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Infographic: Common Corporate Mistakes in Data Science Projects
A visual, professionally distilled summary of six recurring mistakes corporations make in Data Science projects, with a full accessible-text transcript.
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Full text of the infographic
Common Corporate Mistakes in Data Science Projects
- Ignoring existing expertise. "We don't need domain experts, our algorithms will figure it out." Data Science does not replace domain knowledge; it depends on it.
- Neglecting data quality. "Let's just throw more data at it." More data does not fix bad data. Understanding and cleaning data is most of the work.
- Skipping problem framing. "Just build a model." Without a clear business question and success metric, even an accurate model can be useless.
- Underestimating deployment. "The model works in the notebook, we're done." Getting a model into production reliably is often harder than building it.
- Expecting one-shot success. "If it doesn't work immediately, the approach is wrong." Data Science is iterative; early failures are part of the process, not proof of failure.
- Forgetting the human factor. "The numbers speak for themselves." Recommendations must be communicated and adopted by people, or they never create value.
In summary: Corporate Data Science initiatives fail less often because of algorithms and more often because of avoidable mistakes in framing, data, deployment, expectations, and communication.
Paulo Salem, Ph.D., Data Scientist. Version 1, June/2019. Licensed under CC BY-SA 4.0 International. Powered by Piktochart.