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The level of knowledge about the data significantly affects the process of visual discovery. If you have a high level of knowledge about the data, you can focus on testing a specific hypothesis. However, if you know less about the data, the process becomes more open-ended, allowing for mining patterns, trends, and anomalies. The complexity and size of the data also play a role in this process.
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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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