As data science professionals, we are often viewed as people who draw conclusions based only on data and minimize other factors. This perception usually becomes contentious when the insights and evidence from the data are inconsistent with somebody else’s “hypothesis.” Or we are confused and maybe frustrated when “qualitative” analysis trumps quantitative analysis. The next time you feel this frustration, consider these four perspectives on data analytics to validate and indonesia whatsapp number data consider other views so you can try and find common ground:
Outliers equal opportunity.”
Outliers present themselves in a dataset as anomalies. Maybe outliers are noise, but maybe they are special.
Outliers could be unique insights, emerging trends, or interesting segments. In medical research, an outlier could point to a rare but life-threatening side effect of a medication. In the case of customer data, an outlier can be a valuable customer niche that has not yet been addressed. Outliers could be an emerging trend. The color pink started off as an outlier but quickly became the most popular fashion choice.
Before dismissing outliers as noise, use them to spark questions and curiosity:
Does the outlier point to an opportunity?
Why does the outlier exist?