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8 reasons data science projects fail


Data science rarely fails to draw interest from IT and business leaders alike these days. But it does fail.

In fact, data science initiatives, which leverage scientific methods, processes, algorithms, and technology systems to extract a range of insights from structured and unstructured data, can fail in any number of ways, leading to wasted time, money, and other resources. Flawed projects can result in more damage for an enterprise than benefits, by leading decision-makers astray.

Here are some of the most common reasons why data science projects do not pan out as expected.

Poor data quality

Bad data makes for bad data science, so it’s of vital importance to take the time to ensure data is of high quality. That’s true for any analytics undertaking and it’s certainly the case with data science.

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