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bogdan » optimizing team chemistry over competence

12:21 pm on Dec 30, 2024 | read the article | tags:

this is #free-ideas. a space where abandoned inspirations find a second life. these are concepts i’ve toyed with but never pursued – whether out of laziness, lack of time, or simply because the spark didn’t ignite into a fire. instead of letting them gather dust, i’m sharing them here in case they resonate with someone else. take them, twist them, improve them, or prove they don’t work. after all, ideas only become valuable when acted upon.

the idea

teams are often built around talent and competence, but what if the key to success isn’t just skill? from my observations, the real driver of productivity is the chemistry between team members. a team of highly skilled individuals can still fail if they don’t work well together. my suggestion is to integrate data-driven methods into the hiring process to ensure that team dynamics are optimized from the start. by using psychological surveys and machine learning, we can improve team fit, making collaboration more effective and productive.

why it matters

in many cases, a team with great individual performers can underachieve simply because they don’t mesh well together. conflicts, miscommunications, and incompatible working styles can hinder even the most talented people. on the other hand, a well-balanced team, even if not filled with superstars, can outperform expectations. this idea targets the core of team dynamics and aims to make the hiring process more holistic by considering personality compatibility as a crucial factor.

how it works

to apply this concept, the hiring process would go beyond just assessing technical skills. i propose incorporating a psychological survey, based on big 5 personality traits, to evaluate how a potential team member’s personality fits with the existing team (source: [1]). you can gather survey responses as part of regular performance reviews to track changes over time. by feeding these responses together with the performance results into a machine learning model (start with lightgbm), you’ll be able to identify the «ideal» personality profile for your team’s success.

when a new team member is brought on board, their psychological survey results are run through the model to predict the best fit for the team. over time, you’ll have a clearer picture of the team dynamics and can refine hiring decisions to complement the existing personalities.

why it could work

the strength of this idea lies in its data-driven approach. by measuring personalities and team dynamics over time with correlations to performance, you are actively learning which profiles work best for which teams. this not only helps when hiring new team members but also informs how current teams might be adjusted to improve overall collaboration. the data from these surveys could also serve as a predictive tool for improving performance, fostering a more cohesive and engaged team culture. (a few sources [1], [2], [3], [4])

the challenge

one obvious drawback is ensuring anonymity and GDPR compliance. psychological surveys, even if anonymized, could raise privacy concerns. as such, it’s essential to develop a system that protects personal data while still providing useful insights for team optimization. the random sampling method (selecting around 20 items from a pool of 50) could help reduce survey fatigue, keeping employees engaged without overwhelming them. additionally, a unique idea to circumvent the overt psychological testing could be introducing abstract coloring exercises. these could be used to correlate with personality survey results while maintaining a more indirect approach, thereby mitigating privacy concerns.

optimizing team chemistry over competence
(image credits chatgpt)

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