Organizations make hundreds of talent decisions every year. The question is whether they are all based on the same criteria.
In many companies, there is an idea that is frequently repeated: people advance because they demonstrate talent. And in a way, this statement makes sense. Organizations design evaluation models, define competencies, set goals, and create processes to make development decisions as objective as possible.
But within this broad universe, there is a question we rarely ask ourselves: Are we sure this system works equally for everyone?
This is not a matter of judging or pointing fingers at decision-makers. The majority of Human Resources professionals try, as much as possible, to act with fair criteria. The issue perhaps lies in understanding that the context or environment in which these decisions are made is a system built by people, and any human system can be conditioned by personal experiences or different ways of interpreting talent. As the saying goes, “what gets measured gets managed.” In People Analytics, there is an even more important idea: what we do not analyze can remain invisible for years.
Objectivity lies not only in the model, but in how it works
An organization can have a perfectly defined evaluation system, with competencies tied to every role and consistent rating scales. But beware: just because a process is structured does not necessarily mean its results are equal for every person in the organization.
For this reason, when analyzing talent, looking only at the final score is not enough. It is also necessary to understand what happens behind that result.
To put it simply, here is an example: a company might find that men and women obtain a similar average performance rating. However, when analyzing other indicators, differences appear in:
What reflection can we draw after evaluating these indicators? It's simple. For any organization, measuring performance is usually relatively straightforward because it stems from fully observable and measurable elements such as met objectives, achieved results, or demonstrated competencies. However… when we talk about potential, the picture changes entirely, as it requires looking into a future that is inherently unknown.
This is where a real challenge arises, because potential is not data we can directly observe—it is a complete interpretation based on certain signals.
For instance, a person might be considered ready to lead because they have had more exposure, because they communicate their achievements better, or because they fit the traditional idea of leadership; all of these signals can drive our decision-making.
That is why it is so important to analyze how potential is distributed across different variables, such as gender, to ask relevant questions:
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Are we using the same criteria to identify future leaders?
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Does everyone have equal opportunities to demonstrate their capability?
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Are we confusing visibility with potential?
According to various research studies on leadership and diversity, such as those compiled by McKinsey & Company in their studies on women in the workplace, representation gaps do not always appear at entry-level positions, but rather at key moments when organizations decide who moves forward.
Analyzing gender is not about pointing fingers; it is about understanding
It is important to clarify that incorporating gender into talent analysis does not mean assuming a problem exists. Quite the contrary! The true goal is to add context.
In the same way an organization analyzes data by department, tenure, or professional level, it can examine whether different patterns exist by gender to better understand how its processes work. The goal is not to replace manager intuition, but to complement it with data that enables better decision-making.
Intuition can be a useful tool in certain scenarios, but when dealing with talent management, promotions, leadership, or succession, relying solely on it can cause us to repeat patterns without realizing it.