Before companies can profit from big data, they often must deal with bad data. There may indeed be gold in the mountains of information that firms collect today, but there also are stores of contaminated or “noisy” data. In large organizations, especially financial institutions, data often suffer from mislabeling, omissions, and other inaccuracies. In firms
I often learn as much by talking to people in between sessions at a conference as I do when listening to the presentations, and this week’s Predictive Analytics World event has been no exception. I had the...
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