotos

Petar Butkovic

Vojtech Hordejcuk

Ben Jannis Freiberg

Giannis Skevakis

The techniques described herein are directed to identifying data quality issues within information stored across multiple different data sources. For instance, the data quality issues can comprise missing values, inconsistent values, and un-translated values. Once identified, the techniques implement actions to resolve the data quality issues so that consumption or use of the information stored is improved. In at least one example, the identification and resolution of a data quality issue can be implemented in response to receiving a query that identifies an object. Based on the query, the system can collect values, from the multiple different sources, for attributes that have been defined for an item. The system can use algorithms (e.g., a comparison algorithm) to identify a data quality issue and can output a graphical user interface that visually distinguishes between attributes with a data quality issue and attributes without a data quality issue.

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Luxembourg, Luxembourg

Sean Michael Gregory Corrigall