7 Common Biases That Skew Big Data Results

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Summary: Flawed data analysis leads to faulty conclusions and bad business outcomes. Beware of these seven types of bias that commonly challenge organizations' ability to make smart decisions.

This is a great article by Lisa Morgan originally published on InformationWeek.com. See the original article here.

Here’s a quick synopsis.

Data-driven decision-making is considered a smart move, but it can be costly or dangerous when something that appears to be true is not actually true. Even with the best of intentions, some of the world's most famous companies are challenged by skewed results because the data is biased, or the humans collecting and analyzing data are biased, or both.

Here we present seven types of cognitive and data bias that commonly challenge organizations' decision-making. Once you've reviewed these, tell us in the comments section below whether you've experienced any in your organization, and how that worked out for you.

1. Confirmation Bias
2. Selection Bias
3. Outliers
4. Simpson’s Paradox
5. Over Fitting and Under Fitting
6. Confounding Variables
7. Non-normality: The Bell Does Not Toll

About the Author: Lisa Morgan is a freelance writer who covers big data and BI for InformationWeek. She has contributed articles, reports, and other types of content to various publications and sites ranging from SD Times to the Economist Intelligent Unit. Frequent areas of coverage include big data, mobility, enterprise software, the cloud, software development, and emerging cultural issues affecting the C-suite.

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