Seven Ways That Data Science Projects Fail
The pragmatic value of data science for solving business problems has made it a rival or replacement for information science from an industry perspective. I reviewed numerous data science projects and interviewed numerous data science experts to understand the factors that make projects successful, but this work also revealed—by counterfactual reasoning and some confessions from the experts—why some data science projects fail. I identified seven causes of failure and I explain here how “information science thinking” can prevent or lessen these problems in data science projects.
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