Home / SmartTech / Up-and-coming companies live on data. Shouldn’t they be using it to improve hiring decisions? – TechCrunch

Up-and-coming companies live on data. Shouldn’t they be using it to improve hiring decisions? – TechCrunch

While emerging businesses are often started by tech-minded founders and funded by VCs for their data-driven approaches to product and growth, the irony is that these companies often use less data and accuracy in recruiting talent than traditional, less data-centric companies . The truth is that the way tech companies hire has remained relatively untouched by disruption, with most still relying on résumés and interview interviews to make decisions at the highest level.

The consequences of this have a detrimental effect not only on team building, but also on the general diversity of the startup space.

Data-driven settings are not just about having the right funnel metrics to determine the efficiency of the process, they also extend to the information we collect (or not collect) and measure to determine if someone is for a Role is suitable. It is a science of building teams, and therefore selecting talent to join teams. Why is early-stage hiring still not viewed as a data-driven activity?

Some argue that talent selection inherently involves people and therefore cannot be truly scientific. People are unique, complex, emotional, and unpredictable. Furthermore, few people think they are poor judges of character and talent. Most too confidently believe that they have superior instincts and a “nose”

; for talent. Recruiting talent is one of the few operational activities in companies that does not require formal training or decades of experience in order to be above average.

Move away from bowel reviews

The effects of this outdated way of thinking can be felt across the board – especially when it comes to team dynamics. To begin with, to determine if someone is qualified, you need to know what to look for. Organizations with little knowledge of what makes a role successful don’t have the information to build a strong selection system. The result is a poor hiring process that focuses heavily on unstructured surveys, targets predictive signals, and relies on gut-based assessments.

Chemistry, self-confidence and charisma are more likely to determine whether a candidate plays a role compared to competence in getting the job done. As a result, it is estimated that nearly half of all new hires fail and are ineffective, and weak teams are formed. The lack of reliable data also means that most organizations suffer from a broken feedback loop between attitude and team performance that hinders learning and improvement. If you don’t connect the dots, how do you know if your selection process is efficient at evaluating the skills, traits, and behaviors that lead to excellence?

The dangers of subjective approaches

What is more dangerous is that a hiring process that is not designed to collect and evaluate evidence almost always leads to a lack of team diversity, which is known to slow down innovation and therefore reduce business success.

Subjective approaches to the selection and development of talent create a revolving door of unconscious prejudice and exclusion with a profound influence on what makes up today’s homogeneous tech ecosystem. This is not supported by the natural oversupply of networks as a means of filling hiring pipelines in the early stages of business development.

For talent managers and practitioners, the credibility of their profession is not an advantage. Recruiting and selecting talent continues to be referred to as an undemanding, minor back-office role, or a “dark art” that is about as data-informed as looking at a crystal ball.

Evidence-based approach

To make the hiring process more objective, founders and their teams are best served when they start with a clear, evidence-based definition of the success of markers in a role and then structure each selection phase to evaluate them for a specific skill or behavioral trait: what and when will you rate? What criteria do you use to evaluate the data? In other words, the goal is to get as close as possible to signals that are reliable enough to accurately predict that someone will appear in a role.

Until recently, science-based talent assessment tools that enable HR managers to make more objective assessments were largely used by larger, more established companies suffering from a high volume of applications – the luxury problem “Google”. However, three recent shifts suggest that early-stage adoption by startups will see a trend as their teams scale:

  1. Pressure to build diverse and inclusive teams. 2020 has put diversity and inclusion high on the agenda for most businesses. With the help of assessment tools used in team building, groups can better identify where certain cognitive, personality and skill gaps exist and therefore focus on adjusting the missing ingredients. Assessing candidates also helps reduce unconscious biases that might creep in during interviews by showing more objective information about a person’s strengths and weaknesses.

  2. The sharp increase in applicants. The COVID-19 pandemic had two significant effects on recruitment. First, companies have been forced to hire talent in remote roles, which has increased the global talent pool for most jobs in a technology company. Second, the increase in talent available has resulted in the average number of applications increasing dramatically. This shift from a candidate-driven to an employer-driven market means that selecting signals from noise is becoming increasingly challenging, even for early startups with a less established talent brand.

  3. Better designed, cheaper products on the market. Talent assessment software has long been largely inaccessible to non-corporate customers. Academic interfaces and disgusting experiences with candidates have resulted in many scientifically robust tools simply not being able to grab the attention of tech-obsessed and product-obsessed buyers. In addition, many tools that require additional advice or training to manage and interpret are simply outside the scope of early-stage budgets. With new entrants to the valuation marketplace with a focus on automation, product design, and compliance, scale-ups can justify spending in this area, and perceptions will change as they become essential SaaS products in their team’s operational toolkits .

As these external factors continue to drive hiring towards an evidence-based approach, organizations need to prioritize these changes to their hiring practices. While unstructured interviews feel most natural, they are dangerous to accurate talent selection, and while the conversation may be nice, they create noises that do nothing to help you make smart and accurate decisions that really matter.

Instinctive feelings and “gut instincts” in hiring should be treated with caution, and decisions should always be based on role-based evidence that you pinpoint. Emerging companies looking to build a strong team foundation shouldn’t risk the redundancies and prejudices that result from subjective hiring decisions.

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