Getting too fancy by using complex and layered data science approaches can magnify the issues in data instead of controlling them. This blog will explain why and illustrate with a real-world example that I also discussed in The Analytics Revolution to show that the old rule of keeping it simple fully applies to complex areas like data science. A Surprising, But Recurring, PatternOne pattern surprised me when I was first confronted with it. Namely, when building analytical processes that must be operationalized to an enterprise scale, simpler solutions can actually perform better than fancy solutions . . . not just from a systems and processing perspective, but also from an analytical perspective! This can be true even when, theoretically, a more sophisticated method should work better. I’m convinced that this is because data always has some uncertainty, is often sparsely populated, and is never fully complete. This can be especially true with some of the low-level data utilized for operationally oriented analytics today.At some point as analytics applied to a dataset get more sophisticated and layered, there is a risk of magnifying the errors and uncertainties in the data rather than controlling and accounting for them. In addition, it is ...
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