Big data! If you don’t have it, you better get yourself some. Your competition has it, after all. Bottom line: If your data is little, your rivals are going to kick sand in your face and steal your girlfriend. There are many problems with the assumptions behind the “big data” narrative (above, in a reductive…“In some cases, big data is as likely to confuse as it is to enlighten.When companies start using big data, they are wading into the deep end of a number of tough disciplines—statistics, data quality, and everything else that comprises “data science.” Just as in the kind of science that is published every day—and as often, ignored, revised, or never verified—the pitfalls are many. Biases in how data are collected, a lack of context, gaps in what’s gathered, artifacts of how data are processed and the overall cognitive biases that lead even the best researchers to see patterns where there are none mean that “we may be getting drawn into particular kinds of algorithmic illusions,” said MIT Media Lab visiting scholar Kate Crawford. Terrific summary of “big data” myths by @mims is consistent with our view at Joios. Our rigorous data-gathering process (reliant on blind tastings and unbiased individual submissions) produces smaller but better data: highly accurate, intelligible, bounded. We like the trade-off.
Page 1 of 12
← Newer • Older →