"We try to bring as much data and science to what we do on the people side as our engineers do on the product side." - Lazlo Bock, Google
The same data and science has led Google to conclude that neither mind teaser questions nor interviews were any effective performance predictors. They also pretty much ruled out the role of ‘gut feel’ in identifying talent.
What, then, is effective interviewing all about? How can we use the science of data effectively to remove ineffective predictabilities in the interview process?
Data drives smarter interview practices
A data-driven approach certainly makes for better-informed interviews. If data can help acquire business and customers, if it can bring efficiency in work practices, it can certainly add to the effectiveness of the interview process. This is what enables great employer brands architect their dream workforce.
The interview process is a toss-up between selection and elimination. A data-driven approach minimises the conflict between the two by synergising the positive objectives and components in them. Who are the right people to interview for a particular job opening? How many rounds of interviews will be the most effective? What are the assessment criteria that are most likely to predict the right mix of culture fit and on-the-job effectiveness? What is the best mode of interview for a specific requirement - in-person, video, one-to-one, or group-to-one?
Simply put, the data-driven approach takes away the ‘one-size-fits-all’ fatal flaw in most interview practices and focuses on the right segmentation and customisation for the best possible outcomes.
Data weaves learning into the interview process
A data-driven approach to interviewing offers a real-time handshake between the interview and performance. On-the-job performance data of the new hires can be mapped to their interview performance to arrive at a meaningful measure of the quality of hire. The insights can be woven as learning and better practices to further refine and customise the interview process for specific jobs and responsibilities.
Such an approach also allows for replacing standard interview questions with those tailored for the actual performance that is expected in a particular role. Can a simulated case study be used instead of the theoretical ones found on the Internet? Out of the box thinking becomes less abstract and easier to appreciate and measure when linked to the company’s situation.
Such learning is not restricted to evaluating the candidate alone. Data-based insights can also reveal the propensity of interviewer bias (conscious and unconscious) that supports or impairs the selection of the right fit candidate. Building such learning into interviewer training will upgrade their skills for better results.
Data and intelligent automation of the interview process
Online tests and personality assessments, use of facial recognition software, artificial intelligence and machine learning - these enable the assessment to go beyond the correctness of the answers and first impressions of responses to more detailed assessments of words and sentences, and nuances of facial and body language. Though nascent, a few organisations are already deploying such advanced technologies to evaluate transcribed interviews for better pointers to high performance.
Data markets the interview process better
Candidate engagement and experience are two critical components of the interview process. It is a war for talent that makes organisations seek differentiated branding to enhance their reputation. The one-to-one interview time affords a great opportunity to market the organization as well.
Data has shown that peer interviews by potential colleagues and co-workers can brand the organisation better. A data-driven strategy will also allow better training of such peer interviewers for this specific purpose, building the right elements of candidate engagement and experience. Additionally, it can also guide organisations on the type of information and interactions they can provide to candidates – based on their prospective roles, seniority and other factors of diversity.
Interviewing practices need an overhaul if they are to serve the exacting needs of a business environment that has witnessed disruptive upheavals. Data, analytics and other technology platforms have a wealth of powerful enablers that can be effectively leveraged to bring in the required elements of change. Predictability can breed ineffectiveness, and that is the last thing we need to impact the number one priority of most organisations – talent acquisition.