Career Advice

Data Mining For Your Next Employer

Job Upon August 12, 2019

Employers today have had decades to live with civil rights and fairness in hiring, but with the computer age many have been able to sidestep such issues by essentially blaming the computer for the results of their hiring. Instead of trusting subjective screeners to determine the best candidates, today's companies are plugging in desirable terms and traits and letting a scanner and processor crunch thousands of resumes to find viable candidates. This commonplace employment data mining has literally redefined hiring practices in the last 20 years.

Fortunately, employers are not the only one who can used search engines for jobs. Applicants can use them too. The trick is to find a database source and then use the right tools and parameters to run effective queries. Instead of firing off countless applications blindly to everything in the category of a job type, applicants can focus their limited time and resources on those employers who really match best the kind of place one would want to work for.

Search engine analytics are the first place to start. Employers utilize search engines for prospects just as much as anyone else, and that means their entry terms are captured as well. Using a good logic approach with analytics is going to highlight for a user which employers are searching for which type of candidate skills the most. The same way a marketer would use analytics to determine what consumers are searching for, an applicant can use search engines to determine what employers are recruiting for. It's a simple matter of learning how to apply the tool online.

In terms of a second source, ever wish someone would create maps for where certain types of employers are clustered regionally? It's probably already done. is well-known for its GIS ARC mapping tools. What they also provide is GIS-based map files of all sorts of data compiled by different GIS users. These are searchable and can be utilized by an applicant to pinpoint where employer clusters are located geographically and by type. $100 for an annual student account is the total cost and then learning the software to download ready-made files. It's a highly powerful, underutilized tool available to anyone online.