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Visual discovery in data analysis has several benefits. It allows for testing of hypotheses and mining for patterns, trends, and anomalies. It is particularly useful when dealing with large and complex data sets, as it can help to identify patterns and trends that may not be immediately apparent. Furthermore, it provides a more flexible approach to data analysis, allowing for more open-ended work.
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Visual discovery is the most complicated quadrant because it consists of two categories: testing a hypothesis and mining for patterns, trends and anomalies. Berinato says: "The former is focused, whereas the latter is more flexible. The bigger and more complex the data, and the less you know going in, the more open-ended the work."
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