Using Big Data to Look Past Generic Job Applications
It is estimated that HR teams spend up to 85 percent of their time on such administrative activities, but only 15 percent on strategic activities that add meaningful value to their organizations. Big data analytics could automate many of these chores, allow holistic and objective evaluation of candidates’ profiles, shorten the recruitment cycle and free up HR personnel for strategic initiatives.
Though companies have extensively been using data for operational decision making, recruitment is one area that has made only limited strides, despite its strategic and important position in any organization. This was largely due to privacy laws, regulations, and governance structures that prevented companies from using applicants’ online data in the recruitment process. Hence, companies were officially obliged to restrict their evaluation solely based on the static, and often unrepresentative data furnished by applicants in the form of cover letters, curriculum vitae, and supporting transcripts. Many companies do unofficially look up shortlisted candidates to form an opinion, which, based on the HR personnel’s biases and predispositions, may work for or against the candidate. This method is time consuming and is neither efficient nor effective at achieving the ultimate recruitment objective of finding the right candidates.
For example, in today’s economic climate, any job opening attracts hundreds or thousands of applications from job candidates. The human resources department in most companies is vastly understaffed, as companies continue to implement headcount reduction as a prime cost-saving tactic. The sheer volume of applications overwhelms the HR personnel, making it impossible to meticulously sift through all applications, conduct a thorough study of every candidate’s application and finally, select the right candidate. In some dire cases, HR personnel have even resorted to random sampling to shortlist candidates from the applicants’ pool.
For applicants, this situation poses another challenge. As most applicants already know, HR personnel typically spend two to three minutes per application and to capture HR personnel’s attention, candidates typically resort to keeping their application short and convey only the most essential information, such as education and work experience. In doing so, candidates’ applications become hardly distinguishable from one another, as almost all candidates include the aforementioned details in different formats and styles.
Though these trends paint a bleak picture, the good news is that the recruitment landscape is changing for the better. Both companies and candidates are becoming increasingly aware of the benefits of big data in the hiring process and are progressively embracing technologies and tools to that end.
Today, almost all applicants have an online digital footprint that more or less completely captures an applicant’s candidature. Almost all applicants belong to one or more social networks or professional groups, have a blog, a Twitter account, participate in communities, etc. This information reflects a candidate’s behavior over a period of time and closely captures a candidate’s interests, potential, standing in a social network, affiliations, behavioral information, interaction with other individuals, leadership traits, teamwork, and so on. These online fingerprints are distinct from one another, so could constitute a reasonable differentiator among candidates in the applicants pool, which the HR personnel could leverage in identifying the right candidate.
For example, consider a hypothetical job opening for a social media marketer. Almost all candidates applying for this position would demonstrate educational qualifications, some degree of relevant work experience in online marketing, belong to a social network, own a blog/microblog, and so on. Almost all aspiring candidates will include similar information, hence, their applications become barely distinguishable. However, an applicant with a Klout score of over 60 is better networked and has a higher “influencer potential” than a candidate with a lower score. Klout scores are usually acquired over a period of interaction with other individuals. Other aspects being reasonably similar, the company would be justified in recruiting the candidate with a higher Klout score.
Similarly, a candidate seeking a development or data scientist position may have worked on open source projects, maintain a Github account, participated in Kaggle contests and have earned a significant standing in the community. Such accolades are earned over a course of time through peer ratings and are more representative of a candidate’s credentials.
With big data analytics, parameters like these could be automatically collected and used by recruiters to build candidate profiles and identify the right talents. Through a structured approach and data collection, a more complete profile of the candidate could be build and the fit evaluated in real time.
Such dashboards offer considerable time savings, establish statistical significance among metrics, and include evaluation parameters that recruiters may potentially have overlooked. By attributing relative weights to the various evaluation parameters, an objective decision in choosing one candidate over another could be made. These benefits present a strong argument for recruiters to adopt and leverage big data in their recruitment process.
Though these metrics could provide a convenient benchmark, overuse could only lead to abuse. A conscientious approach to recruitment practices, adherence to the laws surrounding data privacy and to selection criteria/metrics when using big data analytics could definitely bestow value on the applicants, HR personnel, and respective organizations.
Is your HR department ready for the big data era? If you are a job seeker, what are your qualms about this brave new world? Please share your thoughts.