Use Data to Drive Hiring Decisions

Data-driven decision-making may sound like business jargon, but in hiring, it simply means relying on unbiased evidence to achieve better hiring outcomes. Conversely, hiring decisions based on untrustworthy information such as gut feelings, hearsay, or personal judgments unrelated to job-specific competencies are well-documented to be less reliable in predicting job performance and retention.

Thankfully, structured interviewing produces data that lends itself to sound decision-making.

Structured interviewing produces the following information outputs:

  • Hiring recommendations (yes, no, strong yes, strong no)
  • Hiring recommendation notes
  • Average and individual per-question ratings ("Poor," "Excellent," etc.)
  • Per-question notes
  • Skills test, case study, or portfolio review scores

Inclusive Decision-Making in Hiring

It is easy to assume that inclusive hiring decisions are those where all interviewers have an equal vote on the final hiring decision, but that is an overly simplistic view of inclusion. An inclusive decision is one that serves all, and inclusive decision-making generally depends on a framework or process that may incorporate voting but has many other inclusive features. In the case of inclusive hiring, once again, it is structured interviewing—done well—that is the framework for inclusive decision-making.

Structured interviewing can support inclusive decision-making in the following ways:

  1. A diverse-as-possible set of subject matter experts develops and reviews the questions and any skills tests utilized in interviews.
  2. A diverse-as-possible group of relevant interviewers should comprise the interviewing panel.
  3. Interviewers get trained in the interviewing process and how to avoid unconscious bias.
  4. The collected feedback from interviewers gets considered equally in hiring decisions.
  5. All interview panels get a forum to express their perspectives on finalists and the hiring process.
  6. Hiring decisions are intentionally data-driven to make better decisions and avoid all types of bias.

Note: the term "diverse-as-possible" recognizes that while there may be many dimensions of diversity in an organization, there are often gaps. For example, the potential diversity of an interview panel of appropriate subject matter experts within the company is a practical limiting factor.

A Transparent Decision-Making Process

How decisions get made in an organization can be a source of tension, especially regarding high-stakes matters such as hiring decisions. Making matters worse, conflict avoidance among leaders can result in a lack of transparency. Nonetheless, it is crucial to clarify how the decision gets made to interviewers and other stakeholders to get ahead of concerns and to make space for process improvements.

It is outside the scope of this guide to dictate how your company makes decisions. However, there are examples of best practices that you may adopt or remix to suit your organization:

  • State who the decision-maker is, if there is a single person, or if the decision will be democratic. Often a hiring manager is the ultimate decision maker as they will be the person who will make the level determination, compensation determination, assess future performance, etc., so it is typical that they would also have the final say in hiring.
  • State how the decision-making process works. As stated above, structured interviewing provides the data for inclusive, data-driven decision-making, but it is essential to specify how the data would get interpreted and utilized. Depending on how scorecards get presented, sometimes there is an assumption that the highest-scored finalist receives an offer, but sometimes a more balanced candidate is a more suitable decision than the highest-scoring; stating this upfront avoids confusion later.
  • State how interviewers will participate in the hiring decision. For example, it is common to have a roundtable discussion about finalists where interviewers can share their perspectives.
  • State any policies about the minimum number of finalists. For example, some job positions may achieve a large candidate pool in an industry where hiring decisions are relatively slow. In this case, there may be a policy that a specific minimum number of candidates must get interviewed before a decision occurs. On the other hand, for roles where talent is scarce and decisions happen fast, it is common to make an offer to the first viable candidate or to complete only scheduled final-round interviews when a viable candidate gets assessed.
  • State how organization diversity goals impact the hiring process, if applicable. Interviewers and other stakeholders are typically not experts in the hiring process and may be surprised that diversity objectives don't factor into hiring decisions directly. Organizations advance their diversity goals by increasing the size and diversity of their applicant pool through targeted marketing and recruiting strategies, not by intentionally making biased hiring decisions.

Effectively Communicate Data to Drive Decisions

An unambiguous presentation of evidence best supports data-driven decision-making, often through visualization. The scorecard is the display format that suits hiring data best. A collection of candidate scorecards dramatically simplifies the evaluation of candidates across several standardized assessment criteria, such as recommendations, scores, and level assessments.