Not knowing much about the data before starting the visual discovery process can lead to a more open-ended work. This is because the process becomes more exploratory, as you are not guided by pre-existing knowledge or hypotheses. This can be both an advantage and a disadvantage. On one hand, it allows for the possibility of uncovering unexpected patterns, trends, and anomalies. On the other hand, it can make the process more challenging and time-consuming, as you have to sift through larger and more complex data without a clear direction.

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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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