In spite of advances in technologies for working with data, analysts still spend an inordinate amount of time diagnosing data quality issues and manipulating data into a usable form. This process of \u2018data wrangling\u2019 often constitutes the most tedious and time-consuming aspect of analysis. Though data cleaning and integration are longstanding issues in the database community, relatively little research has explored how interactive visualization can advance the state of the art. In this article, we review the challenges and opportunities associated with addressing data quality issues. We argue that analysts might more effectively wrangle data through new interactive systems that integrate data verification, transformation, and visualization. We identify a number of outstanding research questions, including how appropriate visual encodings can facilitate apprehension of missing data, discrepant values, and uncertainty; how interactive visualizations might facilitate data transform specification; and how recorded provenance and social interaction might enable wider reuse, verification, and modification of data transformations.<\/p>\n","protected":false},"excerpt":{"rendered":"
In spite of advances in technologies for working with data, analysts still spend an inordinate amount of time diagnosing data quality issues and manipulating data into a usable form. This process of \u2018data wrangling\u2019 often constitutes the most tedious and time-consuming aspect of analysis. Though data cleaning and integration are longstanding issues in the database 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