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Data Pools have Grown to Data Lakes: Taking Advantage of Big Data Capability in Recruiting

by SwoopTalent, on February 12, 2018

Constructing a data lake of available talent goes well beyond a talent pool for reaching the right candidates. New terminology enters the business community all the time. In 2010, systems analyst, James Dixon coined the term "data lake" to call attention to the potentials now available. The idea of a data lake, as a graduation from data pools, does represent a sea change in the available potentials for data acquisition and storage across the cloud. Human resources recruiting ought to be taking advantage of the change.

Becoming an active recruiter.

The focus among progressive HR recruiters these days is moving toward the passive candidate, people currently not actively searching for new employment. On any one-day, active job-seekers represent 25 percent of the workforce. "Tiptoers," those who are keeping an eye out for new opportunities but have no plans to move represent, perhaps, 15 percent of the workforce. True passive candidates, talented people who are reasonably content with their current employment but who could make a real contribution to an organization, are the majority, perhaps 60 percent of the workforce on any one day.  

Finding the silent passive candidates is the true art of the recruiter. The power recruiter slowly and carefully gathers the facts about these prize candidates before engaging them quietly in ways that don't endanger them.  That's what the data lake concept is all about. Collecting and retaining background about potential candidates that comes from a full 360-degree view of their history, interests, traits, skill set and the like. 

The information could come from

  • Previously sourced candidates who went on to accept other positions.
  • Leads gathered from career fairs, business conferences, conventions.
  • Candidates who were unsuccessful in previous recruiting, but whom you would like to re-engage.
  • Job boards, resumes, social media, people aggregators.
  • Random meetings.
  • People with reputations whom you would like to meet.

This could be a large number people in a range of professions and occupational categories. You will need large scale storage and powerful sorting and data search capability to build a data lake of candidates. But if you did that you could offer clients an exciting range of potential. If you had the capacity that current big data control software offers, you could keep your data lakes of people fresh, by continuously updating information as you get it. Then, of course, when you finally make real contact with a prize candidate you could add whole new perspectives on his or her file.

Levels of improvement.

A survey conducted in 2015 found that 37 percent of small businesses are now using some form of recruiting analytics software. Nearly all the firms (96 percent to 86 percent) that use recruiting analytic tools report good to very good results with them in terms of candidate training, employee retention, cost per hire, and similar criteria.

When your pool of potential candidates is sorted according to relevant traits, you can use analytics to improve your hiring success.

  • The Wall Street Journal reported that according to Xerox "creative types" as measured by a personality test, lasted longer through initial training, By selecting these people, Xerox cut attrition by 15 percent.
  • BAI reported that Wells Fargo had better employee retention with employees whose degrees were in financial services or hospitality rather than accounting. The latter did not tend to stick around. Using big data analytics improved teller retention by 15 percent.
  • The Harvard Business Review noted that AT&T and Google found "ability to take initiative" is a far better predictor of high performance than good grades in prestigious universities.

Accumulating a data lake.

Every corporate job opening could attract 250 or more applicants. Each of these applicants passes information about their qualifications relevant to their ability to succeed at a job. You could file all these resumes and enable search engines to sort them according to criteria important to you. Collect notes and business cards from conferences you attend or casual meetings in passing. The files can include a broad array of information for later analysis.

SwoopTalent uses AI-powered algorithms to automatically connect and verify talent data from internal and external sources leaving you with a single access point to retrieve accurate, connected, constantly updated talent data. Please contact us to learn more.

Topics:datatalent data as a servicetalent data

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