Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating data visualization generation. In one implementation, dataset intent data, visual design intent data, and insight intent data determined from a user input natural language query are obtained. A set of candidate intent recommendations is generated using various combinations of the dataset intent data, visual design intent data, and insight intent data. Each of the candidate intent recommendations is incorporated into a set of visualization templates to determine eligibility of the candidate intent recommendations. For eligible candidate intent recommendations, a score associated with a corresponding visualization template is determined. Based on the scores, a candidate intent recommendation and corresponding visualizations template is selected to use as a visual recommendation for presenting a data visualization.

Abstract: A method of determining efficacy of a dataset includes receiving data from a data source, wherein the data comprises a plurality of fields of unknown efficacy; mapping the data based on a plurality of data quality metrics and based on attributes of the plurality of fields wherein meta-features for the data are obtained; predicting a value for each of the plurality of data quality metrics using a ML model that takes the meta-features as input, wherein the value indicates whether a corresponding data quality metric is suitable for measuring efficacy of the fields; selecting a data quality metric based on the value, wherein the data quality metric measures an efficacy of the fields; and monitoring the efficacy of the fields in the data received from the data source based on the data quality metric.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating data visualization generation. In one implementation, dataset intent data, visual design intent data, and insight intent data determined from a user input natural language query are obtained. A set of candidate intent recommendations is generated using various combinations of the dataset intent data, visual design intent data, and insight intent data. Each of the candidate intent recommendations is incorporated into a set of visualization templates to determine eligibility of the candidate intent recommendations. For eligible candidate intent recommendations, a score associated with a corresponding visualization template is determined. Based on the scores, a candidate intent recommendation and corresponding visualizations template is selected to use as a visual recommendation for presenting a data visualization.