(Photo: Shutterstock.com) Arbitrary and Excessive: Three Pitfalls in Conversational Data Discovery
The e-discovery and investigations paradigm is shifting as emerging data sources account for an increasing volume of discoverable information. Traditional workflows need to adapt accordingly.
July 05, 2022 at 07:00 AM
6 minute read
Imagine standing in the middle of a crowded restaurant, surrounded by the din of hundreds of simultaneous, unrelated conversations. Then, imagine you were expected to figure out which of these conversations were about lawnmowers—more specifically, which brand of lawnmower was best, what features it had, how much it cost, the active participants in the conversation, the people listening in and the name of the person who recommended a specific lawnmower. Before, during and after the pertinent points about lawnmowers, that discussion (still happening in a crowded space surrounded by unrelated noise) was intermixed with comments about landscaping tips, the cost of real estate in the area, next weekend's plans, the 10-day weather forecast, the best items on the menu, etc., etc. Pinpointing the details about lawnmowers would be an impossible task.
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