Friday, June 6, 2014

A Primer on Big Data and Hiring: Chapter 1

This is the first chapter of a primer on big data and hiring. The structure of the primer is based on the following graphic created by Evolv, a company that provides "workforce optimization" services. Evolv was selected not because it is sui generis; rather, it is emblematic of numerous companies, from start-ups to well-established companies that market "workforce science" services to employers.

The Evolv graphic below is intended to illustrate the process of workforce science.

Chapter 1: New and Existing Data:
Companies capture and store workforce data.

Most companies have vast amounts of HR data (employee demographics, performance ratings, talent mobility data, training completed, age, academic history, etc.) but they are in no position to use it. According to Bersin by Deloitte, an HR research and consultancy organization, only 20% of the companies believe that the data they capture now (let alone historically) is highly credible and reliable for decision-making in their own organization.

The complexity of working with myriad data types and myriad, often incompatible, systems was underscored by Dat Tran of the U.S. Department of Veterans Affairs at the 2013 MIT Chief Data Officer and Information Quality Symposium. "The VA does not have an integrated data environment; we have myriad systems and databases, and enterprise data standards do not exist. There is no 360-degree view of the customer," Tran said in a discussion of the obstacles facing an agency dealing with 11 petabytes of data and 6.3 million patients.  

"Bad data" is data that has not been collected accurately or consistently or the data has been defined differently from person to person, group to group and company to company. In the recruitment and hiring context, unproctored online tests allow an applicant to take the test anywhere and anytime. 

That freedom creates conditions ripe for obtaining "bad' data. As stated by Jim Beaty, PhD, and Chief Science Officer at testing company Previsor:
Applicants who want to cheat on the test can employ a number of strategies to beat the test, including logging in for the test multiple times to practice or get the answers, colluding with another persons while completing the test, or hiring a test proxy to take the test.
And what about the accuracy of tests responses from those who are hired? Analyzing a sample of over 31,000 employees, Evolv found that employees who said they were most likely to follow the rules left the job on average 10% earlier, were 3% less likely to close a sale and were actually not particularly good at following rules.

Decision-makers increasingly face computer-generated information and analyses that could be collected and analyzed in no other way. Precisely for that reason, going behind that output is out of the question, even if one has good cause to be suspicious. In short, the computer analysis becomes a credible reference point even though based on poor data.

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