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Oracle has introduced explainable machine learning, among several new features in the Oracle Analytics Cloud. Users can now access simple explanations of factors that influenced a model to predict a certain outcome and they can adjust factors to fine-tune results.

With automated data preparation, a  data profiling engine samples and scans data to identify and proactively prompt users about potential data quality issues, such as suggesting the obfuscation of credit card information or Social Security Numbers. Text analytics enables users to extract words from unstructured data, count them, visualize the results, and then join that analysis with original data while Affinity Analysis can discover relationships in data by identifying sets of items that often appear together. Graph analytics show data relationships visually, such as how people and transactions are connected or the shortest distance between two hubs in a network and custom map analytics gives users the ability to apply custom images as map backgrounds and create map layers to enhance data visualizations. In addition, a new Oracle Analytics Mobile App can be used to find data via a consistent user experience across Oracle Analytics Cloud and the app. There is also natural language processing for querying data via text or voice and obtaining spoken narratives in any of 28 languages. An embedded natural-language generation engine  understands the context of the data a user is looking at and automatically updates the narrative as the user adds data, changes filters, or otherwise changes the context as in a typical data discovery and analysis process.

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