Popular Culture Review 30.1
gathered , or what the algorithm suggests about the potential applicant . 16
One of the key limiting factors to sorting information had been computational power . Data scientists would need a large amount of data and were limited to very specific questions . Now , however , computational capabilities have expanded to the point where “ finding a needle in a haystack ” is not only possible , but practical . 17 Now that computational power is no longer an issue , the value of large data pools is becoming more prevalent . A researcher at the Broad Institute discovered that a genetic variant linked to schizophrenia was invisible when analyzing 3,500 cases , weakly identifiable when using 10,000 cases , but statistically significant with 35,000 cases . 18
This information explosion is quickly reinventing a large number of industries , especially in employment . Kelly Trindel , Chief Analyst for the Office of Research , Information and Planning at the EEOC , cautioned big data in regard to employment has different meanings to different people , noting that big data are more than simply very large data sets with a significant amount of rows and columns . 19 The size of the datasets is not what defines big data , but the nature , source , how it is collected , merged , transformed , and utilized . In her testimony , Ms . Trindel suggested that “[ i ] n the employment context , I would define big data as follows : big data is the combination of nontraditional and traditional employment data with technology-enabled analytics to create processes for identifying , recruiting , segmenting and scoring job candidates and employees .” 20
Employers have long been used to using traditional employee data to make hiring decisions . Applications and resumes that lead to interviews can only tell an employer so much about a
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