7 Common Biases That Skew Big Data Results - InformationWeek

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Data Management // Big Data Analytics
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7/9/2015
08:06 AM
Lisa Morgan
Lisa Morgan
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7 Common Biases That Skew Big Data Results

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.
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Selection Bias 
Selection bias commonly occurs when data is selected subjectively rather than objectively or when non-random data has been selected. Because the population selected does not represent the actual population, the results are skewed. 
Surveys are a good example of selection bias, because specific questions are selected for the purpose of revealing particular insights. In addition, the surveys are sent to a select group of people, some of whom opt in. Although survey respondents are often regarded as representative of a total population, the layers of selection bias make that unlikely. 
'Selection bias is one of the major flaws associated with the increased availability of big data,' said Kevin Sheetz, CEO of market intelligence platform provider Powerlytics, in an interview. 'Many businesses only capture a small piece of the pie when it comes to data available to their segment or industry, and this means their data and subsequent analysis are skewed. Much of the data companies use to make critical business decisions is incomplete, inaccurate, and of poor quality, and, as you can expect, this leads to inaccurate analysis and benchmarking.'
(Image: Geralt via Pixabay)

Selection Bias

Selection bias commonly occurs when data is selected subjectively rather than objectively or when non-random data has been selected. Because the population selected does not represent the actual population, the results are skewed.

Surveys are a good example of selection bias, because specific questions are selected for the purpose of revealing particular insights. In addition, the surveys are sent to a select group of people, some of whom opt in. Although survey respondents are often regarded as representative of a total population, the layers of selection bias make that unlikely.

"Selection bias is one of the major flaws associated with the increased availability of big data," said Kevin Sheetz, CEO of market intelligence platform provider Powerlytics, in an interview. "Many businesses only capture a small piece of the pie when it comes to data available to their segment or industry, and this means their data and subsequent analysis are skewed. Much of the data companies use to make critical business decisions is incomplete, inaccurate, and of poor quality, and, as you can expect, this leads to inaccurate analysis and benchmarking."

(Image: Geralt via Pixabay)

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